human/dist/human.esm-nobundle.js

var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all2)=>{__markAsModule(target);for(var name in all2)__defProp(target,name,{get:all2[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(__create(__getProtoOf(module)),"default",{value:module,enumerable:!0}),module),require_blazeface=__commonJS(exports=>{const NUM_LANDMARKS=6;function generateAnchors(inputSize){const spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i<spec.strides.length;i++){const stride=spec.strides[i],gridRows=Math.floor((inputSize+stride-1)/stride),gridCols=Math.floor((inputSize+stride-1)/stride),anchorsNum=spec.anchors[i];for(let gridY=0;gridY<gridRows;gridY++){const anchorY=stride*(gridY+.5);for(let gridX=0;gridX<gridCols;gridX++){const anchorX=stride*(gridX+.5);for(let n=0;n<anchorsNum;n++)anchors.push([anchorX,anchorY])}}}return anchors}const disposeBox=box=>{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{const starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){const boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize),concatAxis=1;return concat2d([startNormalized,endNormalized],concatAxis)}function scaleBoxFromPrediction(face2,scaleFactor){return tidy(()=>{const box=face2.box?face2.box:face2;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}class BlazeFaceModel{constructor(model2,config2){this.blazeFaceModel=model2,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;const[detectedOutputs,boxes,scores]=tidy(()=>{const resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage);let prediction;if(Array.isArray(batchedPrediction)){const sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2),concat2=concat([concat512,concat384],1);prediction=concat2.squeeze(0)}else prediction=batchedPrediction.squeeze();const decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();const boundingBoxesMap=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])),boundingBoxes=boundingBoxesMap.map(boundingBox=>{const vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(const i in boundingBoxes){const boxIndex=boxIndices[i],confidence=scoresVal[boxIndex];if(confidence>this.config.face.detector.minConfidence){const box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){const{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(const face2 of boxes){const landmarkData=face2.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face2,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face2.probability.arraySync(),anchor=face2.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face2.box),face2.landmarks.dispose(),face2.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}}async function load2(config2){const blazeface=await loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config2);return console.log(`Human: load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load2;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox}),require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){const startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){const h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){const center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){const centers=getBoxCenter2(box),size=getBoxSize2(box),maxEdge=Math.max(...size),halfSize=maxEdge/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2}),require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){const radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot3(v1,v2){let product=0;for(let i=0;i<v1.length;i++)product+=v1[i]*v2[i];return product}exports.dot=dot3;function getColumnFrom2DArr2(arr,columnIndex){const column=[];for(let i=0;i<arr.length;i++)column.push(arr[i][columnIndex]);return column}exports.getColumnFrom2DArr=getColumnFrom2DArr2;function multiplyTransformMatrices2(mat1,mat2){const product=[],size=mat1.length;for(let row=0;row<size;row++){product.push([]);for(let col=0;col<size;col++)product[row].push(dot3(mat1[row],getColumnFrom2DArr2(mat2,col)))}return product}function buildRotationMatrix2(rotation,center){const cosA=Math.cos(rotation),sinA=Math.sin(rotation),rotationMatrix=[[cosA,-sinA,0],[sinA,cosA,0],[0,0,1]],translationMatrix=buildTranslationMatrix2(center[0],center[1]),translationTimesRotation=multiplyTransformMatrices2(translationMatrix,rotationMatrix),negativeTranslationMatrix=buildTranslationMatrix2(-center[0],-center[1]);return multiplyTransformMatrices2(translationTimesRotation,negativeTranslationMatrix)}exports.buildRotationMatrix=buildRotationMatrix2;function invertTransformMatrix2(matrix){const rotationComponent=[[matrix[0][0],matrix[1][0]],[matrix[0][1],matrix[1][1]]],translationComponent=[matrix[0][2],matrix[1][2]],invertedTranslation=[-dot3(rotationComponent[0],translationComponent),-dot3(rotationComponent[1],translationComponent)];return[rotationComponent[0].concat(invertedTranslation[0]),rotationComponent[1].concat(invertedTranslation[1]),[0,0,1]]}exports.invertTransformMatrix=invertTransformMatrix2;function rotatePoint2(homogeneousCoordinate,rotationMatrix){return[dot3(homogeneousCoordinate,rotationMatrix[0]),dot3(homogeneousCoordinate,rotationMatrix[1])]}exports.rotatePoint=rotatePoint2;function xyDistanceBetweenPoints(a,b){return Math.sqrt((a[0]-b[0])**2+(a[1]-b[1])**2)}exports.xyDistanceBetweenPoints=xyDistanceBetweenPoints}),require_coords=__commonJS(exports=>{const 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28,17,22,29,25,28,30,26,27,31,29],TRI7=[0,4,1,2,4,3,4,5,6],VTX68=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports=>{const bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i<coords2.MESH_TO_IRIS_INDICES_MAP.length;i++){const{key,indices}=coords2.MESH_TO_IRIS_INDICES_MAP[i],originalIndices=coords2.MESH_ANNOTATIONS[`${prefix}${key}`],shouldReplaceAllKeys=keys==null;if(shouldReplaceAllKeys||keys.includes(key))for(let j=0;j<indices.length;j++){const index=indices[j];rawCoords[originalIndices[j]]=[newCoords[index][0],newCoords[index][1],(newCoords[index][2]+rawCoords[originalIndices[j]][2])/2]}}}class Pipeline{constructor(boundingBoxDetector,meshDetector,irisModel,config2){this.storedBoxes=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=boundingBoxDetector,this.meshDetector=meshDetector,this.irisModel=irisModel,this.meshWidth=config2.face.mesh.inputSize,this.meshHeight=config2.face.mesh.inputSize,this.irisSize=config2.face.iris.inputSize,this.irisEnlarge=2.3,this.skipped=1e3,this.detectedFaces=0}transformRawCoords(rawCoords,box,angle,rotationMatrix){const boxSize=bounding.getBoxSize({startPoint:box.startPoint,endPoint:box.endPoint}),scaleFactor=[boxSize[0]/this.meshWidth,boxSize[1]/this.meshHeight],coordsScaled=rawCoords.map(coord=>[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=util.buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=util.invertTransformMatrix(rotationMatrix),boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1],originalBoxCenter=[util.dot(boxCenter,inverseRotationMatrix[0]),util.dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){const leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face2,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){const box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box);let crop=image.cropAndResize(face2,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){const eyeRawCoords=[];for(let i=0;i<IRIS_NUM_COORDINATES;i++){const x=eyeData[i*3],y=eyeData[i*3+1],z=eyeData[i*3+2];eyeRawCoords.push([(flip?1-x/this.irisSize:x/this.irisSize)*eyeBoxSize[0]+eyeBox.startPoint[0],y/this.irisSize*eyeBoxSize[1]+eyeBox.startPoint[1],z])}return{rawCoords:eyeRawCoords,iris:eyeRawCoords.slice(IRIS_IRIS_INDEX)}}getAdjustedIrisCoords(rawCoords,irisCoords,direction){const upperCenterZ=rawCoords[coords2.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2],lowerCenterZ=rawCoords[coords2.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2],averageZ=(upperCenterZ+lowerCenterZ)/2;return irisCoords.map((coord,i)=>{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config2){this.skipped++;let useFreshBox=!1,detector;if((this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),input2.shape[1]!==255&&input2.shape[2]!==255&&(this.skipped=0)),detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(const possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(const i in this.storedBoxes){const scaledBox=bounding.scaleBoxCoordinates({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},detector.scaleFactor),enlargedBox=bounding.enlargeBox(scaledBox),landmarks=this.storedBoxes[i].landmarks.arraySync(),confidence=this.storedBoxes[i].confidence;this.storedBoxes[i]={...enlargedBox,confidence,landmarks}}this.runsWithoutFaceDetector=0}detector&&detector.boxes&&detector.boxes.forEach(prediction=>{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let angle=0;const boxLandmarksFromMeshModel=box.landmarks.length>=LANDMARKS_COUNT;let[indexOfMouth,indexOfForehead]=MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;boxLandmarksFromMeshModel===!1&&([indexOfMouth,indexOfForehead]=BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES),angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);const faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]];let rotatedImage=input2,rotationMatrix=util.IDENTITY_MATRIX;angle!==0&&(rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized),rotationMatrix=util.buildRotationMatrix(-angle,faceCenter));const face2=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255),outputFace=config2.face.detector.rotation?image.rotateWithOffset(face2,angle):face2;if(!config2.face.mesh.enabled){const prediction2={coords:null,box,faceConfidence:null,confidence:box.confidence,image:outputFace};return prediction2}const[,confidence,contourCoords]=this.meshDetector.predict(face2),confidenceVal=confidence.dataSync()[0];if(confidence.dispose(),confidenceVal<config2.face.detector.minConfidence)return contourCoords.dispose(),null;const coordsReshaped=reshape(contourCoords,[-1,3]);let rawCoords=coordsReshaped.arraySync();if(config2.face.iris.enabled){const{box:leftEyeBox,boxSize:leftEyeBoxSize,crop:leftEyeCrop}=this.getEyeBox(rawCoords,face2,LEFT_EYE_BOUNDS[0],LEFT_EYE_BOUNDS[1],!0),{box:rightEyeBox,boxSize:rightEyeBoxSize,crop:rightEyeCrop}=this.getEyeBox(rawCoords,face2,RIGHT_EYE_BOUNDS[0],RIGHT_EYE_BOUNDS[1]),eyePredictions=this.irisModel.predict(concat([leftEyeCrop,rightEyeCrop])),eyePredictionsData=eyePredictions.dataSync();eyePredictions.dispose();const leftEyeData=eyePredictionsData.slice(0,IRIS_NUM_COORDINATES*3),{rawCoords:leftEyeRawCoords,iris:leftIrisRawCoords}=this.getEyeCoords(leftEyeData,leftEyeBox,leftEyeBoxSize,!0),rightEyeData=eyePredictionsData.slice(IRIS_NUM_COORDINATES*3),{rawCoords:rightEyeRawCoords,iris:rightIrisRawCoords}=this.getEyeCoords(rightEyeData,rightEyeBox,rightEyeBoxSize),leftToRightEyeDepthDifference=this.getLeftToRightEyeDepthDifference(rawCoords);Math.abs(leftToRightEyeDepthDifference)<30?(replaceRawCoordinates(rawCoords,leftEyeRawCoords,"left"),replaceRawCoordinates(rawCoords,rightEyeRawCoords,"right")):leftToRightEyeDepthDifference<1?replaceRawCoordinates(rawCoords,leftEyeRawCoords,"left",["EyeUpper0","EyeLower0"]):replaceRawCoordinates(rawCoords,rightEyeRawCoords,"right",["EyeUpper0","EyeLower0"]);const adjustedLeftIrisCoords=this.getAdjustedIrisCoords(rawCoords,leftIrisRawCoords,"left"),adjustedRightIrisCoords=this.getAdjustedIrisCoords(rawCoords,rightIrisRawCoords,"right");rawCoords=rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords)}const transformedCoordsData=this.transformRawCoords(rawCoords,box,angle,rotationMatrix);dispose(rawCoords);const landmarksBox=bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData)),transformedCoords=tensor2d(transformedCoordsData),prediction={coords:transformedCoords,box:landmarksBox,faceConfidence:confidenceVal,confidence:box.confidence,image:outputFace};return this.storedBoxes[i]={...landmarksBox,landmarks:transformedCoords.arraySync(),confidence:box.confidence,faceConfidence:confidenceVal},prediction}));return results=results.filter(a=>a!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){const xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}}exports.Pipeline=Pipeline}),require_facemesh=__commonJS(exports=>{const blazeface=__toModule(require_blazeface()),pipe=__toModule(require_facepipeline()),coords2=__toModule(require_coords());class MediaPipeFaceMesh{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.pipeline=new pipe.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input2,config2){const predictions=await this.pipeline.predict(input2,config2),results=[];for(const prediction of predictions||[]){if(prediction.isDisposedInternal)continue;const mesh=prediction.coords?prediction.coords.arraySync():null,annotations={};if(mesh&&mesh.length>0)for(const key in coords2.MESH_ANNOTATIONS)(config2.face.iris.enabled||key.includes("Iris")===!1)&&(annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]));results.push({confidence:prediction.confidence||0,box:prediction.box?[prediction.box.startPoint[0],prediction.box.startPoint[1],prediction.box.endPoint[0]-prediction.box.startPoint[0],prediction.box.endPoint[1]-prediction.box.startPoint[1]]:0,mesh,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}}async function load2(config2){const models=await Promise.all([blazeface.load(config2),loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}),loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")})]),faceMesh=new MediaPipeFaceMesh(models[0],models[1],models[2],config2);return console.log(`Human: load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),console.log(`Human: load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load2;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords2.TRI468}),require_profile=__commonJS(exports=>{const profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;const maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);const res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,console.log("Human profiler",name,res)}exports.run=profile3}),require_age=__commonJS(exports=>{const profile3=__toModule(require_profile()),models={};let last={age:0},frame2=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await loadGraphModel(config2.face.age.modelPath),console.log(`Human: load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image3,config2){return models.age?frame2<config2.face.age.skipFrames&&config2.videoOptimized&&last.age&&last.age>0?(frame2+=1,last):(frame2=0,new Promise(async resolve=>{const resize=image.resizeBilinear(image3,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT;const obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{const profileAge=config2.face.age.enabled?await profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){const data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_gender=__commonJS(exports=>{const profile3=__toModule(require_profile()),models={};let last={gender:""},frame2=Number.MAX_SAFE_INTEGER,alternative=!1;const rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,console.log(`Human: load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image3,config2){return models.gender?frame2<config2.face.gender.skipFrames&&config2.videoOptimized&&last.gender!==""?(frame2+=1,last):(frame2=0,new Promise(async resolve=>{const resize=image.resizeBilinear(image3,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1);let enhance;alternative?enhance=tidy(()=>{const[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]),grayscale=addN([redNorm,greenNorm,blueNorm]);return grayscale.sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT;const obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{const profileGender=config2.face.gender.enabled?await profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){const data2=genderT.dataSync();if(alternative){const confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{const confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_emotion=__commonJS(exports=>{const profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surpise","neutral"],models={};let last=[],frame2=Number.MAX_SAFE_INTEGER;const rgb=[.2989,.587,.114],scale2=1;async function load2(config2){return models.emotion||(models.emotion=await loadGraphModel(config2.face.emotion.modelPath),console.log(`Human: load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image3,config2){return models.emotion?frame2<config2.face.emotion.skipFrames&&config2.videoOptimized&&last.length>0?(frame2+=1,last):(frame2=0,new Promise(async resolve=>{const resize=image.resizeBilinear(image3,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();const redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();const grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();const normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();const obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){const profileData=await profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{const emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;i<data2.length;i++)scale2*data2[i]>config2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2}),require_embedding=__commonJS(exports=>{const profile3=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await loadGraphModel(config2.face.embedding.modelPath),console.log(`Human: load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;const distance=10*Math.sqrt(embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**2,0)),confidence=2*(.5-distance);return Math.trunc(1e3*confidence)/1e3}async function predict2(image3,config2){return models.embedding?new Promise(async resolve=>{const resize=image.resizeBilinear(image3,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1);let data2=[];if(config2.face.embedding.enabled)if(config2.profile){const profileData=await profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{const embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict2;exports.simmilarity=simmilarity2;exports.load=load2}),require_modelBase=__commonJS(exports=>{class BaseModel{constructor(model2,outputStride){this.model=model2,this.outputStride=outputStride}predict(input2){return tidy(()=>{const asFloat=this.preprocessInput(input2.toFloat()),asBatch=asFloat.expandDims(0),results=this.model.predict(asBatch),results3d=results.map(y=>y.squeeze([0])),namedResults=this.nameOutputResults(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}}exports.BaseModel=BaseModel}),require_modelMobileNet=__commonJS(exports=>{const modelBase=__toModule(require_modelBase());class MobileNet extends modelBase.BaseModel{preprocessInput(input2){return tidy(()=>div(input2,127.5).sub(1))}nameOutputResults(results){const[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}}exports.MobileNet=MobileNet}),require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}class MaxHeap{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){const max2=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max2}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j<this.numberOfElements&&this.less(j,j+1)&&j++,!this.less(k,j))break;this.exchange(k,j),k=j}}getValueAt(i){return this.getElementValue(this.priorityQueue[i])}less(i,j){return this.getValueAt(i)<this.getValueAt(j)}exchange(i,j){const t=this.priorityQueue[i];this.priorityQueue[i]=this.priorityQueue[j],this.priorityQueue[j]=t}}exports.MaxHeap=MaxHeap}),require_buildParts=__commonJS(exports=>{const heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){const[height,width]=scores.shape;let localMaximum=!0;const yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrent<yEnd;++yCurrent){const xStart=Math.max(heatmapX-localMaximumRadius,0),xEnd=Math.min(heatmapX+localMaximumRadius+1,width);for(let xCurrent=xStart;xCurrent<xEnd;++xCurrent)if(scores.get(yCurrent,xCurrent,keypointId)>score){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){const[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY<height;++heatmapY)for(let heatmapX=0;heatmapX<width;++heatmapX)for(let keypointId=0;keypointId<numKeypoints;++keypointId){const score=scores.get(heatmapY,heatmapX,keypointId);if(score<scoreThreshold)continue;scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores)&&queue.enqueue({score,part:{heatmapY,heatmapX,id:keypointId}})}return queue}exports.buildPartWithScoreQueue=buildPartWithScoreQueue}),require_keypoints=__commonJS(exports=>{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});const connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports=>{const kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){const{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){const result=new Array(size);for(let i=0;i<size;i++)result[i]=element;return result}exports.fillArray=fillArray;function clamp2(a,min2,max2){return a<min2?min2:a>max2?max2:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){const dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min2,max2){return{y:clamp2(a.y,min2,max2),x:clamp2(a.x,min2,max2)}}exports.clampVector=clampVector}),require_decodePose=__commonJS(exports=>{const keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){const numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){const[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),displacedPoint=vectors.addVectors(sourceKeypoint.position,displacement);let targetKeypoint=displacedPoint;for(let i=0;i<offsetRefineStep;i++){const targetKeypointIndices=getStridedIndexNearPoint(targetKeypoint,outputStride,height,width),offsetPoint=vectors.getOffsetPoint(targetKeypointIndices.y,targetKeypointIndices.x,targetKeypointId,offsets);targetKeypoint=vectors.addVectors({x:targetKeypointIndices.x*outputStride,y:targetKeypointIndices.y*outputStride},{x:offsetPoint.x,y:offsetPoint.y})}const targetKeyPointIndices=getStridedIndexNearPoint(targetKeypoint,outputStride,height,width),score=scoresBuffer.get(targetKeyPointIndices.y,targetKeyPointIndices.x,targetKeypointId);return{position:targetKeypoint,part:keypoints.partNames[targetKeypointId],score}}function decodePose(root,scores,offsets,outputStride,displacementsFwd,displacementsBwd){const numParts=scores.shape[2],numEdges=parentToChildEdges.length,instanceKeypoints=new Array(numParts),{part:rootPart,score:rootScore}=root,rootPoint=vectors.getImageCoords(rootPart,outputStride,offsets);instanceKeypoints[rootPart.id]={score:rootScore,part:keypoints.partNames[rootPart.id],position:rootPoint};for(let edge=numEdges-1;edge>=0;--edge){const sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge<numEdges;++edge){const sourceKeypointId=childToParentEdges[edge],targetKeypointId=parentToChildEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsFwd))}return instanceKeypoints}exports.decodePose=decodePose}),require_decodeMultiple=__commonJS(exports=>{const buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors());function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{const correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){const notOverlappedKeypointScores=instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0);return notOverlappedKeypointScores/instanceKeypoints.length}const kLocalMaximumRadius=1;function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,outputStride,maxPoseDetections,scoreThreshold=.5,nmsRadius=20){const poses=[],queue=buildParts.buildPartWithScoreQueue(scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=nmsRadius*nmsRadius;for(;poses.length<maxPoseDetections&&!queue.empty();){const root=queue.dequeue(),rootImageCoords=vectors.getImageCoords(root.part,outputStride,offsetsBuffer);if(withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,rootImageCoords,root.part.id))continue;const keypoints=decodePose.decodePose(root,scoresBuffer,offsetsBuffer,outputStride,displacementsFwdBuffer,displacementsBwdBuffer),score=getInstanceScore(poses,squaredNmsRadius,keypoints);poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports=>{const kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a<minConfidence||b<minConfidence}function getAdjacentKeyPoints(keypoints,minConfidence){return kpt.connectedPartIndices.reduce((result,[leftJoint,rightJoint])=>(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;const{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){const{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){const input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){const scaledPoses=poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth));return scaledPoses}exports.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports=>{const modelMobileNet=__toModule(require_modelMobileNet()),decodeMultiple=__toModule(require_decodeMultiple()),util=__toModule(require_util2());class PoseNet{constructor(net){this.baseModel=net,this.outputStride=16}async estimatePoses(input2,config2){return new Promise(async resolve=>{const height=input2.shape[1],width=input2.shape[2],resized=util.resizeTo(input2,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized),allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,this.outputStride,config2.body.maxDetections,config2.body.scoreThreshold,config2.body.nmsRadius),resultPoses=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),resolve(resultPoses)})}dispose(){this.baseModel.dispose()}}exports.PoseNet=PoseNet;async function load2(config2){const graphModel=await loadGraphModel(config2.body.modelPath),mobilenet=new modelMobileNet.MobileNet(graphModel,this.outputStride);return console.log(`Human: load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load2}),require_posenet=__commonJS(exports=>{const modelMobileNet=__toModule(require_modelMobileNet()),modelPoseNet=__toModule(require_modelPoseNet()),decodeMultiple=__toModule(require_decodeMultiple()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.MobileNet=modelMobileNet.MobileNet;exports.decodeMultiplePoses=decodeMultiple.decodeMultiplePoses;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose}),require_handdetector=__commonJS(exports=>{class HandDetector{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{const boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{const landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config2){const batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();const scores=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scoresVal=scores.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();const filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scores.dispose(),filteredT.dispose();const hands=[];for(const boxIndex of filtered)if(scoresVal[boxIndex]>=config2.hand.minConfidence){const matchingBox=slice(boxes,[boxIndex,0],[1,-1]),rawPalmLandmarks=slice(predictions,[boxIndex,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,boxIndex).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scoresVal[boxIndex]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config2){const inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config2);if(image3.dispose(),!predictions||predictions.length===0)return null;const hands=[];for(const prediction of predictions){const boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}}exports.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports=>{const PALM_BOX_SHIFT_VECTOR=[0,-.4],PALM_BOX_ENLARGE_FACTOR=3,HAND_BOX_SHIFT_VECTOR=[0,-.1],HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2;class HandPipeline{constructor(boundingBoxDetector,meshDetector,inputSize){this.boxDetector=boundingBoxDetector,this.meshDetector=meshDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=1e3,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){const rotatedPalmLandmarks=palmLandmarks.map(coord=>{const homogeneousCoordinate=[...coord,1];return rotatePoint(homogeneousCoordinate,rotationMatrix)}),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(shiftBox(boxAroundPalm,PALM_BOX_SHIFT_VECTOR)),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){const boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(shiftBox(boundingBox,HAND_BOX_SHIFT_VECTOR)),HAND_BOX_ENLARGE_FACTOR),palmLandmarks=[];for(let i=0;i<PALM_LANDMARK_IDS.length;i++)palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0,2));return boxAroundHand.palmLandmarks=palmLandmarks,boxAroundHand}transformRawCoords(rawCoords,box2,angle,rotationMatrix){const boxSize=getBoxSize(box2),scaleFactor=[boxSize[0]/this.inputSize,boxSize[1]/this.inputSize],coordsScaled=rawCoords.map(coord=>[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>{const rotated=rotatePoint(coord,coordsRotationMatrix);return[...rotated,coord[2]]}),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot2(boxCenter,inverseRotationMatrix[0]),dot2(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config2){this.skipped++;let useFreshBox=!1,boxes;if((this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.boxDetector.estimateHandBounds(image3,config2),image3.shape[1]!==255&&image3.shape[2]!==255&&(this.skipped=0)),boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)){this.storedBoxes=[],this.detectedHands=0;for(const possible of boxes)this.storedBoxes.push(possible);this.storedBoxes.length>0&&(useFreshBox=!0)}const hands=[];for(const i in this.storedBoxes){const currentBox=this.storedBoxes[i];if(!currentBox)continue;if(config2.hand.landmarks){const angle=computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE],currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]),palmCenter=getBoxCenter(currentBox),palmCenterNormalized=[palmCenter[0]/image3.shape[2],palmCenter[1]/image3.shape[1]],rotatedImage=image.rotateWithOffset(image3,angle,0,palmCenterNormalized),rotationMatrix=buildRotationMatrix(-angle,palmCenter),newBox=useFreshBox?this.getBoxForPalmLandmarks(currentBox.palmLandmarks,rotationMatrix):currentBox,croppedInput=cutBoxFromImageAndResize(newBox,rotatedImage,[this.inputSize,this.inputSize]),handImage=croppedInput.div(255);croppedInput.dispose(),rotatedImage.dispose();const[confidence,keypoints]=await this.meshDetector.predict(handImage);handImage.dispose();const confidenceValue=confidence.dataSync()[0];if(confidence.dispose(),confidenceValue>=config2.hand.minConfidence){const keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();const coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;const result={landmarks:coords2,confidence:confidenceValue,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{const enlarged=enlargeBox(squarifyBox(shiftBox(currentBox,HAND_BOX_SHIFT_VECTOR)),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){const xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}}exports.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports=>{exports.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_cente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${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),console.log(`Human: load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handpose2}exports.load=load2}),require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];const gestures=[];for(const pose of res){const leftWrist=pose.keypoints.find(a=>a.part==="leftWrist"),rightWrist=pose.keypoints.find(a=>a.part==="rightWrist"),nose=pose.keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.y<nose.position.y&&rightWrist.position.y<nose.position.y?gestures.push("i give up"):nose&&leftWrist&&leftWrist.position.y<nose.position.y?gestures.push("raise left hand"):nose&&rightWrist&&rightWrist.position.y<nose.position.y&&gestures.push("raise right hand");const leftShoulder=pose.keypoints.find(a=>a.part==="leftShoulder"),rightShoulder=pose.keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push(`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`)}return gestures};exports.face=res=>{if(!res)return[];const gestures=[];for(const face2 of res)if(face2.mesh&&face2.mesh.length>0){const eyeFacing=face2.mesh[35][2]-face2.mesh[263][2];Math.abs(eyeFacing)<10?gestures.push("facing camera"):gestures.push(`facing ${eyeFacing<0?"right":"left"}`);const openLeft=Math.abs(face2.mesh[374][1]-face2.mesh[386][1])/Math.abs(face2.mesh[443][1]-face2.mesh[450][1]);openLeft<.2&&gestures.push("blink left eye");const openRight=Math.abs(face2.mesh[145][1]-face2.mesh[159][1])/Math.abs(face2.mesh[223][1]-face2.mesh[230][1]);openRight<.2&&gestures.push("blink right eye");const mouthOpen=Math.min(100,500*Math.abs(face2.mesh[13][1]-face2.mesh[14][1])/Math.abs(face2.mesh[10][1]-face2.mesh[152][1]));mouthOpen>10&&gestures.push(`mouth ${Math.trunc(mouthOpen)}% open`);const chinDepth=face2.mesh[152][2];Math.abs(chinDepth)>10&&gestures.push(`head ${chinDepth<0?"up":"down"}`)}return gestures};exports.hand=res=>{if(!res)return[];const gestures=[];for(const hand2 of res){const fingers=[];for(const[finger,pos]of Object.entries(hand2.annotations))finger!=="palmBase"&&fingers.push({name:finger.toLowerCase(),position:pos[0]});if(fingers&&fingers.length>0){const closest=fingers.reduce((best,a)=>best.position[2]<a.position[2]?best:a),highest=fingers.reduce((best,a)=>best.position[1]<a.position[1]?best:a);gestures.push(`${closest.name} forward ${highest.name} up`)}}return gestures}}),require_imagefx=__commonJS(exports=>{const WebGLProgram=function(gl,vertexSource,fragmentSource){const _collect=function(source,prefix,collection){const r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){const shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};const _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(const a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(const u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null;const _canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){const args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;const f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};const _resize=function(width,height){if(width===_width&&height===_height)return;if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){const vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){const fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);const renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);const texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=_getTempFramebuffer(_currentFramebufferIndex).texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2===0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=_getTempFramebuffer(_currentFramebufferIndex).fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);const floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram};let DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
`),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`);let _filter={};_filter.colorMatrix=function(matrix){const m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;const shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
`),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
`),_filter.brightness=function(brightness){const b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){const x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){const v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;const cos2=Math.cos(rotation),sin2=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos2*(1-lumR)+sin2*-lumR,lumG+cos2*-lumG+sin2*-lumG,lumB+cos2*-lumB+sin2*(1-lumB),0,0,lumR+cos2*-lumR+sin2*.143,lumG+cos2*(1-lumG)+sin2*.14,lumB+cos2*-lumB+sin2*-.283,0,0,lumR+cos2*-lumR+sin2*-(1-lumR),lumG+cos2*-lumG+sin2*lumG,lumB+cos2*(1-lumB)+sin2*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){const m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){const a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){const s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){const blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;","}"].join(`
`),_filter.pixelate=function(size){const blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)};exports.Canvas=WebGLImageFilter}),require_image=__commonJS(exports=>{const fxImage=__toModule(require_imagefx());let inCanvas=null,outCanvas=null;function process3(input2,config2){let tensor;if(input2 instanceof Tensor)tensor=clone(input2);else{const originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0;let targetWidth=originalWidth,targetHeight=originalHeight;config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));const ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){(!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas);const gl=!1;if(gl){const glBuffer=new Uint8Array(outCanvas.width*outCanvas.height*4),pixBuffer=new Uint8Array(outCanvas.width*outCanvas.height*3);gl.readPixels(0,0,outCanvas.width,outCanvas.height,gl.RGBA,gl.UNSIGNED_BYTE,glBuffer);let i=0;for(let y=outCanvas.height-1;y>=0;y--)for(let x=0;x<outCanvas.width;x++){const index=(x+y*outCanvas.width)*4;pixBuffer[i++]=glBuffer[index+0],pixBuffer[i++]=glBuffer[index+1],pixBuffer[i++]=glBuffer[index+2]}outCanvas.data=pixBuffer}}else outCanvas=inCanvas;let pixels;if(outCanvas.data){const shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{const tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;const tempCtx=tempCanvas.getContext("2d");tempCtx.drawImage(outCanvas,0,0);const data2=tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}const casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports.process=process3});const 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copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports3,module)=>{(function(pool6,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten5(options.entropy?[seed,tostring(pool6)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool6),(options.pass||callback||function(prng2,seed2,is_math_call,state6){return state6&&(state6.S&&copy(state6,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten5(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten5(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser=global2.navigator,plugins=browser&&browser.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool6)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool6),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom2=__commonJS2((exports3,module)=>{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state6=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=String(data2);for(var i=0;i<data2.length;i++){n+=data2.charCodeAt(i);var h=.02519603282416938*n;n=h>>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.x&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.X&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports3,module)=>{(function(global2,pool6,math){var width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten5(options.entropy?[seed,tostring(pool6)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool6),(options.pass||callback||function(prng2,seed2,is_math_call,state6){return state6&&(state6.S&&copy(state6,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten5(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten5(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser=global2.navigator,plugins=browser&&browser.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool6)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool6),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd?define(function(){return seedrandom5}):math["seed"+rngname]=seedrandom5})(typeof self!="undefined"?self:exports3,[],Math)}),require_seedrandom4=__commonJS2((exports3,module)=>{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_string_decoder=__commonJS2(()=>{}),require_alea3=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state6=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i<data2.length;i++){n+=data2.charCodeAt(i);var h=.02519603282416938*n;n=h>>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1283=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow3=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k<strseed.length+64;k++)me.x^=strseed.charCodeAt(k)|0,k==strseed.length&&(me.d=me.x<<10^me.x>>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift73=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j<seed2.length;++j)X[j&7]=X[j&7]<<15^seed2.charCodeAt(j)+X[j+1&7]<<13;for(;X.length<8;)X.push(0);for(j=0;j<8&&X[j]===0;++j);for(j==8?w=X[7]=-1:w=X[j],me2.x=X,me2.i=0,j=256;j>0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.x&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40963=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j<limit;++j)seed2&&(v^=seed2.charCodeAt((j+32)%seed2.length)),j===0&&(w=v),v^=v<<10,v^=v>>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(state6.X&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei3=__commonJS2((exports3,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k<strseed.length+20;k++)me.b^=strseed.charCodeAt(k)|0,me.next()}function copy(f,t){return t.a=f.a,t.b=f.b,t.c=f.c,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state6=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state6&&(typeof state6=="object"&&copy(state6,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports3,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom5=__commonJS2((exports3,module)=>{(function(pool6,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten5(options.entropy?[seed,tostring(pool6)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n<significance;)n=(n+x)*width,d*=width,x=arc4.g(1);for(;n>=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool6),(options.pass||callback||function(prng2,seed2,is_math_call,state6){return state6&&(state6.S&&copy(state6,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i<width;)s[i]=i++;for(i=0;i<width;i++)s[i]=s[j=mask&j+key[i%keylen]+(t=s[i])],s[j]=t;(me.g=function(count2){for(var t2,r=0,i2=me.i,j2=me.j,s2=me.S;count2--;)t2=s2[i2=mask&i2+1],r=r*width+s2[mask&(s2[i2]=s2[j2=mask&j2+t2])+(s2[j2]=t2)];return me.i=i2,me.j=j2,r})(width)}function copy(f,t){return t.i=f.i,t.j=f.j,t.S=f.S.slice(),t}function flatten5(obj,depth){var result=[],typ=typeof obj,prop;if(depth&&typ=="object")for(prop in obj)try{result.push(flatten5(obj[prop],depth-1))}catch(e){}return result.length?result:typ=="string"?obj:obj+"\0"}function mixkey(seed,key){for(var stringseed=seed+"",smear,j=0;j<stringseed.length;)key[mask&j]=mask&(smear^=key[mask&j]*19)+stringseed.charCodeAt(j++);return tostring(key)}function autoseed(){try{var out;return nodecrypto&&(out=nodecrypto.randomBytes)?out=out(width):(out=new Uint8Array(width),(global2.crypto||global2.msCrypto).getRandomValues(out)),tostring(out)}catch(e){var browser=global2.navigator,plugins=browser&&browser.plugins;return[+new Date,global2,plugins,global2.screen,tostring(pool6)]}}function tostring(a){return String.fromCharCode.apply(0,a)}if(mixkey(math.random(),pool6),typeof module=="object"&&module.exports){module.exports=seedrandom5;try{nodecrypto=require_crypto()}catch(ex){}}else typeof define=="function"&&define.amd&&define(function(){return seedrandom5})})([],Math)}),require_seedrandom6=__commonJS2((exports3,module)=>{var alea5=require_alea3(),xor128=require_xor1283(),xorwow=require_xorwow3(),xorshift7=require_xorshift73(),xor4096=require_xor40963(),tychei=require_tychei3(),sr=require_seedrandom5();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports3,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer11&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer11=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir&&(scriptDirectory=_scriptDir),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:165,maximum:165+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack9=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack9===0&&(stack9=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack9!==0&&stackRestore(stack9),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,str="";!(idx>=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i<str.length;++i){var u=str.charCodeAt(i);u>=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer12){GROWABLE_HEAP_I8().set(array2,buffer12)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256384,STACKTOP=STACK_BASE,STACK_MAX=13504,DYNAMIC_BASE=5256384,DYNAMICTOP_PTR=12576,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer11=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer11=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer11.byteLength,updateGlobalBufferAndViews(buffer11),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports5=instance.exports;if(Module.asm=exports5,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13488;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i<pthreadPoolSize;++i)PThread.allocateUnusedWorker();PThread.mainThreadBlock=12736;for(var i=0;i<232/4;++i)GROWABLE_HEAP_U32()[PThread.mainThreadBlock/4+i]=0;GROWABLE_HEAP_I32()[PThread.mainThreadBlock+12>>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=12976,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i],pthread=worker.pthread;PThread.freeThreadData(pthread),worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data2){worker.onmessage({data:data2})}),worker.on("error",function(data2){worker.onerror(data2)}),worker.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()<t;);}};function establishStackSpace(stackTop,stackMax){STACK_BASE=STACKTOP=stackTop,STACK_MAX=stackMax,stackRestore(stackTop)}Module.establishStackSpace=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module.getNoExitRuntime=getNoExitRuntime;function ___assert_fail(condition,filename,line,func2){abort("Assertion failed: "+UTF8ToString(condition)+", at: "+[filename?UTF8ToString(filename):"unknown filename",line,func2?UTF8ToString(func2):"unknown function"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;ENVIRONMENT_IS_NODE?_emscripten_get_now=function(){var t=process.hrtime();return t[0]*1e3+t[1]/1e6}:ENVIRONMENT_IS_PTHREAD?_emscripten_get_now=function(){return performance.now()-Module.__performance_now_clock_drift}:typeof dateNow!="undefined"?_emscripten_get_now=dateNow:_emscripten_get_now=function(){return performance.now()};function setErrNo(value){return GROWABLE_HEAP_I32()[___errno_location()>>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack9=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i<numCallArgs;i++)GROWABLE_HEAP_F64()[b+i]=arguments[2+i];var ret=_emscripten_run_in_main_runtime_thread_js(index,numCallArgs,args,sync);return stackRestore(stack9),ret}var _emscripten_receive_on_main_thread_js_callArgs=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgs.array||(readAsmConstArgs.array=[]);var args=readAsmConstArgs.array;args.length=0;for(var ch;ch=GROWABLE_HEAP_U8()[sigPtr++];)ch===100||ch===102?(buf=buf+7&~7,args.push(GROWABLE_HEAP_F64()[buf>>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i<numCallArgs;i++)_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i];var isEmAsmConst=index<0,func2=isEmAsmConst?ASM_CONSTS[-index-1]:proxiedFunctionTable[index];if(isEmAsmConst){var sigPtr=_emscripten_receive_on_main_thread_js_callArgs[1],varargPtr=_emscripten_receive_on_main_thread_js_callArgs[2],constArgs=readAsmConstArgs(sigPtr,varargPtr);return func2.apply(null,constArgs)}return func2.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{return wasmMemory.grow(size-buffer11.byteLength+65535>>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i)JSEvents.deferredCalls[i].targetFunction==targetFunction&&(JSEvents.deferredCalls.splice(i,1),--i)},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests())return;for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1),--i,call.targetFunction.apply(null,call.argsList)}},inEventHandler:0,currentEventHandler:null,eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)&&JSEvents._removeHandler(i--)},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture),JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function(event){++JSEvents.inEventHandler,JSEvents.currentEventHandler=eventHandler,JSEvents.runDeferredCalls(),eventHandler.handlerFunc(event),JSEvents.runDeferredCalls(),--JSEvents.inEventHandler};if(eventHandler.callbackfunc)eventHandler.eventListenerFunc=jsEventHandler,eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture),JSEvents.eventHandlers.push(eventHandler),JSEvents.registerRemoveEventListeners();else for(var i=0;i<JSEvents.eventHandlers.length;++i)JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString&&JSEvents._removeHandler(i--)},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave(),varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferFloatViews[i]=miniTempFloatBuffer.subarray(0,i+1);for(var miniTempIntBuffer=new Int32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i<GL.MINI_TEMP_BUFFER_SIZE;i++)GL.miniTempBufferIntViews[i]=miniTempIntBuffer.subarray(0,i+1)},recordError:function(errorCode){GL.lastError||(GL.lastError=errorCode)},getNewId:function(table){for(var ret=GL.counter++,i=table.length;i<ret;i++)table[i]=null;return ret},MINI_TEMP_BUFFER_SIZE:256,miniTempBufferFloatViews:[0],miniTempBufferIntViews:[0],getSource:function(shader,count2,string,length){for(var source="",i=0;i<count2;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),context.initExtensionsDone)return;context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p2,i),name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1),name.slice(-1)=="]"&&(name=name.slice(0,name.lastIndexOf("[")));var loc=GLctx.getUniformLocation(p2,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id],GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+"["+j+"]";loc=GLctx.getUniformLocation(p2,n),id=GL.getNewId(GL.uniforms),GL.uniforms[id]=loc}}}}},__emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var contextAttributes={},a=attributes>>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer12=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer12,0)),buffer12.length=0):buffer12.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i<iovcnt;i++){for(var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j]);num+=len}return GROWABLE_HEAP_I32()[pnum>>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 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Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;ENVIRONMENT_IS_NODE?(ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"}):ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir&&(scriptDirectory=_scriptDir),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)},setWindowTitle=function(title){document.title=title});var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:147,maximum:147+0,element:"anyfunc"}),ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack9=0;if(args)for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];converter?(stack9===0&&(stack9=stackSave()),cArgs[i]=converter(args[i])):cArgs[i]=args[i]}var ret=func2.apply(null,cArgs);return ret=convertReturnValue(ret),stack9!==0&&stackRestore(stack9),ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"}),numericRet=returnType!=="string";return numericRet&&numericArgs&&!opts?getCFunc(ident):function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function UTF8ArrayToString(heap,idx,maxBytesToRead){for(var endIdx=idx+maxBytesToRead,endPtr=idx;heap[endPtr]&&!(endPtr>=endIdx);)++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder)return UTF8Decoder.decode(heap.subarray(idx,endPtr));for(var str="";idx<endPtr;){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array2,buffer12){HEAP8.set(array2,buffer12)}var buffer11,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer11=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;function callRuntimeCallbacks(callbacks3){for(;callbacks3.length>0;){var callback=callbacks3.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATPOSTRUN__=[],runtimeInitialized=!1,runtimeExited=!1;function preRun(){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=!0}function postRun(){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={env:asmLibraryArg,wasi_snapshot_preview1:asmLibraryArg};function receiveInstance(instance,module2){var exports5=instance.exports;Module.asm=exports5,wasmMemory=exports5.memory,updateGlobalBufferAndViews(wasmMemory.buffer),removeRunDependency("wasm-instantiate")}addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports4=Module.instantiateWasm(info,receiveInstance);return exports4}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer12=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer12,0)),buffer12.length=0):buffer12.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){for(var num=0,i=0;i<iovcnt;i++){for(var ptr=HEAP32[iov+i*8>>2],len=HEAP32[iov+(i*8+4)>>2],j=0;j<len;j++)SYSCALLS.printChar(fd,HEAPU8[ptr+j]);num+=len}return HEAP32[pnum>>2]=num,0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}var asmLibraryArg={emscripten_notify_memory_growth:_emscripten_notify_memory_growth,fd_close:_fd_close,fd_seek:_fd_seek,fd_write:_fd_write,proc_exit:_proc_exit,roundf:_roundf},asm=createWasm();Module.asm=asm;var _init=Module._init=function(){return(_init=Module._init=Module.asm.init).apply(null,arguments)},_register_tensor=Module._register_tensor=function(){return(_register_tensor=Module._register_tensor=Module.asm.register_tensor).apply(null,arguments)},_dispose_data=Module._dispose_data=function(){return(_dispose_data=Module._dispose_data=Module.asm.dispose_data).apply(null,arguments)},_dispose=Module._dispose=function(){return(_dispose=Module._dispose=Module.asm.dispose).apply(null,arguments)},_Abs=Module._Abs=function(){return(_Abs=Module._Abs=Module.asm.Abs).apply(null,arguments)},_Add=Module._Add=function(){return(_Add=Module._Add=Module.asm.Add).apply(null,arguments)},_AddN=Module._AddN=function(){return(_AddN=Module._AddN=Module.asm.AddN).apply(null,arguments)},_ArgMax=Module._ArgMax=function(){return(_ArgMax=Module._ArgMax=Module.asm.ArgMax).apply(null,arguments)},_AvgPool=Module._AvgPool=function(){return(_AvgPool=Module._AvgPool=Module.asm.AvgPool).apply(null,arguments)},_BatchMatMul=Module._BatchMatMul=function(){return(_BatchMatMul=Module._BatchMatMul=Module.asm.BatchMatMul).apply(null,arguments)},_ClipByValue=Module._ClipByValue=function(){return(_ClipByValue=Module._ClipByValue=Module.asm.ClipByValue).apply(null,arguments)},_Conv2D=Module._Conv2D=function(){return(_Conv2D=Module._Conv2D=Module.asm.Conv2D).apply(null,arguments)},_Conv2DBackpropInput=Module._Conv2DBackpropInput=function(){return(_Conv2DBackpropInput=Module._Conv2DBackpropInput=Module.asm.Conv2DBackpropInput).apply(null,arguments)},_Cos=Module._Cos=function(){return(_Cos=Module._Cos=Module.asm.Cos).apply(null,arguments)},_CropAndResize=Module._CropAndResize=function(){return(_CropAndResize=Module._CropAndResize=Module.asm.CropAndResize).apply(null,arguments)},_Cumsum=Module._Cumsum=function(){return(_Cumsum=Module._Cumsum=Module.asm.Cumsum).apply(null,arguments)},_DepthToSpace=Module._DepthToSpace=function(){return(_DepthToSpace=Module._DepthToSpace=Module.asm.DepthToSpace).apply(null,arguments)},_DepthwiseConv2dNative=Module._DepthwiseConv2dNative=function(){return(_DepthwiseConv2dNative=Module._DepthwiseConv2dNative=Module.asm.DepthwiseConv2dNative).apply(null,arguments)},_Div=Module._Div=function(){return(_Div=Module._Div=Module.asm.Div).apply(null,arguments)},_Equal=Module._Equal=function(){return(_Equal=Module._Equal=Module.asm.Equal).apply(null,arguments)},_Exp=Module._Exp=function(){return(_Exp=Module._Exp=Module.asm.Exp).apply(null,arguments)},_FlipLeftRight=Module._FlipLeftRight=function(){return(_FlipLeftRight=Module._FlipLeftRight=Module.asm.FlipLeftRight).apply(null,arguments)},_FloorDiv=Module._FloorDiv=function(){return(_FloorDiv=Module._FloorDiv=Module.asm.FloorDiv).apply(null,arguments)},_FusedBatchNorm=Module._FusedBatchNorm=function(){return(_FusedBatchNorm=Module._FusedBatchNorm=Module.asm.FusedBatchNorm).apply(null,arguments)},_FusedConv2D=Module._FusedConv2D=function(){return(_FusedConv2D=Module._FusedConv2D=Module.asm.FusedConv2D).apply(null,arguments)},_FusedDepthwiseConv2D=Module._FusedDepthwiseConv2D=function(){return(_FusedDepthwiseConv2D=Module._FusedDepthwiseConv2D=Module.asm.FusedDepthwiseConv2D).apply(null,arguments)},_Gather=Module._Gather=function(){return(_Gather=Module._Gather=Module.asm.Gather).apply(null,arguments)},_GatherNd=Module._GatherNd=function(){return(_GatherNd=Module._GatherNd=Module.asm.GatherNd).apply(null,arguments)},_Greater=Module._Greater=function(){return(_Greater=Module._Greater=Module.asm.Greater).apply(null,arguments)},_GreaterEqual=Module._GreaterEqual=function(){return(_GreaterEqual=Module._GreaterEqual=Module.asm.GreaterEqual).apply(null,arguments)},_Less=Module._Less=function(){return(_Less=Module._Less=Module.asm.Less).apply(null,arguments)},_LessEqual=Module._LessEqual=function(){return(_LessEqual=Module._LessEqual=Module.asm.LessEqual).apply(null,arguments)},_Log=Module._Log=function(){return(_Log=Module._Log=Module.asm.Log).apply(null,arguments)},_LogicalAnd=Module._LogicalAnd=function(){return(_LogicalAnd=Module._LogicalAnd=Module.asm.LogicalAnd).apply(null,arguments)},_Max=Module._Max=function(){return(_Max=Module._Max=Module.asm.Max).apply(null,arguments)},_MaxPool=Module._MaxPool=function(){return(_MaxPool=Module._MaxPool=Module.asm.MaxPool).apply(null,arguments)},_Maximum=Module._Maximum=function(){return(_Maximum=Module._Maximum=Module.asm.Maximum).apply(null,arguments)},_Min=Module._Min=function(){return(_Min=Module._Min=Module.asm.Min).apply(null,arguments)},_Minimum=Module._Minimum=function(){return(_Minimum=Module._Minimum=Module.asm.Minimum).apply(null,arguments)},_Multiply=Module._Multiply=function(){return(_Multiply=Module._Multiply=Module.asm.Multiply).apply(null,arguments)},_Negate=Module._Negate=function(){return(_Negate=Module._Negate=Module.asm.Negate).apply(null,arguments)},_NonMaxSuppressionV3=Module._NonMaxSuppressionV3=function(){return(_NonMaxSuppressionV3=Module._NonMaxSuppressionV3=Module.asm.NonMaxSuppressionV3).apply(null,arguments)},_NonMaxSuppre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calledRun;Module.then=function(func2){if(calledRun)func2(Module);else{var old=Module.onRuntimeInitialized;Module.onRuntimeInitialized=function(){old&&old(),func2(Module)}}return Module};function ExitStatus(status){this.name="ExitStatus",this.message="Program terminated with exit("+status+")",this.status=status}var calledMain=!1;dependenciesFulfilled=function runCaller(){calledRun||run(),calledRun||(dependenciesFulfilled=runCaller)};function callMain(args){var entryFunction=Module.__start;try{entryFunction();var ret=0;exit(ret,!0)}catch(e){if(e instanceof ExitStatus)return;if(e=="unwind"){noExitRuntime=!0;return}else{var toLog=e;e&&typeof e=="object"&&e.stack&&(toLog=[e,e.stack]),err("exception thrown: "+toLog),quit_(1,e)}}finally{calledMain=!0}}function run(args){if(args=args||arguments_,runDependencies>0)return;if(preRun(),runDependencies>0)return;function 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exports3=="object"&&(exports3.WasmBackendModule=WasmBackendModule)});const EPSILON_FLOAT32=1e-7,EPSILON_FLOAT16=1e-4;class DataStorage{constructor(backend3,dataMover){this.backend=backend3,this.dataMover=dataMover,this.data=new WeakMap,this.dataIdsCount=0}get(dataId){return this.data.has(dataId)||this.dataMover.moveData(this.backend,dataId),this.data.get(dataId)}set(dataId,value){this.dataIdsCount++,this.data.set(dataId,value)}has(dataId){return this.data.has(dataId)}delete(dataId){return this.dataIdsCount--,this.data.delete(dataId)}numDataIds(){return this.dataIdsCount}}class KernelBackend{time(f){return notYetImplemented("time")}read(dataId){return notYetImplemented("read")}readSync(dataId){return notYetImplemented("readSync")}numDataIds(){return notYetImplemented("numDataIds")}disposeData(dataId){return notYetImplemented("disposeData")}write(values,shape,dtype){return notYetImplemented("write")}move(dataId,values,shape,dtype){return notYetImplemented("move")}memory(){return notYetImplemented("memory")}floatPrecision(){return notYetImplemented("floatPrecision")}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT32:EPSILON_FLOAT16}batchMatMul(a,b,transposeA,transposeB){return notYetImplemented("batchMatMul")}fusedBatchMatMul({a,b,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}){return notYetImplemented("fusedBatchMatMul")}slice(x,begin,size){return notYetImplemented("slice")}stridedSlice(x,begin,end,strides){return notYetImplemented("stridedSlice")}unstack(x,axis){return notYetImplemented("unstack")}reverse(a,axis){return notYetImplemented("reverse")}concat(tensors,axis){return notYetImplemented("concat")}neg(a){return notYetImplemented("neg")}add(a,b){return notYetImplemented("add")}addN(tensors){return notYetImplemented("addN")}subtract(a,b){return notYetImplemented("subtract")}multiply(a,b){return notYetImplemented("multiply")}realDivide(a,b){return notYetImplemented("realDivide")}floorDiv(a,b){return notYetImplemented("floorDiv")}sum(x,axes){return notYetImplemented("sum")}prod(x,axes){return notYetImplemented("prod")}unsortedSegmentSum(x,segmentIds,numSegments){return notYetImplemented("unsortedSegmentSum")}argMin(x,axis){return notYetImplemented("argMin")}argMax(x,axis){return notYetImplemented("argMax")}equal(a,b){return notYetImplemented("equal")}notEqual(a,b){return notYetImplemented("notEqual")}less(a,b){return notYetImplemented("less")}lessEqual(a,b){return notYetImplemented("lessEqual")}greater(a,b){return notYetImplemented("greater")}greaterEqual(a,b){return notYetImplemented("greaterEqual")}logicalNot(a){return notYetImplemented("logicalNot")}logicalAnd(a,b){return notYetImplemented("logicalAnd")}logicalOr(a,b){return notYetImplemented("logicalOr")}where(condition){return notYetImplemented("where")}select(condition,a,b){return notYetImplemented("select")}topk(x,k,sorted){return notYetImplemented("topk")}min(x,axes){return notYetImplemented("min")}minimum(a,b){return notYetImplemented("minimum")}mod(a,b){return notYetImplemented("mod")}max(x,axes){return notYetImplemented("max")}maximum(a,b){return notYetImplemented("maximum")}all(x,axes){return notYetImplemented("all")}any(x,axes){return notYetImplemented("any")}squaredDifference(a,b){return notYetImplemented("squaredDifference")}ceil(x){return notYetImplemented("ceil")}floor(x){return notYetImplemented("floor")}round(x){return notYetImplemented("round")}sign(x){return notYetImplemented("sign")}isNaN(x){return notYetImplemented("isNaN")}isInf(x){return notYetImplemented("isInf")}isFinite(x){return notYetImplemented("isFinite")}pow(a,b){return notYetImplemented("pow")}exp(x){return notYetImplemented("exp")}expm1(x){return notYetImplemented("expm1")}softmax(x,dim){return notYetImplemented("softmax")}log(x){return notYetImplemented("log")}log1p(x){return notYetImplemented("log1p")}sqrt(x){return notYetImplemented("sqrt")}rsqrt(x){return notYetImplemented("rsqrt")}square(x){return notYetImplemented("square")}reciprocal(x){return notYetImplemented("reciprocal")}relu(x){return notYetImplemented("relu")}relu6(x){return notYetImplemented("relu6")}prelu(x,a){return notYetImplemented("prelu")}elu(x){return notYetImplemented("elu")}eluDer(dy,y){return notYetImplemented("eluDer")}selu(x){return notYetImplemented("selu")}int(x){return notYetImplemented("int")}clip(x,min8,max10){return notYetImplemented("clip")}abs(x){return notYetImplemented("abs")}complexAbs(x){return notYetImplemented("complexAbs")}sigmoid(x){return notYetImplemented("sigmoid")}softplus(x){return notYetImplemented("softplus")}sin(x){return notYetImplemented("sin")}cos(x){return notYetImplemented("cos")}tan(x){return notYetImplemented("tan")}asin(x){return notYetImplemented("asin")}acos(x){return notYetImplemented("acos")}atan(x){return notYetImplemented("atan")}atan2(a,b){return notYetImplemented("atan2")}sinh(x){return notYetImplemented("sinh")}cosh(x){return notYetImplemented("cosh")}tanh(x){return notYetImplemented("tanh")}asinh(x){return notYetImplemented("asinh")}acosh(x){return notYetImplemented("acosh")}atanh(x){return notYetImplemented("atanh")}erf(x){return notYetImplemented("erf")}step(x,alpha){return notYetImplemented("step")}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){return notYetImplemented("fusedConv2d")}conv2d(x,filter,convInfo){return notYetImplemented("conv2d")}conv2dDerInput(dy,filter,convInfo){return notYetImplemented("conv2dDerInput")}conv2dDerFilter(x,dY,convInfo){return notYetImplemented("conv2dDerFilter")}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){return notYetImplemented("fusedDepthwiseConv2D")}depthwiseConv2D(input2,filter,convInfo){return notYetImplemented("depthwiseConv2D")}depthwiseConv2DDerInput(dy,filter,convInfo){return notYetImplemented("depthwiseConv2DDerInput")}depthwiseConv2DDerFilter(x,dY,convInfo){return notYetImplemented("depthwiseConv2DDerFilter")}conv3d(x,filter,convInfo){return notYetImplemented("conv3d")}conv3dDerInput(dy,filter,convInfo){return notYetImplemented("conv3dDerInput")}conv3dDerFilter(x,dY,convInfo){return notYetImplemented("conv3dDerFilter")}maxPool(x,convInfo){return notYetImplemented("maxPool")}maxPoolBackprop(dy,x,y,convInfo){return notYetImplemented("maxPoolBackprop")}avgPool(x,convInfo){return notYetImplemented("avgPool")}avgPoolBackprop(dy,x,convInfo){return notYetImplemented("avgPoolBackprop")}avgPool3d(x,convInfo){return notYetImplemented("avgPool3d")}avgPool3dBackprop(dy,x,convInfo){return notYetImplemented("avgPool3dBackprop")}maxPool3d(x,convInfo){return notYetImplemented("maxPool3d")}maxPool3dBackprop(dy,x,y,convInfo){return notYetImplemented("maxPool3dBackprop")}reshape(x,shape){return notYetImplemented("reshape")}cast(x,dtype){return notYetImplemented("cast")}tile(x,reps){return notYetImplemented("tile")}pad(x,paddings,constantValue){return notYetImplemented("pad")}transpose(x,perm){return notYetImplemented("transpose")}gather(x,indices,axis){return notYetImplemented("gather")}gatherND(x,indices){return notYetImplemented("gatherND")}scatterND(indices,updates,shape){return notYetImplemented("scatterND")}batchToSpaceND(x,blockShape,crops){return notYetImplemented("batchToSpaceND")}spaceToBatchND(x,blockShape,paddings){return notYetImplemented("spaceToBatchND")}resizeBilinear(x,newHeight,newWidth,alignCorners){return notYetImplemented("resizeBilinear")}resizeBilinearBackprop(dy,x,alignCorners){return notYetImplemented("resizeBilinearBackprop")}resizeNearestNeighbor(x,newHEight,newWidth,alignCorners){return notYetImplemented("resizeNearestNeighbor")}resizeNearestNeighborBackprop(dy,x,alignCorners){return notYetImplemented("resizeNearestNeighborBackprop")}batchNorm(x,mean7,variance,offset,scale2,varianceEpsilon){return notYetImplemented("batchNorm")}localResponseNormalization4D(x,radius,bias,alpha,beta){return notYetImplemented("localResponseNormalization4D")}LRNGrad(dy,inputImage,outputImage,radius,bias,alpha,beta){return notYetImplemented("LRNGrad")}multinomial(logits,normalized,numSamples,seed){return notYetImplemented("multinomial")}oneHot(indices,depth,onValue,offValue){return notYetImplemented("oneHot")}cumsum(x,axis,exclusive,reverse12){return notYetImplemented("cumsum")}nonMaxSuppression(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return notYetImplemented("nonMaxSuppression")}fft(x){return notYetImplemented("fft")}ifft(x){return notYetImplemented("ifft")}complex(real8,imag8){return notYetImplemented("complex")}real(input2){return notYetImplemented("real")}imag(input2){return notYetImplemented("imag")}cropAndResize(image3,boxes,boxIndex,cropSize,method,extrapolationValue){return notYetImplemented("cropAndResize")}depthToSpace(x,blockSize,dataFormat){return notYetImplemented("depthToSpace")}split(value,sizeSplits,axis){return notYetImplemented("split")}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){return notYetImplemented("sparseToDense")}diag(x){return notYetImplemented("diag")}fill(shape,value,dtype){return notYetImplemented("fill")}onesLike(x){return notYetImplemented("onesLike")}zerosLike(x){return notYetImplemented("zerosLike")}linspace(start,stop,num){return notYetImplemented("linspace")}dispose(){return notYetImplemented("dispose")}}function notYetImplemented(kernelName){throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function shuffle(array2){let counter=array2.length,temp=0,index=0;for(;counter>0;)index=Math.random()*counter|0,counter--,temp=array2[counter],array2[counter]=array2[index],array2[index]=temp}function clamp(min8,x,max10){return Math.max(min8,Math.min(x,max10))}function nearestLargerEven(val){return val%2===0?val:val+1}function sum(arr){let sum29=0;for(let i=0;i<arr.length;i++)sum29+=arr[i];return sum29}function randUniform(a,b){const r=Math.random();return b*r+(1-r)*a}function distSquared(a,b){let result=0;for(let i=0;i<a.length;i++){const diff=Number(a[i])-Number(b[i]);result+=diff*diff}return result}function assert(expr,msg){if(!expr)throw new Error(typeof msg=="string"?msg:msg())}function assertShapesMatch(shapeA,shapeB,errorMessagePrefix=""){assert(arraysEqual(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function assertNonNull(a){assert(a!=null,()=>"The input to the tensor constructor must be a non-null value.")}function flatten(arr,result=[],skipTypedArray=!1){if(result==null&&(result=[]),Array.isArray(arr)||isTypedArray(arr)&&!skipTypedArray)for(let i=0;i<arr.length;++i)flatten(arr[i],result,skipTypedArray);else result.push(arr);return result}function sizeFromShape(shape){if(shape.length===0)return 1;let size=shape[0];for(let i=1;i<shape.length;i++)size*=shape[i];return size}function isScalarShape(shape){return shape.length===0}function arraysEqual(n1,n2){if(n1===n2)return!0;if(n1==null||n2==null)return!1;if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==n2[i])return!1;return!0}function isInt(a){return a%1===0}function tanh(x){if(Math.tanh!=null)return Math.tanh(x);if(x===Infinity)return 1;if(x===-Infinity)return-1;{const e2x=Math.exp(2*x);return(e2x-1)/(e2x+1)}}function sizeToSquarishShape(size){const width=Math.ceil(Math.sqrt(size));return[width,Math.ceil(size/width)]}function createShuffledIndices(n){const shuffledIndices=new Uint32Array(n);for(let i=0;i<n;++i)shuffledIndices[i]=i;return shuffle(shuffledIndices),shuffledIndices}function rightPad(a,size){return size<=a.length?a:a+" ".repeat(size-a.length)}function repeatedTry(checkFn,delayFn=counter=>0,maxCounter){return new Promise((resolve,reject)=>{let tryCount=0;const tryFn=()=>{if(checkFn()){resolve();return}tryCount++;const nextBackoff=delayFn(tryCount);if(maxCounter!=null&&tryCount>=maxCounter){reject();return}setTimeout(tryFn,nextBackoff)};tryFn()})}function inferFromImplicitShape(shape,size){let shapeProd=1,implicitIdx=-1;for(let i=0;i<shape.length;++i)if(shape[i]>=0)shapeProd*=shape[i];else if(shape[i]===-1){if(implicitIdx!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);implicitIdx=i}else if(shape[i]<0)throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);if(implicitIdx===-1){if(size>0&&size!==shapeProd)throw Error(`Size(${size}) must match the product of shape ${shape}`);return shape}if(shapeProd===0)throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);if(size%shapeProd!==0)throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);const newShape=shape.slice();return newShape[implicitIdx]=size/shapeProd,newShape}function parseAxisParam(axis,shape){const rank=shape.length;return axis=axis==null?shape.map((s,i)=>i):[].concat(axis),assert(axis.every(ax=>ax>=-rank&&ax<rank),()=>`All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`),assert(axis.every(ax=>isInt(ax)),()=>`All values in axis param must be integers but got axis ${axis}`),axis.map(a=>a<0?rank+a:a)}function squeezeShape(shape,axis){const newShape=[],keptDims=[],isEmptyArray=axis!=null&&Array.isArray(axis)&&axis.length===0,axes=axis==null||isEmptyArray?null:parseAxisParam(axis,shape).sort();let j=0;for(let i=0;i<shape.length;++i){if(axes!=null){if(axes[j]===i&&shape[i]!==1)throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);(axes[j]==null||axes[j]>i)&&shape[i]===1&&(newShape.push(shape[i]),keptDims.push(i)),axes[j]<=i&&j++}shape[i]!==1&&(newShape.push(shape[i]),keptDims.push(i))}return{newShape,keptDims}}function getTypedArrayFromDType(dtype,size){let values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);return values}function getArrayFromDType(dtype,size){let values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else if(dtype==="string")values=new Array(size);else throw new Error(`Unknown data type ${dtype}`);return values}function checkConversionForErrors(vals,dtype){for(let i=0;i<vals.length;i++){const num=vals[i];if(isNaN(num)||!isFinite(num))throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`)}}function isValidDtype(dtype){return dtype==="bool"||dtype==="complex64"||dtype==="float32"||dtype==="int32"||dtype==="string"}function hasEncodingLoss(oldType,newType){return newType==="complex64"||newType==="float32"&&oldType!=="complex64"||newType==="int32"&&oldType!=="float32"&&oldType!=="complex64"?!1:!(newType==="bool"&&oldType==="bool")}function isTypedArray(a){return a instanceof Float32Array||a instanceof Int32Array||a instanceof Uint8Array}function bytesPerElement(dtype){if(dtype==="float32"||dtype==="int32")return 4;if(dtype==="complex64")return 8;if(dtype==="bool")return 1;throw new Error(`Unknown dtype ${dtype}`)}function bytesFromStringArray(arr){if(arr==null)return 0;let bytes=0;return arr.forEach(x=>bytes+=x.length),bytes}function isString(value){return typeof value=="string"||value instanceof String}function isBoolean(value){return typeof value=="boolean"}function isNumber(value){return typeof value=="number"}function inferDtype(values){return Array.isArray(values)?inferDtype(values[0]):values instanceof Float32Array?"float32":values instanceof Int32Array||values instanceof Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i<size;++i)if(size%i===0)return i;return size}function computeStrides(shape){const rank=shape.length;if(rank<2)return[];const strides=new Array(rank-1);strides[rank-2]=shape[rank-1];for(let i=rank-3;i>=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){const ret=new Array;if(shape.length===1){const d=shape[0];for(let i=0;i<d;i++)ret[i]=a[offset+i]}else{const d=shape[0],rest=shape.slice(1),len=rest.reduce((acc,c)=>acc*c);for(let i=0;i<d;i++)ret[i]=createNestedArray(offset+i*len,rest,a)}return ret}function toNestedArray(shape,a){if(shape.length===0)return a[0];const size=shape.reduce((acc,c)=>acc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){const array2=makeZerosTypedArray(size,dtype);for(let i=0;i<array2.length;i++)array2[i]=1;return array2}function makeZerosTypedArray(size,dtype){if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(size);if(dtype==="int32")return new Int32Array(size);if(dtype==="bool")return new Uint8Array(size);throw new Error(`Unknown data type ${dtype}`)}function makeZerosNestedTypedArray(shape,dtype){const size=shape.reduce((prev,curr)=>prev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=strides[i]*locs[i];return index}function indexToLoc(index,rank,strides){if(rank===0)return[];if(rank===1)return[index];const locs=new Array(rank);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/strides[i]),index-=locs[i]*strides[i];return locs[locs.length-1]=index,locs}function isPromise(object){return object&&object.then&&typeof object.then=="function"}const TENSORFLOWJS_FLAGS_PREFIX="tfjsflags";class Environment{constructor(global2){this.global=global2,this.flags={},this.flagRegistry={},this.urlFlags={},this.populateURLFlags()}setPlatform(platformName,platform){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platform}.`),this.platformName=platformName,this.platform=platform}registerFlag(flagName,evaluationFn,setHook){if(this.flagRegistry[flagName]={evaluationFn,setHook},this.urlFlags[flagName]!=null){const flagValue=this.urlFlags[flagName];console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`),this.set(flagName,flagValue)}}async getAsync(flagName){return flagName in this.flags?this.flags[flagName]:(this.flags[flagName]=await this.evaluateFlag(flagName),this.flags[flagName])}get(flagName){if(flagName in this.flags)return this.flags[flagName];const flagValue=this.evaluateFlag(flagName);if(isPromise(flagValue))throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[flagName]=flagValue,this.flags[flagName]}getNumber(flagName){return this.get(flagName)}getBool(flagName){return this.get(flagName)}getFlags(){return this.flags}get features(){return this.flags}set(flagName,value){if(this.flagRegistry[flagName]==null)throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);this.flags[flagName]=value,this.flagRegistry[flagName].setHook!=null&&this.flagRegistry[flagName].setHook(value)}evaluateFlag(flagName){if(this.flagRegistry[flagName]==null)throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);return this.flagRegistry[flagName].evaluationFn()}setFlags(flags6){this.flags=Object.assign({},flags6)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;const urlParams=getQueryParams(this.global.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in urlParams){const keyValues=urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");keyValues.forEach(keyValue=>{const[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}}}function getQueryParams(queryString){const params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}let ENV=null;function setEnvironmentGlobal(environment15){ENV=environment15}let globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){const ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){const globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{const singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}const Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolBackprop="AvgPoolBackprop",AvgPool3D="AvgPool3D",AvgPool3DBackprop="AvgPool3DBackprop",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",Div="Div",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNBackprop="LRNBackprop",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolBackprop="MaxPoolBackprop",MaxPool3D="MaxPool3D",MaxPool3DBackprop="MaxPool3DBackprop",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multiply="Multiply",Negate="Negate",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",SelectV2="SelectV2",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){const key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){const it=kernelRegistry.entries(),result=[];for(;;){const{done,value}=it.next();if(done)break;const[key,config2]=value,[backend3]=key.split("_");backend3===backendName&&result.push(config2)}return result}function registerKernel(config2){const{kernelName,backendName}=config2,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config2)}function registerGradient(config2){const{kernelName}=config2;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config2)}function unregisterKernel(kernelName,backendName){const key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){const kernels=getKernelsForBackend(registeredBackendName);kernels.forEach(kernelConfig=>{const newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}const util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){const bool=new Uint8Array(a.length);for(let i=0;i<bool.length;++i)Math.round(a[i])!==0&&(bool[i]=1);return bool}else throw new Error(`Unknown data type ${dtype}`)}function now(){return env().platform.now()}function fetch2(path,requestInits){return env().platform.fetch(path,requestInits)}function encodeString(s,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.encode(s,encoding)}function decodeString(bytes,encoding="utf-8"){return encoding=encoding||"utf-8",env().platform.decode(bytes,encoding)}class Profiler{constructor(backendTimer,logger){this.backendTimer=backendTimer,this.logger=logger,logger==null&&(this.logger=new Logger)}profileKernel(kernelName,inputs,f){let outputs;const holdResultWrapperFn=()=>{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);for(let i=0;i<outputs.length;i++){const output=outputs[i];output.data().then(tensorVals=>{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}const kernelProfile={kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")};return kernelProfile}logKernelProfile(kernelProfile){const{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}}function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i<vals.length;i++){const num=vals[i];if(isNaN(num)||!isFinite(num))return console.warn(`Found ${num} in the result of '${kernelName}'`),!0}return!1}class Logger{logKernelProfile(name,result,vals,timeMs,inputs,extraInfo){const time2=typeof timeMs=="number"?rightPad(`${timeMs}ms`,9):timeMs.error,paddedName=rightPad(name,25),rank=result.rank,size=result.size,shape=rightPad(result.shape.toString(),14);let inputShapesDescription="";for(const name2 in inputs){const input2=inputs[name2];if(input2!=null){const inputShape=input2.shape||result.shape,inputRank=inputShape.length;inputShapesDescription+=`${name2}: ${inputRank}D ${inputRank>0?inputShape:""} `}}console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}}function getFilteredNodesXToY(tape2,xs,y){const tensorsFromX={},nodesFromX={};for(let i=0;i<xs.length;i++)tensorsFromX[xs[i].id]=!0;for(let i=0;i<tape2.length;i++){const node=tape2[i],nodeInputs=node.inputs;for(const inputName in nodeInputs){const input2=nodeInputs[inputName];let anyInputFromX=!1;for(let j=0;j<xs.length;j++)if(tensorsFromX[input2.id]){node.outputs.forEach(output=>tensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}const tensorsLeadToY={};tensorsLeadToY[y.id]=!0;const nodesToY={};for(let i=tape2.length-1;i>=0;i--){const node=tape2[i],nodeInputs=node.inputs;for(let j=0;j<node.outputs.length;j++)if(tensorsLeadToY[node.outputs[j].id]){for(const inputName in nodeInputs)tensorsLeadToY[nodeInputs[inputName].id]=!0,nodesToY[node.id]=!0;break}}const filteredTape=[];for(let i=0;i<tape2.length;i++){const node=tape2[i];if(nodesFromX[node.id]&&nodesToY[node.id]){const prunedInputs={};for(const inputName in node.inputs){const nodeInput=node.inputs[inputName];tensorsFromX[nodeInput.id]&&(prunedInputs[inputName]=nodeInput)}const prunedNode=Object.assign({},node);prunedNode.inputs=prunedInputs,prunedNode.outputs=node.outputs,filteredTape.push(prunedNode)}}return filteredTape}function backpropagateGradients(tensorAccumulatedGradientMap,filteredTape,tidy2,add33){for(let i=filteredTape.length-1;i>=0;i--){const node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{const gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);const inputGradients=node.gradient(dys);for(const inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);const dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);const x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{const curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add33(curGradient,dx),curGradient.dispose()}}}}const FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){const strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(`  dtype: ${dtype}`),lines.push(`  rank: ${rank}`),lines.push(`  shape: [${shape}]`),lines.push("  values:")),lines.push(valsLines.map(l=>"    "+l).join(`
`)),lines.join(`
`)}function computeMaxSizePerColumn(vals,shape,dtype,strides){const n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;row<n/numCols;row++){const offset=row*numCols;for(let j=0;j<numCols;j++)padPerCol[j]=Math.max(padPerCol[j],valToString(valuesOrTuples[offset+j],0,dtype).length)}return padPerCol}function valToString(val,pad11,dtype){let valStr;return Array.isArray(val)?valStr=`${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`:isString(val)?valStr=`'${val}'`:dtype==="bool"?valStr=boolNumToString(val):valStr=parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),rightPad(valStr,pad11)}function boolNumToString(v){return v===0?"false":"true"}function subTensorToString(vals,shape,dtype,strides,padPerCol,isLast=!0){const storagePerElement=dtype==="complex64"?2:1,size=shape[0],rank=shape.length;if(rank===0){if(dtype==="complex64"){const complexTuple=createComplexTuples(vals);return[valToString(complexTuple[0],0,dtype)]}return dtype==="bool"?[boolNumToString(vals[0])]:[vals[0].toString()]}if(rank===1){if(size>FORMAT_LIMIT_NUM_VALS){const firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement;let firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}const displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}const subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i<FORMAT_NUM_FIRST_LAST_VALS;i++){const start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,!1))}lines.push("...");for(let i=size-FORMAT_NUM_FIRST_LAST_VALS;i<size;i++){const start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}}else for(let i=0;i<size;i++){const start=i*stride,end=start+stride;lines.push(...subTensorToString(vals.slice(start,end),subshape,dtype,substrides,padPerCol,i===size-1))}const sep=rank===2?",":"";lines[0]="["+lines[0]+sep;for(let i=1;i<lines.length-1;i++)lines[i]=" "+lines[i]+sep;let newLineSep=`,
`;for(let i=2;i<rank;i++)newLineSep+=`
`;return lines[lines.length-1]=" "+lines[lines.length-1]+"]"+(isLast?"":newLineSep),lines}function createComplexTuples(vals){const complexTuples=[];for(let i=0;i<vals.length;i+=2)complexTuples.push([vals[i],vals[i+1]]);return complexTuples}class TensorBuffer{constructor(shape,dtype,values){if(this.dtype=dtype,this.shape=shape.slice(),this.size=sizeFromShape(shape),values!=null){const n=values.length;assert(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);const index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(const loc of locs){if(loc<0||loc>=this.shape[i]){const msg=`Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2<locs.length-1;++i2)index+=this.strides[i2]*locs[i2];return this.values[index]}locToIndex(locs){if(this.rank===0)return 0;if(this.rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i<locs.length-1;++i)index+=this.strides[i]*locs[i];return index}indexToLoc(index){if(this.rank===0)return[];if(this.rank===1)return[index];const locs=new Array(this.shape.length);for(let i=0;i<locs.length-1;++i)locs[i]=Math.floor(index/this.strides[i]),index-=locs[i]*this.strides[i];return locs[locs.length-1]=index,locs}get rank(){return this.shape.length}toTensor(){return trackerFn().makeTensor(this.values,this.shape,this.dtype)}}let trackerFn=null,opHandler=null,deprecationWarningFn=null;function setTensorTracker(fn){trackerFn=fn}function setOpHandler(handler){opHandler=handler}function setDeprecationWarningFn(fn){deprecationWarningFn=fn}class Tensor{constructor(shape,dtype,dataId,id){this.kept=!1,this.isDisposedInternal=!1,this.shape=shape.slice(),this.dtype=dtype||"float32",this.size=sizeFromShape(shape),this.strides=computeStrides(shape),this.dataId=dataId,this.id=id,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const vals=await this.data();return opHandler.buffer(this.shape,this.dtype,vals)}bufferSync(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())}async array(){const vals=await this.data();return toNestedArray(this.shape,vals)}arraySync(){return toNestedArray(this.shape,this.dataSync())}async data(){this.throwIfDisposed();const data2=trackerFn().read(this.dataId);if(this.dtype==="string"){const bytes=await data2;try{return bytes.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data2}dataSync(){this.throwIfDisposed();const data2=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data2.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data2}async bytes(){this.throwIfDisposed();const data2=await trackerFn().read(this.dataId);return this.dtype==="string"?data2:new Uint8Array(data2.buffer)}dispose(){if(this.isDisposed)return;trackerFn().disposeTensor(this),this.isDisposedInternal=!0}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){const vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}}Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});class Variable extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}}Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});const tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));const upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];const dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor168,tensorList){return tensorList.some(x=>x.id===tensor168.id)}function getTensorsInContainer(result){const list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container2,list,seen){if(container2==null)return;if(container2 instanceof Tensor){list.push(container2);return}if(!isIterable(container2))return;const iterable=container2;for(const k in iterable){const val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}class EngineState{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}dispose(){for(const variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}}class Engine{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;const sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){const backendName=sortedBackends[i],success=await this.initializeBackend(backendName).success;if(success){await this.setBackend(backendName);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){const{name,asyncInit}=this.initializeBackendsAndReturnBest();if(asyncInit)throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(name)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(backendName){if(!(backendName in this.registry))if(backendName in this.registryFactory){const{asyncInit}=this.initializeBackend(backendName);if(asyncInit)return null}else return null;return this.registry[backendName]}findBackendFactory(backendName){return backendName in this.registryFactory?this.registryFactory[backendName].factory:null}registerBackend(backendName,factory,priority=1){return backendName in this.registryFactory?(console.warn(`${backendName} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[backendName]={factory,priority},!0)}async setBackend(backendName){if(this.registryFactory[backendName]==null)throw new Error(`Backend name '${backendName}' not found in registry`);if(this.backendName=backendName,this.registry[backendName]==null){this.backendInstance=null;const{success,asyncInit}=this.initializeBackend(backendName),result=asyncInit?await success:success;if(!result)return!1}return this.backendInstance=this.registry[backendName],this.setupRegisteredKernels(),this.profiler=new Profiler(this.backendInstance),!0}setupRegisteredKernels(){const kernels=getKernelsForBackend(this.backendName);kernels.forEach(kernel=>{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){const kernels=getKernelsForBackend(backendName);kernels.forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){const registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{const backend3=registryFactoryEntry.factory();if(backend3&&!(backend3 instanceof KernelBackend)&&typeof backend3.then=="function"){const promiseId=++this.pendingBackendInitId,success=backend3.then(backendInstance=>promiseId<this.pendingBackendInitId?!1:(this.registry[backendName]=backendInstance,this.pendingBackendInit=null,!0)).catch(err=>(promiseId<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message)),!1));return this.pendingBackendInit=success,{success,asyncInit:!0}}else return this.registry[backendName]=backend3,{success:!0,asyncInit:!1}}catch(err){return console.warn(`Initialization of backend ${backendName} failed`),console.warn(err.stack||err.message),{success:!1,asyncInit:!1}}}removeBackend(backendName){if(!(backendName in this.registryFactory))throw new Error(`${backendName} backend not found in registry`);this.backendName===backendName&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,backendName in this.registry&&(this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName]),delete this.registryFactory[backendName],this.backendName===backendName&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,b)=>this.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){const sortedBackends=this.getSortedBackends();for(let i=0;i<sortedBackends.length;i++){const backendName=sortedBackends[i],{success,asyncInit}=this.initializeBackend(backendName);if(asyncInit||success)return{name:backendName,asyncInit}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(backend3,dataId){const info=this.state.tensorInfo.get(dataId),srcBackend=info.backend,values=this.readSync(dataId);srcBackend.disposeData(dataId),info.backend=backend3,backend3.move(dataId,values,info.shape,info.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(nameOrFn,fn){let name=null;if(fn==null){if(typeof nameOrFn!="function")throw new Error("Please provide a function to tidy()");fn=nameOrFn}else{if(typeof nameOrFn!="string"&&!(nameOrFn instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof fn!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");name=nameOrFn}let result;return this.scopedRun(()=>this.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{const res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){const y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{const dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){const forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){const numDataIdsAfter=this.backend.numDataIds();let numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});const numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[];const isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");const startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;const kernel=getKernel(kernelName,this.backendName);let out;if(kernel!=null)kernelFunc3=()=>{const numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});const outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);const outTensors=outInfos.map(({dataId,shape,dtype})=>this.makeTensorFromDataId(dataId,shape,dtype));if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);const outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{const saveFunc=tensors=>{if(!isTapeOn)return;saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)))};kernelFunc3=()=>{const numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));const outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){const saved=tensors.map(tensor168=>this.keep(this.clone(tensor168)));return saved}getTensorsForGradient(kernelName,inputs,outputs){const gradConfig=getGradient(kernelName);if(gradConfig!=null){const inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[];let inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);const outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend3){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend3=backend3||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));const dataId=backend3.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend3),dtype==="string"){const info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend3){dtype=dtype||"float32";const t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend3),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));const v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend3){const refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend3||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;const info=this.state.tensorInfo.get(a.dataId),refCount=info.refCount;refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(const varName in this.state.registeredVariables){const v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){const info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;const startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(const kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){const tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){const output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){const scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){const tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i<this.state.activeScope.track.length;i++){const tensor168=this.state.activeScope.track[i];!tensor168.kept&&!tensorsToTrackInParentSet.has(tensor168.id)&&tensor168.dispose()}const oldScope=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],tensorsToTrackInParent.forEach(tensor168=>{!tensor168.kept&&tensor168.scopeId===oldScope.id&&this.track(tensor168)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);const y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");const filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{const accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);const grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(const tensor168 of node.saved)tensor168.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res;const inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{const gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){const info=this.state.tensorInfo.get(dataId);return info.backend.readSync(dataId)}read(dataId){const info=this.state.tensorInfo.get(dataId);return info.backend.read(dataId)}async time(query){const start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(const backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){const values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){const ns=getGlobalNamespace();if(ns._tfengine==null){const environment15=new Environment(ns);ns._tfengine=new Engine(environment15)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}const ENGINE=getOrMakeEngine();function add(a,b){const inputs={a,b};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.add(a,b);return save([a,b]),res},inputs,null,Add)}const device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){const a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0,4))}return!1}function isBrowser(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}const ENV2=env();ENV2.registerFlag("DEBUG",()=>!1,debugValue=>{debugValue&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];const shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);const subShape=shape.slice(1);for(let i=0;i<val.length;++i)deepAssertShapeConsistency(val[i],subShape,indices.concat(i))}function assertDtype(expectedDtype,actualDType,argName,functionName){if(expectedDtype==null)return;if(expectedDtype!=="numeric"&&expectedDtype!==actualDType||expectedDtype==="numeric"&&actualDType==="string")throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`)}function convertToTensor(x,argName,functionName,parseAsDtype="numeric"){if(x instanceof Tensor)return assertDtype(parseAsDtype,x.dtype,argName,functionName),x;let inferredDtype=inferDtype(x);if(inferredDtype!=="string"&&["bool","int32","float32"].indexOf(parseAsDtype)>=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){const type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}const inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);const skipTypedArray=!0,values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],skipTypedArray);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);const tensors=arg;return tensors.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName),parseAsDtype)}const OP_SCOPE_SUFFIX="__op";function op(f){const keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0];const fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;const f2=(...args)=>{ENGINE.startScope(opName);try{const result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real8,imag8){const $real=convertToTensor(real8,"real","complex"),$imag=convertToTensor(imag8,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);const forward=backend3=>backend3.complex($real,$imag),inputs={real:$real,imag:$imag};return ENGINE.runKernelFunc(forward,inputs,null,Complex)}const complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);const providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i<inferredShape.length;++i){const inferred=inferredShape[i],flatDimsDontMatch=i===inferredShape.length-1?inferred!==sizeFromShape(shape.slice(i)):!0;assert(inferredShape[i]===shape[i]||!flatDimsDontMatch,()=>`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor4(values,shape,dtype){const inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}const DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){const specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor168=>tensor168.name):Object.keys(tensors);for(let i=0;i<names.length;++i){const name=names[i],t=Array.isArray(tensors)?tensors[i].tensor:tensors[name];if(t.dtype!=="float32"&&t.dtype!=="int32"&&t.dtype!=="bool"&&t.dtype!=="string"&&t.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);const spec={name,shape:t.shape,dtype:t.dtype};if(t.dtype==="string"){const utf8bytes=new Promise(async resolve=>{const vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes);let offset=0;for(let i2=0;i2<vals.length;i2++){const val=vals[i2],bytesOfLength=new Uint8Array(new Uint32Array([val.length]).buffer);bytes.set(bytesOfLength,offset),offset+=NUM_BYTES_STRING_LENGTH,bytes.set(val,offset),offset+=val.length}resolve(bytes)});dataPromises.push(utf8bytes)}else dataPromises.push(t.data());group!=null&&(spec.group=group),specs.push(spec)}const tensorValues=await Promise.all(dataPromises);return{data:concatenateTypedArrays(tensorValues),specs}}function decodeWeights(buffer11,specs){const out={};let float16Decode,offset=0;for(const spec of specs){const name=spec.name,dtype=spec.dtype,shape=spec.shape,size=sizeFromShape(shape);let values;if("quantization"in spec){const quantization=spec.quantization;if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){if(!("min"in quantization&&"scale"in quantization))throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`)}else if(quantization.dtype==="float16"){if(dtype!=="float32")throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`)}else throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);const quantizationSizeFactor=DTYPE_VALUE_SIZE_MAP[quantization.dtype],byteBuffer=buffer11.slice(offset,offset+size*quantizationSizeFactor),quantizedArray=quantization.dtype==="uint8"?new Uint8Array(byteBuffer):new Uint16Array(byteBuffer);if(dtype==="float32")if(quantization.dtype==="uint8"||quantization.dtype==="uint16"){values=new Float32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){const v=quantizedArray[i];values[i]=v*quantization.scale+quantization.min}}else if(quantization.dtype==="float16")float16Decode===void 0&&(float16Decode=getFloat16Decoder()),values=float16Decode(quantizedArray);else throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);else if(dtype==="int32"){if(quantization.dtype!=="uint8"&&quantization.dtype!=="uint16")throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);values=new Int32Array(quantizedArray.length);for(let i=0;i<quantizedArray.length;i++){const v=quantizedArray[i];values[i]=Math.round(v*quantization.scale+quantization.min)}}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*quantizationSizeFactor}else if(dtype==="string"){const size2=sizeFromShape(spec.shape);values=[];for(let i=0;i<size2;i++){const byteLength=new Uint32Array(buffer11.slice(offset,offset+NUM_BYTES_STRING_LENGTH))[0];offset+=NUM_BYTES_STRING_LENGTH;const bytes=new Uint8Array(buffer11.slice(offset,offset+byteLength));values.push(bytes),offset+=byteLength}}else{const dtypeFactor=DTYPE_VALUE_SIZE_MAP[dtype],byteBuffer=buffer11.slice(offset,offset+size*dtypeFactor);if(dtype==="float32")values=new Float32Array(byteBuffer);else if(dtype==="int32")values=new Int32Array(byteBuffer);else if(dtype==="bool")values=new Uint8Array(byteBuffer);else if(dtype==="complex64"){values=new Float32Array(byteBuffer);const real8=new Float32Array(values.length/2),image3=new Float32Array(values.length/2);for(let i=0;i<real8.length;i++)real8[i]=values[i*2],image3[i]=values[i*2+1];const realTensor=tensor4(real8,shape,"float32"),imageTensor=tensor4(image3,shape,"float32");out[name]=complex(realTensor,imageTensor),realTensor.dispose(),imageTensor.dispose()}else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);offset+=size*dtypeFactor}dtype!=="complex64"&&(out[name]=tensor4(values,shape,dtype))}return out}function concatenateTypedArrays(xs){if(xs===null)throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);let totalByteLength=0;const normalizedXs=[];xs.forEach(x=>{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});const y=new Uint8Array(totalByteLength);let offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}const useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer11){if(useNodeBuffer)return Buffer.from(buffer11).toString("base64");const buf=new Uint8Array(buffer11);let s="";for(let i=0,l=buf.length;i<l;i++)s+=String.fromCharCode(buf[i]);return btoa(s)}function base64StringToArrayBuffer(str){if(useNodeBuffer){const buf=Buffer.from(str,"base64");return buf.buffer.slice(buf.byteOffset,buf.byteOffset+buf.byteLength)}const s=atob(str),buffer11=new Uint8Array(s.length);for(let i=0;i<s.length;++i)buffer11.set([s.charCodeAt(i)],i);return buffer11.buffer}function concatenateArrayBuffers(buffers){if(buffers.length===1)return buffers[0];let totalByteLength=0;buffers.forEach(buffer11=>{totalByteLength+=buffer11.byteLength});const temp=new Uint8Array(totalByteLength);let offset=0;return buffers.forEach(buffer11=>{temp.set(new Uint8Array(buffer11),offset),offset+=buffer11.byteLength}),temp.buffer}function basename(path){const SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);const items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){const convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)===0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){const exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){const offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){const mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{const buffer11=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer11);for(let index=0;index<quantizedArray.length;index++){const float16Bits=quantizedArray[index],float32Bits=mantisaTable[offsetTable[float16Bits>>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer11)}}class IORouterRegistry{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){const validHandlers=[],routers=handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters;return routers.forEach(router=>{const handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}}const registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){const db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}class BrowserIndexedDB{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{const openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{const db=openRequest.result;if(modelArtifacts==null){const modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),modelStore=modelTx.objectStore(MODEL_STORE_NAME),getRequest=modelStore.get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{const modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite");let infoStore=infoTx.objectStore(INFO_STORE_NAME);const putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo});let modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");const modelStore=modelTx.objectStore(MODEL_STORE_NAME),putModelRequest=modelStore.put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);const deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}}BrowserIndexedDB.URL_SCHEME="indexeddb://";const indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}class BrowserIndexedDBManager{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{const openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{const db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),store=tx.objectStore(INFO_STORE_NAME),getAllInfoRequest=store.getAll();getAllInfoRequest.onsuccess=()=>{const out={};for(const item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{const openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{const db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path);let modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{const deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");const modelStore=modelTx.objectStore(MODEL_STORE_NAME),deleteModelRequest=modelStore.delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}}const PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){const items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}class BrowserLocalStorage{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{const topology21=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{return this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology21),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData)),this.LS.setItem(this.keys.modelMetadata,JSON.stringify({format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata})),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){const info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");const out={},topology21=JSON.parse(this.LS.getItem(this.keys.topology));if(topology21==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology21;const weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;const metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){const metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,out.userDefinedMetadata=metadata.userDefinedMetadata}const weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}}BrowserLocalStorage.URL_SCHEME="localstorage://";const localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}class BrowserLocalStorageManager{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){const out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i<this.LS.length;++i){const key=this.LS.key(i);if(key.startsWith(prefix)&&key.endsWith(suffix)){const modelPath=getModelPathFromKey(key);out[modelPath]=JSON.parse(this.LS.getItem(key))}}return out}async removeModel(path){path=maybeStripScheme2(path);const keys=getModelKeys(path);if(this.LS.getItem(keys.info)==null)throw new Error(`Cannot find model at path '${path}'`);const info=JSON.parse(this.LS.getItem(keys.info));return this.LS.removeItem(keys.info),this.LS.removeItem(keys.topology),this.LS.removeItem(keys.weightSpecs),this.LS.removeItem(keys.weightData),info}}const URL_SCHEME_SUFFIX="://";class ModelStoreManagerRegistry{constructor(){this.managers={}}static getInstance(){return ModelStoreManagerRegistry.instance==null&&(ModelStoreManagerRegistry.instance=new ModelStoreManagerRegistry),ModelStoreManagerRegistry.instance}static registerManager(scheme,manager){assert(scheme!=null,()=>"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");const registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){const manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}}function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);const loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);const loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);const saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);const saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){const schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(const scheme of schemes){const schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(const path in schemeOut){const url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){const schemeAndPath=parseURL(url),manager=ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);return manager.removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){const deleteSource=!1;return cloneModelInternal(sourceURL,destURL,deleteSource)}async function moveModel(sourceURL,destURL){const deleteSource=!0;return cloneModelInternal(sourceURL,destURL,deleteSource)}class PlatformBrowser{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}}if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}const getNodeFetch={importFetch:()=>require_browser()};let systemFetch;class PlatformNode{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){const time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}}env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){const $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");const inputs={x:$x},attrs={dtype};return ENGINE.runKernelFunc(backend3=>backend3.cast($x,dtype),inputs,null,Cast,attrs)}const cast=op({cast_});function clone_(x){const $x=convertToTensor(x,"x","clone",null),forward=()=>ENGINE.makeTensorFromDataId($x.dataId,$x.shape,$x.dtype),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Identity)}const clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();const opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);const io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});const DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}class BrowserDownloads{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");const weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest},modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){const weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}}BrowserDownloads.URL_SCHEME="downloads://";class BrowserFiles{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){const jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{const jsonReader=new FileReader;jsonReader.onload=event=>{const modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});const weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}const weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{const weightFileReader=new FileReader;weightFileReader.onload=event2=>{const weightData=event2.target.result,index=paths.indexOf(path);perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1&&resolve({modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy,userDefinedMetadata:modelJSON.userDefinedMetadata})},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){const basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(const group of manifest)group.paths.forEach(path=>{const pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}}const browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0;const registerMonitor=promise=>(promise.then(value=>{const fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});const fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,responses=loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction),bufferPromises=responses.map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1,buffers=loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction);return buffers}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){const fetchWeights=fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}),loadWeights2=weightsLoaderFactory(fetchWeights);return loadWeights2(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{const groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{const rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){const weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}const groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{const fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});const buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={};let bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{const numBuffers=manifest[i].paths.length;let groupBytes=0;for(let i2=0;i2<numBuffers;i2++)groupBytes+=buffers[bufferIndexOffset+i2].byteLength;const groupBuffer=new ArrayBuffer(groupBytes),groupByteBuffer=new Uint8Array(groupBuffer);let groupBufferOffset=0;for(let i2=0;i2<numBuffers;i2++){const buffer11=new Uint8Array(buffers[bufferIndexOffset+i2]);groupByteBuffer.set(buffer11,groupBufferOffset),groupBufferOffset+=buffer11.byteLength}const weightsEntries=groupWeightsToFetch[i];weightsEntries.forEach(weightsEntry=>{const byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(const name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}const OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json";class HTTPRequest{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;const weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,userDefinedMetadata:modelArtifacts.userDefinedMetadata,weightsManifest};init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");const response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){const modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}const modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;if(weightsManifest!=null){const results=await this.loadWeights(weightsManifest);[weightSpecs,weightData]=results}const artifacts={modelTopology,weightSpecs,weightData,userDefinedMetadata,generatedBy,convertedBy,format},initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){const weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(const entry of weightsManifest)weightSpecs.push(...entry.weights);const fetchURLs=[],urlPromises=[];for(const weightsGroup of weightsManifest)for(const path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));const buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}}HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){const lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}const httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}class PassthroughLoader{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}}class PassthroughSaver{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}}function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){if(arguments.length===1){const isModelArtifacts=modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null;return isModelArtifacts?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts}))}else return console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig})}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}const math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function reshape_(x,shape){const $x=convertToTensor(x,"x","reshape",null),inputs={x:$x},attrs={shape},forward=(backend3,save)=>(shape=inferFromImplicitShape(shape,$x.size),assert($x.size===sizeFromShape(shape),()=>"new shape and old shape must have the same number of elements."),save([$x]),backend3.reshape($x,shape));return ENGINE.runKernelFunc(forward,inputs,null,Reshape,attrs)}const reshape=op({reshape_});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{save([$a,$b]);const innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;assert($a.rank>=2&&$b.rank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`),assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);const outShapeOuterDims=batchDimA>batchDimB?outerDimsA:outerDimsB,outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),res3d=backend3.batchMatMul(a3D,b3D,transposeA,transposeB);return reshape(res3d,outShape)},inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernelFunc(forward,inputs,null,BatchMatMul,attrs)}const matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);const $indices=convertToTensor(indices,"indices","oneHot","int32"),outShape=[...$indices.shape,depth],forward=(backend3,save)=>(save([$indices]),reshape(backend3.oneHot(reshape($indices,[$indices.size]),depth,onValue,offValue),outShape)),inputs={indices:$indices},attrs={depth,onValue,offValue};return ENGINE.runKernelFunc(forward,inputs,null,OneHot,attrs)}const oneHot=op({oneHot_});function transpose_(x,perm){const $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();const inputs={x:$x},attrs={perm};return ENGINE.runKernelFunc(backend3=>backend3.transpose($x,perm),inputs,null,Transpose,attrs)}const transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){const $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);const oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}const confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");const inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}let fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){const HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState<HAVE_CURRENT_DATA_READY_STATE)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}const kernel=getKernel(FromPixels,ENGINE.backendName);if(kernel!=null){const inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}const[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height];let vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{const numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i<numPixels;i++)for(let channel=0;channel<numChannels;++channel)values[i*numChannels+channel]=vals[i*4+channel]}const outShape=[height,width,numChannels];return tensor3d(values,outShape,"int32")}async function toPixels(img,canvas){let $img=convertToTensor(img,"img","toPixels");if(!(img instanceof Tensor)){const originalImgTensor=$img;$img=cast(originalImgTensor,"int32"),originalImgTensor.dispose()}if($img.rank!==2&&$img.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);const[height,width]=$img.shape.slice(0,2),depth=$img.rank===2?1:$img.shape[2];if(depth>4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);const data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i<height*width;++i){const rgba=[0,0,0,255];for(let d=0;d<depth;d++){const value=data2[i*depth+d];if($img.dtype==="float32"){if(value<0||value>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}const j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;const ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}const fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor168,indices){if(tensor168.rank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensor168.rank}.`);if(indices.rank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indices.rank-1]>tensor168.rank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indices.rank-1]} vs. ${tensor168.rank}`);if(tensor168.size===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor168.shape}.`);const indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1];let nResult=1;for(let i=0;i<indicesShape.length-1;++i)nResult*=indicesShape[i];const inputShape=tensor168.shape,resultShape=indicesShape.slice();resultShape.pop();let sliceSize=1;for(let i=sliceRank;i<tensor168.rank;++i)sliceSize*=inputShape[i],resultShape.push(inputShape[i]);const strides=[...computeStrides(tensor168.shape).map(stride=>stride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}const scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){const sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank<batchDim)throw new Error(shapeError+` update.rank < ${batchDim}. `);if(shape.length<sliceDim+(updates.rank-batchDim))throw new Error(shapeError+` Output shape length < ${sliceDim+(updates.rank-batchDim)}`);if(updates.rank!==batchDim+shape.length-sliceDim)throw new Error(shapeError+` update.rank != ${batchDim+shape.length-sliceDim}`);for(let d=0;d<batchDim;++d)if(updates.shape[d]!==indices.shape[d])throw new Error(shapeError+` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);for(let d=0;d<updates.rank-batchDim;++d)if(updates.shape[d+batchDim]!==shape[d+sliceDim])throw new Error(shapeError+` updates.shape[${d+batchDim}] (${updates.shape[d+batchDim]}) != shape[${d+batchDim}] (${shape[d+batchDim]})`)}function validateInput(updates,indices,shape){if(indices.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);if(updates.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);if(indices.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);if(shape.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);if(shape.length===0){if(indices.size===0)throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);if(updates.size===0)throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`)}validateUpdateShape(shape,indices,updates)}function calculateShapes(updates,indices,shape){const indicesRank=indices.shape.length,sliceRank=indicesRank>1?indices.shape[indicesRank-1]:1,totalNd=shape.length;let sliceSize=1;for(let i=sliceRank;i<totalNd;++i)sliceSize*=shape[i];const safeSliceDim=sliceRank<1?1:sliceRank,numUpdates=sizeFromShape(indices.shape)/safeSliceDim,strides=[...computeStrides(shape.slice(0,sliceRank)),1],outputSize=sizeFromShape(shape);return{sliceRank,numUpdates,sliceSize,strides,outputSize}}const slice_util_exports={};__export2(slice_util_exports,{assertParamsValid:()=>assertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){const inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i<inputRank;++i)assert(begin[i]+size[i]<=input2.shape[i],()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){const axes=[];let axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){const size=[];for(let axis=0;axis<begin.length;axis++)size[axis]=Math.ceil((end[axis]-begin[axis])/strides[axis]);return size}function stridesWithElidedDims(strides,ellipsisInsertionIndex,numElidedAxes,inputShape){const newStrides=[...strides];for(let i=newStrides.length;i<inputShape.length;i++)newStrides.push(1);for(let i=0;i<numElidedAxes;i++)i===0?newStrides[ellipsisInsertionIndex]=1:(newStrides.splice(ellipsisInsertionIndex,0,1),newStrides.pop());return newStrides}function unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,normalizedAxis){return normalizedAxis<=ellipsisInsertionIndex?normalizedAxis:normalizedAxis-(numElidedAxes-1)}function getElidedAxes(numElidedAxes,ellipsisInsertionIndex){const elidedAxes=[];for(let i=0;i<numElidedAxes;i++)elidedAxes.push(ellipsisInsertionIndex+i);return elidedAxes}function getNormalizedAxes(inputShape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask){const inputRank=inputShape.length;let normalizedBegin=new Array(inputRank),normalizedEnd=new Array(inputRank),normalizedStrides=new Array(inputRank);if(ellipsisAxes.length&&numInterpolatedAxes>0){const fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis<inputRank;axis++)normalizedBegin[axis]=startForAxis(beginMask,begin,strides,inputShape,axis,ellipsisMask),normalizedEnd[axis]=stopForAxis(endMask,end,strides,inputShape,axis,ellipsisMask),normalizedStrides[axis]=stridesForAxis(strides,axis,ellipsisMask);return{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}}function startIndicesWithElidedDims(beginMask,ellipsisInsertionIndex,numElidedAxes,originalBegin,inputShape){const newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=0;else{const originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis);let originalValue=originalBegin[originalAxis];beginMask&1<<originalAxis&&(originalValue=0),newIndices[axis]=originalValue}return newIndices}function stopIndicesWithElidedDims(endMask,ellipsisInsertionIndex,numElidedAxes,originalEnd,inputShape){const newIndices=[...inputShape],elidedAxes=getElidedAxes(numElidedAxes,ellipsisInsertionIndex);for(let axis=0;axis<newIndices.length;axis++)if(elidedAxes.indexOf(axis)>-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{const originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis);let originalValue=originalEnd[originalAxis];endMask&1<<originalAxis&&(originalValue=Number.MAX_SAFE_INTEGER),newIndices[axis]=originalValue}for(let i=0;i<newIndices.length;i++){const axisSize=inputShape[i];newIndices[i]<0&&(newIndices[i]+=axisSize),newIndices[i]=clamp(0,newIndices[i],inputShape[i])}return newIndices}function stridesForAxis(strides,axis,ellipsisMask){let stride=strides[axis];return(ellipsisMask&1<<axis||stride==null)&&(stride=1),stride}function startForAxis(beginMask,startIndices,strides,inputShape,axis,ellipsisMask){let start=startIndices[axis];const stride=strides[axis]||1;(beginMask&1<<axis||ellipsisMask&1<<axis||start==null)&&(stride>0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);const axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis];const stride=strides[axis]||1;(endMask&1<<axis||ellipsisMask&1<<axis||stop==null)&&(stride>0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);const axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i<size.length;i++)if(size[i]>1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i<size.length;i++)if(begin[i]>0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i<begin.length-1;i++)flatOffset+=begin[i]*strides[i];return flatOffset}function parseSliceParams(x,begin,size){let begin_;const xRank=x.shape.length;typeof begin=="number"?begin_=[begin,...new Array(xRank-1).fill(0)]:begin.length<xRank?begin_=begin.concat(new Array(xRank-begin.length).fill(0)):begin_=begin.slice(),begin_.forEach(d=>{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.length<xRank?size_=size.concat(new Array(xRank-size.length).fill(-1)):size_=size,size_=size_.map((d,i)=>d>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}const serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});class Serializable{getClassName(){return this.constructor.className}static fromConfig(cls,config2){return new cls(config2)}}class SerializationMap{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}}function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}const test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});const TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){const aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){const actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}const actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
Actual:   ${actualFlat}.
Expected: ${expectedFlat}.`);for(let i=0;i<expectedFlat.length;++i){const a=actualFlat[i],e=expectedFlat[i];if(!predicate(a,e))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
Actual:   ${actualFlat}.
Expected: ${expectedFlat}.`)}}function expectPromiseToFail(fn,done){fn().then(()=>done.fail(),()=>done())}function expectArraysEqual(actual,expected){const exp13=typeof expected=="string"||typeof expected=="number"||typeof expected=="boolean"?[expected]:expected;return isString(actual)||isString(actual[0])||isString(expected)||isString(expected[0])?expectArraysPredicate(actual,exp13,(a,b)=>a==b):expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,0))}function expectNumbersClose(a,e,epsilon3){if(epsilon3==null&&(epsilon3=testEpsilon()),!areClose(a,e,epsilon3))throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`)}function areClose(a,e,epsilon3){return!isFinite(a)&&!isFinite(e)?!0:!(isNaN(a)||isNaN(e)||Math.abs(a-e)>epsilon3)}function expectValuesInRange(actual,low,high){for(let i=0;i<actual.length;i++)if(actual[i]<low||actual[i]>high)throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`)}function expectArrayBuffersEqual(actual,expected){expect(new Float32Array(actual)).toEqual(new Float32Array(expected))}const version="2.7.0";function enableProdMode(){env().set("PROD",!0)}function enableDebugMode(){env().set("DEBUG",!0)}function disableDeprecationWarnings(){env().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function deprecationWarn(msg){env().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(msg+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}setDeprecationWarningFn(deprecationWarn);function disposeVariables(){ENGINE.disposeVariables()}function engine15(){return ENGINE}function memory(){return ENGINE.memory()}function profile(f){return ENGINE.profile(f)}function tidy(nameOrFn,fn){return ENGINE.tidy(nameOrFn,fn)}function dispose(container2){const tensors=getTensorsInContainer(container2);tensors.forEach(tensor168=>tensor168.dispose())}function keep(result){return ENGINE.keep(result)}function time(f){return ENGINE.time(f)}function setBackend(backendName){return ENGINE.setBackend(backendName)}function ready(){return ENGINE.ready()}function getBackend(){return ENGINE.backendName}function removeBackend(name){ENGINE.removeBackend(name)}function findBackend(name){return ENGINE.findBackend(name)}function findBackendFactory(name){return ENGINE.findBackendFactory(name)}function registerBackend(name,factory,priority=1){return ENGINE.registerBackend(name,factory,priority)}function backend2(){return ENGINE.backend}function setPlatform(platformName,platform){env().setPlatform(platformName,platform)}function add_(a,b){let $a=convertToTensor(a,"a","add"),$b=convertToTensor(b,"b","add");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{const res=backend3.add($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Add)}const add2=op({add_});function floorDiv_(a,b){let $a=convertToTensor(a,"a","floorDiv"),$b=convertToTensor(b,"b","floorDiv");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{const res=backend3.floorDiv($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,FloorDiv)}const floorDiv=op({floorDiv_});function div_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");if([$a,$b]=makeTypesMatch($a,$b),$a.dtype==="int32"&&$b.dtype==="int32")return floorDiv($a,$b);const forward=(backend3,save)=>{const res=backend3.realDivide($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,Div,attrs)}const div=op({div_});function mul_(a,b){let $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{const res=backend3.multiply($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Multiply)}const mul=op({mul_});function abs_(x){const $x=convertToTensor(x,"x","abs"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),$x.dtype==="complex64"?backend3.complexAbs($x):backend3.abs($x)),inputs,null,Abs)}const abs=op({abs_});function acos_(x){const $x=convertToTensor(x,"x","acos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.acos($x);return save([$x]),res},inputs,null,Acos)}const acos=op({acos_});function acosh_(x){const $x=convertToTensor(x,"x","acosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.acosh($x);return save([$x]),res},inputs,null,Acosh)}const acosh=op({acosh_});function addN_(tensors){assert(Array.isArray(tensors),()=>"The argument passed to tf.addN() must be a list of tensors"),assert(tensors.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);const $tensors=tensors.map((t,i)=>convertToTensor(t,`tensors${i}`,"addN")),firstTensor=$tensors[0];$tensors.forEach(t=>{if(t.dtype!==firstTensor.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),$tensors.forEach(t=>{if(!arraysEqual(t.shape,firstTensor.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});const forward=(backend3,save)=>{const res=backend3.addN($tensors);return save($tensors),res},inputs=$tensors;return ENGINE.runKernelFunc(forward,inputs,null,AddN)}const addN=op({addN_});function axesAreInnerMostDims(axes,rank){for(let i=0;i<axes.length;++i)if(axes[axes.length-i-1]!==rank-1-i)return!1;return!0}function combineLocations(outputLoc,reduceLoc,axes){const rank=outputLoc.length+reduceLoc.length,loc=[];let outIdx=0,reduceIdx=0;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1?loc.push(outputLoc[outIdx++]):loc.push(reduceLoc[reduceIdx++]);return loc}function computeOutAndReduceShapes(aShape,axes){const outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)axes.indexOf(dim)===-1&&outShape.push(aShape[dim]);const reduceShape=axes.map(dim=>aShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){const reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;const result=[];for(let i=0;i<rank;++i)axes.indexOf(i)===-1&&result.push(i);return axes.forEach(axis=>result.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){const res=[];for(let i=rank-numAxes;i<rank;++i)res.push(i);return res}function all_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","all","bool");const forward=backend3=>{const origAxes=parseAxisParam(axis,$x.shape);let axes=origAxes;const permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));const res=backend3.all($x,axes);if(keepDims){const newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,All,attrs)}const all=op({all_});function any_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","any","bool");const forward=backend3=>{const origAxes=parseAxisParam(axis,$x.shape);let axes=origAxes;const permutedAxes=getAxesPermutation(axes,$x.rank);permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));const res=backend3.any($x,axes);if(keepDims){const newShape=expandShapeToKeepDim(res.shape,origAxes);return reshape(res,newShape)}return res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Any,attrs)}const any=op({any_});function argMax_(x,axis=0){let $x=convertToTensor(x,"x","argMax");const forward=(backend3,save)=>{save([$x]);let axes=parseAxisParam(axis,$x.shape);const permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMax($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMax,attrs)}const argMax=op({argMax_});function argMin_(x,axis=0){let $x=convertToTensor(x,"x","argMin");const forward=(backend3,save)=>{save([$x]),axis==null&&(axis=0);let axes=parseAxisParam(axis,$x.shape);const permutedAxes=getAxesPermutation(axes,$x.rank);return permutedAxes!=null&&($x=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank)),backend3.argMin($x,axes[0])},inputs={x:$x},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,ArgMin,attrs)}const argMin=op({argMin_});function asin_(x){const $x=convertToTensor(x,"x","asin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.asin($x);return save([$x]),res},inputs,null,Asin)}const asin=op({asin_});function asinh_(x){const $x=convertToTensor(x,"x","asinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.asinh($x);return save([$x]),res},inputs,null,Asinh)}const asinh=op({asinh_});function atan_(x){const $x=convertToTensor(x,"x","atan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.atan($x);return save([$x]),res},inputs,null,Atan)}const atan=op({atan_});function atan2_(a,b){let $a=convertToTensor(a,"a","atan2"),$b=convertToTensor(b,"b","atan2");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{const res=backend3.atan2($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Atan2)}const atan2=op({atan2_});function atanh_(x){const $x=convertToTensor(x,"x","atanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.atanh($x);return save([$x]),res},inputs,null,Atanh)}const atanh=op({atanh_});function computeDilation2DInfo(inputShape,filterShape,strides,pad11,dataFormat="NHWC",dilations){const inputChannels=inputShape[3],$filterShape=[...filterShape,inputChannels],$dataFormat=convertConv2DDataFormat(dataFormat);return computeConv2DInfo(inputShape,$filterShape,strides,dilations,pad11,null,null,$dataFormat)}function computePool2DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="channelsLast"){const[filterHeight,filterWidth]=parseTupleParam(filterSize);let filterShape;if(dataFormat==="channelsLast")filterShape=[filterHeight,filterWidth,inShape[3],inShape[3]];else if(dataFormat==="channelsFirst")filterShape=[filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,!1,dataFormat)}function computePool3DInfo(inShape,filterSize,strides,dilations,pad11,roundingMode,dataFormat="NDHWC"){const[filterDepth,filterHeight,filterWidth]=parse3TupleParam(filterSize);let filterShape,$dataFormat;if(dataFormat==="NDHWC")$dataFormat="channelsLast",filterShape=[filterDepth,filterHeight,filterWidth,inShape[4],inShape[4]];else if(dataFormat==="NCDHW")$dataFormat="channelsFirst",filterShape=[filterDepth,filterHeight,filterWidth,inShape[1],inShape[1]];else throw new Error(`Unknown dataFormat ${dataFormat}`);return computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,!1,$dataFormat,roundingMode)}function computeConv2DInfo(inShape,filterShape,strides,dilations,pad11,roundingMode,depthwise=!1,dataFormat="channelsLast"){let[batchSize,inHeight,inWidth,inChannels]=[-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);const[filterHeight,filterWidth,,filterChannels]=filterShape,[strideHeight,strideWidth]=parseTupleParam(strides),[dilationHeight,dilationWidth]=parseTupleParam(dilations),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outHeight,outWidth}=getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,effectiveFilterHeight,effectiveFilterWidth,roundingMode,dataFormat),outChannels=depthwise?filterChannels*inChannels:filterChannels;let outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,effectiveFilterHeight,effectiveFilterWidth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeConv3DInfo(inShape,filterShape,strides,dilations,pad11,depthwise=!1,dataFormat="channelsLast",roundingMode){let[batchSize,inDepth,inHeight,inWidth,inChannels]=[-1,-1,-1,-1,-1];if(dataFormat==="channelsLast")[batchSize,inDepth,inHeight,inWidth,inChannels]=inShape;else if(dataFormat==="channelsFirst")[batchSize,inChannels,inDepth,inHeight,inWidth]=inShape;else throw new Error(`Unknown dataFormat ${dataFormat}`);const[filterDepth,filterHeight,filterWidth,,filterChannels]=filterShape,[strideDepth,strideHeight,strideWidth]=parse3TupleParam(strides),[dilationDepth,dilationHeight,dilationWidth]=parse3TupleParam(dilations),effectiveFilterDepth=getEffectiveFilterSize(filterDepth,dilationDepth),effectiveFilterHeight=getEffectiveFilterSize(filterHeight,dilationHeight),effectiveFilterWidth=getEffectiveFilterSize(filterWidth,dilationWidth),{padInfo,outDepth,outHeight,outWidth}=get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,roundingMode),outChannels=depthwise?filterChannels*inChannels:filterChannels;let outShape;return dataFormat==="channelsFirst"?outShape=[batchSize,outChannels,outDepth,outHeight,outWidth]:dataFormat==="channelsLast"&&(outShape=[batchSize,outDepth,outHeight,outWidth,outChannels]),{batchSize,dataFormat,inDepth,inHeight,inWidth,inChannels,outDepth,outHeight,outWidth,outChannels,padInfo,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,effectiveFilterDepth,effectiveFilterHeight,effectiveFilterWidth,dilationDepth,dilationHeight,dilationWidth,inShape,outShape,filterShape}}function computeOutputShape2D(inShape,fieldSize,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));const inputRows=inShape[0],inputCols=inShape[1],outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);const outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputRows,outputCols]}function computeOutputShape4D(inShape,fieldSize,outChannels,stride,zeroPad,roundingMode){zeroPad==null&&(zeroPad=computeDefaultPad(inShape,fieldSize,stride));const inputDepth=inShape[0],inputRows=inShape[1],inputCols=inShape[2],outputDepths=conditionalRound((inputDepth-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputDepths),()=>`The output # of depths (${outputDepths}) must be an integer. Change the stride and/or zero pad parameters`);const outputRows=conditionalRound((inputRows-fieldSize+2*zeroPad)/stride+1,roundingMode);assert(isInt(outputRows),()=>`The output # of rows (${outputRows}) must be an integer. Change the stride and/or zero pad parameters`);const outputCols=conditionalRound((inputCols-fieldSize+2*zeroPad)/stride+1,roundingMode);return assert(isInt(outputCols),()=>`The output # of columns (${outputCols}) must be an integer. Change the stride and/or zero pad parameters`),[outputDepths,outputRows,outputCols,outChannels]}function computeDefaultPad(inputShape,fieldSize,stride,dilation=1){const effectiveFieldSize=getEffectiveFilterSize(fieldSize,dilation);return Math.floor((inputShape[0]*(stride-1)-stride+effectiveFieldSize)/2)}function parseTupleParam(param){return typeof param=="number"?[param,param,param]:param.length===2?[param[0],param[1],1]:param}function parse3TupleParam(param){return typeof param=="number"?[param,param,param]:param}function getEffectiveFilterSize(filterSize,dilation){return dilation<=1?filterSize:filterSize+(filterSize-1)*(dilation-1)}function getPadAndOutInfo(pad11,inHeight,inWidth,strideHeight,strideWidth,filterHeight,filterWidth,roundingMode,dataFormat){let padInfo,outHeight,outWidth;if(typeof pad11=="number"){const padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,type:padType};const outShape=computeOutputShape2D([inHeight,inWidth],filterHeight,strideHeight,pad11,roundingMode);outHeight=outShape[0],outWidth=outShape[1]}else if(pad11==="same"){outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);const padAlongHeight=Math.max(0,(outHeight-1)*strideHeight+filterHeight-inHeight),padAlongWidth=Math.max(0,(outWidth-1)*strideWidth+filterWidth-inWidth),top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,type:"VALID"},outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else if(typeof pad11=="object"){const top=dataFormat==="channelsLast"?pad11[1][0]:pad11[2][0],bottom=dataFormat==="channelsLast"?pad11[1][1]:pad11[2][1],left=dataFormat==="channelsLast"?pad11[2][0]:pad11[3][0],right=dataFormat==="channelsLast"?pad11[2][1]:pad11[3][1],padType=top===0&&bottom===0&&left===0&&right===0?"VALID":"EXPLICIT";padInfo={top,bottom,left,right,type:padType},outHeight=conditionalRound((inHeight-filterHeight+top+bottom)/strideHeight+1,roundingMode),outWidth=conditionalRound((inWidth-filterWidth+left+right)/strideWidth+1,roundingMode)}else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outHeight,outWidth}}function get3DPadAndOutInfo(pad11,inDepth,inHeight,inWidth,strideDepth,strideHeight,strideWidth,filterDepth,filterHeight,filterWidth,roundingMode){let padInfo,outDepth,outHeight,outWidth;if(typeof pad11=="number"){const padType=pad11===0?"VALID":"NUMBER";padInfo={top:pad11,bottom:pad11,left:pad11,right:pad11,front:pad11,back:pad11,type:padType};const outShape=computeOutputShape4D([inDepth,inHeight,inWidth,1],filterDepth,1,strideDepth,pad11,roundingMode);outDepth=outShape[0],outHeight=outShape[1],outWidth=outShape[2]}else if(pad11==="same"){outDepth=Math.ceil(inDepth/strideDepth),outHeight=Math.ceil(inHeight/strideHeight),outWidth=Math.ceil(inWidth/strideWidth);const padAlongDepth=(outDepth-1)*strideDepth+filterDepth-inDepth,padAlongHeight=(outHeight-1)*strideHeight+filterHeight-inHeight,padAlongWidth=(outWidth-1)*strideWidth+filterWidth-inWidth,front=Math.floor(padAlongDepth/2),back=padAlongDepth-front,top=Math.floor(padAlongHeight/2),bottom=padAlongHeight-top,left=Math.floor(padAlongWidth/2),right=padAlongWidth-left;padInfo={top,bottom,left,right,front,back,type:"SAME"}}else if(pad11==="valid")padInfo={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},outDepth=Math.ceil((inDepth-filterDepth+1)/strideDepth),outHeight=Math.ceil((inHeight-filterHeight+1)/strideHeight),outWidth=Math.ceil((inWidth-filterWidth+1)/strideWidth);else throw Error(`Unknown padding parameter: ${pad11}`);return{padInfo,outDepth,outHeight,outWidth}}function conditionalRound(value,roundingMode){if(!roundingMode)return value;switch(roundingMode){case"round":return Math.round(value);case"ceil":return Math.ceil(value);case"floor":return Math.floor(value);default:throw new Error(`Unknown roundingMode ${roundingMode}`)}}function tupleValuesAreOne(param){const[dimA,dimB,dimC]=parseTupleParam(param);return dimA===1&&dimB===1&&dimC===1}function eitherStridesOrDilationsAreOne(strides,dilations){return tupleValuesAreOne(strides)||tupleValuesAreOne(dilations)}function convertConv2DDataFormat(dataFormat){if(dataFormat==="NHWC")return"channelsLast";if(dataFormat==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${dataFormat}`)}function avgPool_(x,filterSize,strides,pad11,dimRoundingMode){const $x=convertToTensor(x,"x","avgPool","float32"),dilations=1;assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=(backend3,save)=>{const convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode);return save([x4D]),convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?x4D.clone():backend3.avgPool(x4D,convInfo)},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode};let res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool,attrs);return res=cast(res,$x.dtype),reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const avgPool=op({avgPool_});function avgPool3d_(x,filterSize,strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");const $x=convertToTensor(x,"x","avgPool3d","float32");let x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);const convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat);return save([x5D]),backend3.avgPool3d(x5D,convInfo)},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations};let res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3D,attrs);return res=cast(res,x5D.dtype),reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}const avgPool3d=op({avgPool3d_});function assertParamsConsistent(shapes,axis){const rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis<rank,()=>`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);const firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r<rank;r++)assert(r===axis||shape[r]===firstShape[r],()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){const outputShape=shapes[0].slice();for(let i=1;i<shapes.length;i++)outputShape[axis]+=shapes[i][axis];return outputShape}function concat_(tensors,axis=0){assert(tensors.length>=1,()=>"Pass at least one tensor to concat");let $tensors=convertToTensorArray(tensors,"tensors","concat");$tensors[0].dtype==="complex64"&&$tensors.forEach(tensor168=>{if(tensor168.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${tensor168.dtype}. `)});const forward=(backend3,save)=>{const $axis=parseAxisParam(axis,$tensors[0].shape)[0],outShape=computeOutShape2($tensors.map(t=>t.shape),$axis);if(sizeFromShape(outShape)===0)return tensor4([],outShape);if($tensors=$tensors.filter(t=>t.size>0),$tensors.length===1)return $tensors[0];const shapes=$tensors.map(t=>t.shape);assertParamsConsistent(shapes,$axis);const res=backend3.concat($tensors,$axis);return save($tensors),res},inputs=$tensors,attr={axis};return ENGINE.runKernelFunc(forward,inputs,null,Concat,attr)}const concat=op({concat_});function sigmoid_(x){const $x=convertToTensor(x,"x","sigmoid"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.sigmoid($x);return save([res]),res},inputs,null,Sigmoid)}const sigmoid=op({sigmoid_});function slice_(x,begin,size){const $x=convertToTensor(x,"x","slice");if($x.rank===0)throw new Error("Slicing scalar is not possible");const forward=(backend3,save)=>{const[begin_,size_]=parseSliceParams($x,begin,size);return assertParamsValid($x,begin_,size_),save([$x]),backend3.slice($x,begin_,size_)},inputs={x:$x},attrs={begin,size};return ENGINE.runKernelFunc(forward,inputs,null,Slice,attrs)}const slice=op({slice_});function tanh_(x){const $x=convertToTensor(x,"x","tanh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const y=backend3.tanh($x);return save([y]),y},inputs,null,Tanh)}const tanh2=op({tanh_});function basicLSTMCell_(forgetBias,lstmKernel,lstmBias,data2,c,h){const $forgetBias=convertToTensor(forgetBias,"forgetBias","basicLSTMCell"),$lstmKernel=convertToTensor(lstmKernel,"lstmKernel","basicLSTMCell"),$lstmBias=convertToTensor(lstmBias,"lstmBias","basicLSTMCell"),$data=convertToTensor(data2,"data","basicLSTMCell"),$c=convertToTensor(c,"c","basicLSTMCell"),$h=convertToTensor(h,"h","basicLSTMCell"),combined=concat([$data,$h],1),weighted=matMul(combined,$lstmKernel),res=add2(weighted,$lstmBias),batchSize=res.shape[0],sliceCols=res.shape[1]/4,sliceSize=[batchSize,sliceCols],i=slice(res,[0,0],sliceSize),j=slice(res,[0,sliceCols],sliceSize),f=slice(res,[0,sliceCols*2],sliceSize),o=slice(res,[0,sliceCols*3],sliceSize),newC=add2(mul(sigmoid(i),tanh2(j)),mul($c,sigmoid(add2($forgetBias,f)))),newH=mul(tanh2(newC),sigmoid(o));return[newC,newH]}const basicLSTMCell=op({basicLSTMCell_});function batchToSpaceND_(x,blockShape,crops){const $x=convertToTensor(x,"x","batchToSpaceND"),prod5=blockShape.reduce((a,b)=>a*b);assert($x.rank>=1+blockShape.length,()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`),assert(crops.length===blockShape.length,()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`),assert($x.shape[0]%prod5===0,()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod5}`);const forward=backend3=>backend3.batchToSpaceND($x,blockShape,crops),inputs={x:$x},attrs={blockShape,crops};return ENGINE.runKernelFunc(forward,inputs,null,BatchToSpaceND,attrs)}const batchToSpaceND=op({batchToSpaceND_});function xAs4D(x){let x4D;return x.rank===0||x.rank===1?x4D=reshape(x,[1,1,1,x.size]):x.rank===2?x4D=reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x4D=x,x4D}function batchNorm_(x,mean7,variance,offset,scale2,varianceEpsilon){varianceEpsilon==null&&(varianceEpsilon=.001);const $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm");let $scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($mean.rank===$variance.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),assert($offset==null||$mean.rank===$offset.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),assert($scale==null||$mean.rank===$scale.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const x4D=xAs4D($x),forward=(backend3,save)=>(save([x4D,$mean,$variance,$scale]),backend3.batchNorm(x4D,as1DOr4D($mean),as1DOr4D($variance),as1DOr4D($offset),as1DOr4D($scale),varianceEpsilon)),inputs={x:x4D,scale:$scale,offset:$offset,mean:$mean,variance:$variance},attrs={varianceEpsilon},res=ENGINE.runKernelFunc(forward,inputs,null,FusedBatchNorm,attrs);return reshape(res,$x.shape)}function as1DOr4D(x){return x==null?null:x.rank===0?reshape(x,[x.size]):x.rank===1?x:x.rank===2?reshape(x,[1,1,x.shape[0],x.shape[1]]):x.rank===3?reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]):x}const batchNorm=op({batchNorm_});function batchNorm2d_(x,mean7,variance,offset,scale2,varianceEpsilon){const $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm");let $scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`),assert($mean.rank===2||$mean.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===2||$variance.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===2||$scale.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===2||$offset.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}const batchNorm2d=op({batchNorm2d_});function batchNorm3d_(x,mean7,variance,offset,scale2,varianceEpsilon){const $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm");let $scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`),assert($mean.rank===3||$mean.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===3||$variance.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===3||$scale.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===3||$offset.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}const batchNorm3d=op({batchNorm3d_});function batchNorm4d_(x,mean7,variance,offset,scale2,varianceEpsilon){const $x=convertToTensor(x,"x","batchNorm"),$mean=convertToTensor(mean7,"mean","batchNorm"),$variance=convertToTensor(variance,"variance","batchNorm");let $scale;scale2!=null&&($scale=convertToTensor(scale2,"scale","batchNorm"));let $offset;return offset!=null&&($offset=convertToTensor(offset,"offset","batchNorm")),assert($x.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`),assert($mean.rank===4||$mean.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`),assert($variance.rank===4||$variance.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`),$scale!=null&&assert($scale.rank===4||$scale.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`),$offset!=null&&assert($offset.rank===4||$offset.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`),batchNorm($x,$mean,$variance,$offset,$scale,varianceEpsilon)}const batchNorm4d=op({batchNorm4d_});function broadcastTo_(x,shape){let input2=convertToTensor(x,"broadcastTo","x");const xShape=input2.shape;if(shape.some(d=>!(d>0)||d%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);if(shape.length<input2.rank)throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);if(shape.length>input2.rank){const newShape=input2.shape.slice();for(;newShape.length<shape.length;)newShape.unshift(1);input2=reshape(input2,newShape)}const inputShape=input2.shape,reps=Array.from(shape);for(let i=shape.length-1;i>=0;i--)if(inputShape[i]===shape[i])reps[i]=1;else if(input2.shape[i]!==1)throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);const axes=reps.map((n,i)=>n>1?i:-1).filter(i=>i>=0);if(axes.length===0)return clone(input2);const forward=backend3=>backend3.tile(input2,reps),inputs={x:input2},attrs={shape,inputShape};return ENGINE.runKernelFunc(forward,inputs,null,BroadcastTo,attrs)}const broadcastTo=op({broadcastTo_});function ceil_(x){const $x=convertToTensor(x,"x","ceil"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.ceil($x),inputs,null,Ceil)}const ceil=op({ceil_});function clipByValue_(x,clipValueMin,clipValueMax){const $x=convertToTensor(x,"x","clipByValue");assert(clipValueMin<=clipValueMax,()=>`Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);const inputs={x:$x},attrs={clipValueMin,clipValueMax};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.clip($x,clipValueMin,clipValueMax);return save([$x]),res},inputs,null,ClipByValue,attrs)}const clipByValue=op({clipByValue_});function concat1d_(tensors){return concat(tensors,0)}const concat1d=op({concat1d_});function concat2d_(tensors,axis){return concat(tensors,axis)}const concat2d=op({concat2d_});function concat3d_(tensors,axis){return concat(tensors,axis)}const concat3d=op({concat3d_});function concat4d_(tensors,axis){return concat(tensors,axis)}const concat4d=op({concat4d_});function conv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){const $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d");let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1];assert(inDepth===$filter.shape[2],()=>`Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const forward=(backend3,save)=>{const $dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2d(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2D,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const conv2d=op({conv2d_});function conv1d_(x,filter,stride,pad11,dataFormat="NWC",dilation=1,dimRoundingMode){const $x=convertToTensor(x,"x","conv1d"),$filter=convertToTensor(filter,"filter","conv1d");let x3D=$x,reshapedTo3D=!1;$x.rank===2&&(reshapedTo3D=!0,x3D=reshape($x,[1,$x.shape[0],$x.shape[1]])),assert(x3D.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`),assert($filter.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x3D.shape[2]===$filter.shape[1],()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`),assert(eitherStridesOrDilationsAreOne(stride,dilation),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`),assert(dataFormat==="NWC",()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);const filter4D=reshape($filter,[1,$filter.shape[0],$filter.shape[1],$filter.shape[2]]),input4D=reshape(x3D,[x3D.shape[0],1,x3D.shape[1],x3D.shape[2]]),strides=[1,stride],dilations=[1,dilation],conv2dDataFormat="NHWC",res=conv2d(input4D,filter4D,strides,pad11,conv2dDataFormat,dilations,dimRoundingMode);return reshapedTo3D?reshape(res,[res.shape[2],res.shape[3]]):reshape(res,[res.shape[0],res.shape[2],res.shape[3]])}const conv1d=op({conv1d_});function conv2DBackpropInput_(xShape,dy,filter,strides,pad11,dataFormat="NHWC",dimRoundingMode){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape4D=xShape,dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]),xShape4D=[1,xShape[0],xShape[1],xShape[2]]),assert(xShape4D.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`),assert(filter.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);const inDepth=dataFormat==="NHWC"?xShape4D[3]:xShape4D[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filter.shape[2],()=>`Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`),assert(outDepth===filter.shape[3],()=>`Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=(backend3,save)=>{const dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(xShape4D,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),res2=backend3.conv2dDerInput(dy4D,filter,convInfo);return save([dy4D,filter]),res2},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,inputShape:xShape4D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const conv2DBackpropInput=op({conv2DBackpropInput_});function conv2dTranspose_(x,filter,outputShape,strides,pad11,dimRoundingMode){const $x=convertToTensor(x,"x","conv2dTranspose"),$filter=convertToTensor(filter,"filter","conv2dTranspose");return conv2DBackpropInput(outputShape,$x,$filter,strides,pad11,"NHWC",dimRoundingMode)}const conv2dTranspose=op({conv2dTranspose_});function conv3d_(x,filter,strides,pad11,dataFormat="NDHWC",dilations=[1,1,1]){const $x=convertToTensor(x,"x","conv3d"),$filter=convertToTensor(filter,"filter","conv3d");let x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`),assert($filter.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`),assert(x5D.shape[4]===$filter.shape[3],()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NDHWC",()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);const forward=(backend3,save)=>{const convInfo=computeConv3DInfo(x5D.shape,$filter.shape,strides,dilations,pad11),res2=backend3.conv3d(x5D,$filter,convInfo);return save([x5D,$filter]),res2},inputs={x:x5D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}const conv3d=op({conv3d_});function conv3DBackpropInput_(xShape,dy,filter,strides,pad11){assert(xShape.length===dy.rank,()=>`Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);let xShape5D=xShape,dy5D=dy,reshapedTo5D=!1;dy.rank===4&&(reshapedTo5D=!0,dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]]),xShape5D=[1,xShape[0],xShape[1],xShape[2],xShape[3]]);const inDepth=xShape5D[4],outDepth=dy5D.shape[4];assert(xShape5D.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`),assert(filter.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`),assert(inDepth===filter.shape[3],()=>`Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`),assert(outDepth===filter.shape[4],()=>`Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);const forward=backend3=>{const dilations=1,convInfo=computeConv3DInfo(xShape5D,filter.shape,strides,dilations,pad11);return backend3.conv3dDerInput(dy5D,filter,convInfo)},inputs={dy:dy5D,filter},attrs={pad:pad11,strides,inputShape:xShape5D},res=ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropInputV2,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}const conv3DBackpropInput=op({conv3DBackpropInput_});function conv3dTranspose_(x,filter,outputShape,strides,pad11){const $x=convertToTensor(x,"x","conv3dTranspose"),$filter=convertToTensor(filter,"filter","conv3dTranspose");return conv3DBackpropInput(outputShape,$x,$filter,strides,pad11)}const conv3dTranspose=op({conv3dTranspose_});function cos_(x){const $x=convertToTensor(x,"x","cos"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.cos($x);return save([$x]),res},inputs,null,Cos)}const cos=op({cos_});function cosh_(x){const $x=convertToTensor(x,"x","cosh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.cosh($x);return save([$x]),res},inputs,null,Cosh)}const cosh=op({cosh_});function cumsum_(x,axis=0,exclusive=!1,reverse12=!1){const $x=convertToTensor(x,"x","cumsum"),forward=(backend3,save)=>{const permutation=getAxesPermutation([axis],$x.rank);let permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation));const permutedAxis=getInnerMostAxes(1,$x.rank)[0];let value=backend3.cumsum(permutedX,permutedAxis,exclusive,reverse12);if(save([$x]),permutation!=null){const reversePermutation=getUndoAxesPermutation(permutation);value=transpose(value,reversePermutation)}return value},inputs={x:$x},attrs={axis,exclusive,reverse:reverse12};return ENGINE.runKernelFunc(forward,inputs,null,Cumsum,attrs)}const cumsum=op({cumsum_});function depthToSpace_(x,blockSize,dataFormat="NHWC"){const $x=convertToTensor(x,"x","depthToSpace"),inputHeight=dataFormat==="NHWC"?$x.shape[1]:$x.shape[2],inputWidth=dataFormat==="NHWC"?$x.shape[2]:$x.shape[3],inputDepth=dataFormat==="NHWC"?$x.shape[3]:$x.shape[1];assert(inputHeight*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
    ${$x.shape}`),assert(inputWidth*blockSize>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${inputWidth} and ${blockSize} for depthToSpace with input shape
        ${$x.shape}`),assert(inputDepth%(blockSize*blockSize)===0,()=>`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);const forward=backend3=>backend3.depthToSpace($x,blockSize,dataFormat),inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernelFunc(forward,inputs,null,DepthToSpace,attrs)}const depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){const $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d");let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=(backend3,save)=>{dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0),res2=backend3.depthwiseConv2D(x4D,$filter,convInfo);return save([x4D,$filter]),res2},inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNative,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){const $x=convertToTensor(x,"x","diag"),forward=backend3=>{const flat=reshape($x,[$x.size]),result=backend3.diag(flat),outShape=[...x.shape,...x.shape];return reshape(result,outShape)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Diag)}const diag=op({diag_});function dilation2d_(x,filter,strides,pad11,dilations=[1,1],dataFormat="NHWC"){const $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);const inputs={x:x4D,filter:$filter},attrs={strides,pad:pad11,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){const inRank=inShape.length,dims=[];for(let i=0;i<inRank;i++){const dim=inRank-1-i,a=inShape[dim]||1,b=outShape[outShape.length-1-i]||1;b>1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){const result=[];for(let i=0;i<outShape.length;i++){const inDim=inShape[inShape.length-i-1],outAxis=outShape.length-i-1,outDim=outShape[outAxis];(inDim==null||inDim===1&&outDim>1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){const result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i<l;i++){let a=shapeA[shapeA.length-i-1];a==null&&(a=1);let b=shapeB[shapeB.length-i-1];if(b==null&&(b=1),a===1)result.unshift(b);else if(b===1)result.unshift(a);else if(a!==b){const errMsg=`Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;throw Error(errMsg)}else result.unshift(a)}return result}function equal_(a,b){let $a=convertToTensor(a,"a","equal"),$b=convertToTensor(b,"b","equal");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=backend3=>backend3.equal($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Equal)}const equal=op({equal_});function where_(condition,a,b){const $a=convertToTensor(a,"a","where"),$b=convertToTensor(b,"b","where"),$condition=convertToTensor(condition,"condition","where","bool"),broadcastShape=assertAndGetBroadcastShape($a.shape,$b.shape),$broadcastedA=broadcastTo($a,broadcastShape),$broadcastedB=broadcastTo($b,broadcastShape);$condition.rank===1&&assert($condition.shape[0]===$a.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");const forward=(backend3,save)=>{const res=backend3.select($condition,$broadcastedA,$broadcastedB);return save([$condition]),res},inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernelFunc(forward,inputs,null,SelectV2)}const where=op({where_});function zerosLike_(x){const $x=convertToTensor(x,"x","zerosLike"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.zerosLike($x),inputs,null,ZerosLike)}const zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);const divResult=div($a,$b),zeros10=zerosLike(divResult),bEqualsZero=equal($b,zeros10);return where(bEqualsZero,zeros10,divResult)}const divNoNan=op({divNoNan_});function dot_(t1,t2){const $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);const t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){const t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){const t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){const t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{const t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul($t1,t22D);return t1t2}}const dot=op({dot_});function elu_(x){const $x=convertToTensor(x,"x","elu"),forward=(backend3,save)=>{const y=backend3.elu($x);return save([y]),y},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Elu)}const elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));const inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.erf($x);return save([$x]),res},inputs,null,Erf)}const erf=op({erf_});function exp_(x){const $x=convertToTensor(x,"x","exp"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.exp($x);return save([res]),res},inputs,null,Exp)}const exp=op({exp_});function expandDims_(x,axis=0){const parseAs=null,$x=convertToTensor(x,"x","expandDims",parseAs);assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");const newShape=$x.shape.slice();return axis<0&&(assert(-($x.rank+1)<=axis,()=>`Axis must be in the interval [${-($x.rank+1)}, ${$x.rank}]`),axis=$x.rank+axis+1),newShape.splice(axis,0,1),reshape($x,newShape)}const expandDims=op({expandDims_});function expm1_(x){const $x=convertToTensor(x,"x","expm1"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.expm1($x);return save([$x]),res},inputs,null,Expm1)}const expm1=op({expm1_});function tile_(x,reps){const parseAs=null,$x=convertToTensor(x,"x","tile",parseAs);assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);const forward=(backend3,save)=>{const res=backend3.tile($x,reps);return save([$x]),res},inputsToSave=[$x],inputs={x:$x},attrs={reps};return ENGINE.runKernelFunc(forward,inputs,null,Tile,attrs,inputsToSave)}const tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);const buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i<n;++i)buff.set(1,i,i);const out=reshape(buff.toTensor(),[numRows,numColumns]);if(batchShape==null)return out;if(batchShape.length===1)return tile(expandDims(out,0),[batchShape[0],1,1]);if(batchShape.length===2)return tile(expandDims(expandDims(out,0),0),[batchShape[0],batchShape[1],1,1]);if(batchShape.length===3)return tile(expandDims(expandDims(expandDims(out,0),0),0),[batchShape[0],batchShape[1],batchShape[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`)}const eye=op({eye_});function fill(shape,value,dtype){const attrs={shape,value,dtype};return ENGINE.runKernelFunc(backend3=>backend3.fill(shape,value,dtype),{},null,Fill,attrs)}function floor_(x){const $x=convertToTensor(x,"x","floor"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.floor($x),inputs,null,Floor)}const floor=op({floor_}),segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});const PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(inSize){return inSize<=PARALLELIZE_THRESHOLD?inSize:nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)))}function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){const outShape=[],rank=aShape.length;for(let dim=0;dim<rank;dim++)dim!==axis?outShape.push(aShape[dim]):outShape.push(numSegments);return outShape}function collectGatherOpShapeInfo(x,indices,axis){const dimSize=x.shape[axis],outputShape=[];let batchSize=1,sliceSize=1;for(let i=0;i<axis;i++)outputShape.push(x.shape[i]),batchSize*=x.shape[i];for(let i=0;i<indices.rank;i++)outputShape.push(indices.shape[i]);for(let i=axis+1;i<x.rank;i++)outputShape.push(x.shape[i]),sliceSize*=x.shape[i];return{batchSize,sliceSize,dimSize,outputShape}}function gather_(x,indices,axis=0){const $x=convertToTensor(x,"x","gather"),$indices=convertToTensor(indices,"indices","gather","int32"),inputs={x:$x,indices:$indices},attrs={axis},forward=(backend3,save)=>{const parsedAxis=parseAxisParam(axis,$x.shape)[0],shapeInfo=collectGatherOpShapeInfo($x,$indices,parsedAxis),res=backend3.gather($x,reshape($indices,[$indices.size]),parsedAxis);return save([$x,$indices]),reshape(res,shapeInfo.outputShape)};return ENGINE.runKernelFunc(forward,inputs,null,GatherV2,attrs)}const gather=op({gather_});function greater_(a,b){let $a=convertToTensor(a,"a","greater"),$b=convertToTensor(b,"b","greater");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=backend3=>backend3.greater($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Greater)}const greater=op({greater_});function greaterEqual_(a,b){let $a=convertToTensor(a,"a","greaterEqual"),$b=convertToTensor(b,"b","greaterEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=(backend3,save)=>{const res=backend3.greaterEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,GreaterEqual)}const greaterEqual=op({greaterEqual_});function imag_(input2){const $input=convertToTensor(input2,"input","imag"),forward=backend3=>backend3.imag($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Imag)}const imag=op({imag_});function isFinite_(x){const $x=convertToTensor(x,"x","isFinite"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isFinite($x),inputs,null,IsFinite)}const isFinite2=op({isFinite_});function isInf_(x){const $x=convertToTensor(x,"x","isInf"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isInf($x),inputs,null,IsInf)}const isInf=op({isInf_});function isNaN_(x){const $x=convertToTensor(x,"x","isNaN"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.isNaN($x),inputs,null,IsNan)}const isNaN2=op({isNaN_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=(backend3,save)=>{const res=backend3.maximum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Maximum)}const maximum=op({maximum_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");const shape=[],inferredShape=[];return makeTensor(value,shape,inferredShape,dtype)}function leakyRelu_(x,alpha=.2){const $x=convertToTensor(x,"x","leakyRelu");return maximum(mul(scalar(alpha),$x),$x)}const leakyRelu=op({leakyRelu_});function less_(a,b){let $a=convertToTensor(a,"a","less"),$b=convertToTensor(b,"b","less");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=backend3=>backend3.less($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Less)}const less=op({less_});function lessEqual_(a,b){let $a=convertToTensor(a,"a","lessEqual"),$b=convertToTensor(b,"b","lessEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=(backend3,save)=>{const res=backend3.lessEqual($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,LessEqual)}const lessEqual=op({lessEqual_});function linspace(start,stop,num){if(num<=0)throw new Error("The number of values should be positive.");const attrs={start,stop,num};return ENGINE.runKernelFunc(backend3=>backend3.linspace(start,stop,num),{},null,LinSpace,attrs)}function localResponseNormalization_(x,depthRadius=5,bias=1,alpha=1,beta=.5){const $x=convertToTensor(x,"x","localResponseNormalization");assert($x.rank===4||$x.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));const forward=(backend3,save)=>{const y=backend3.localResponseNormalization4D(x4D,depthRadius,bias,alpha,beta);return save([x4D,y]),y},inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernelFunc(forward,inputs,null,LRN,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const localResponseNormalization=op({localResponseNormalization_});function log_(x){const $x=convertToTensor(x,"x","log"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.log($x);return save([$x]),res},inputs,null,Log)}const log=op({log_});function log1p_(x){const $x=convertToTensor(x,"x","log1p"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.log1p($x);return save([$x]),res},inputs,null,Log1p)}const log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{const $x=convertToTensor(x,"x","tf.grad",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{const{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");const $args=convertToTensorArray(args,"args","tf.grads",null),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{const{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");const{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");const res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");const specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(const varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}const specifiedNonTrainable=specifiedVarList?varList.filter(variable3=>!variable3.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable3=>variable3.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);const allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);const namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){const numNullGradients=grads2.filter(g=>g==null).length;if(numNullGradients>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
    the f you passed encloses all operations that lead from x to y.`)}function neg_(x){const $x=convertToTensor(x,"x","neg"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.neg($x),inputs,null,Negate)}const neg=op({neg_});function softplus_(x){const $x=convertToTensor(x,"x","softplus"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.softplus($x);return save([$x]),res},inputs,null,Softplus)}const softplus=op({softplus_});function logSigmoid_(x){const $x=convertToTensor(x,"x","logSigmoid"),customOp=customGrad(x2=>{const value=neg(softplus(neg(x2))),gradFunc=dy=>{const derX=mul(dy,sigmoid(neg(x2)));return derX};return{value,gradFunc}});return customOp($x)}const logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){const $x=convertToTensor(x,"x","max"),forward=(backend3,save)=>{const origAxes=parseAxisParam(axis,$x.shape);let axes=origAxes;const permutedAxes=getAxesPermutation(axes,$x.rank);let maxInput=$x;permutedAxes!=null&&(maxInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,maxInput.rank));const y=backend3.max(maxInput,axes);permutedAxes!=null&&maxInput.dispose();let res=y;if(keepDims){const expandedShape=expandShapeToKeepDim(res.shape,parseAxisParam(axis,$x.shape));res=reshape(res,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Max,attrs)}const max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{const res=backend3.subtract($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Sub)}const sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));const forward=(backend3,save)=>{save([$x]);const axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank);let reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.sum(permutedX,reductionAxes);if(keepDims){const newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Sum,attrs)}const sum2=op({sum_});function logSoftmax_(logits,axis=-1){const $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);const forward=(backend3,save)=>{const keepDims=!0,xMax=max(logits,axis,!0),shifted=sub(logits,xMax),value=sub(cast(shifted,"float32"),log(sum2(exp(shifted),axis,keepDims)));return save([value]),value},inputs={logits:$logits},attrs={axis};return ENGINE.runKernelFunc(forward,inputs,null,LogSoftmax,attrs)}const logSoftmax=op({logSoftmax_});function logSumExp_(x,axis=null,keepDims=!1){const $x=convertToTensor(x,"x","logSumExp"),axes=parseAxisParam(axis,$x.shape),xMax=max($x,axes,!0),a=sub($x,xMax),b=exp(a),c=sum2(b,axes),d=log(c),res=add2(reshape(xMax,d.shape),d);if(keepDims){const newShape=expandShapeToKeepDim(res.shape,axes);return reshape(res,newShape)}return res}const logSumExp=op({logSumExp_});function logicalAnd_(a,b){const $a=convertToTensor(a,"a","logicalAnd","bool"),$b=convertToTensor(b,"b","logicalAnd","bool");assertAndGetBroadcastShape($a.shape,$b.shape);const inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalAnd($a,$b),inputs,null,LogicalAnd)}const logicalAnd=op({logicalAnd_});function logicalNot_(x){const $x=convertToTensor(x,"x","logicalNot","bool"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.logicalNot($x),inputs,null,LogicalNot)}const logicalNot=op({logicalNot_});function logicalOr_(a,b){const $a=convertToTensor(a,"a","logicalOr","bool"),$b=convertToTensor(b,"b","logicalOr","bool");assertAndGetBroadcastShape($a.shape,$b.shape);const inputs={a:$a,b:$b};return ENGINE.runKernelFunc(backend3=>backend3.logicalOr($a,$b),inputs,null,LogicalOr)}const logicalOr=op({logicalOr_});function logicalXor_(a,b){const $a=convertToTensor(a,"a","logicalXor","bool"),$b=convertToTensor(b,"b","logicalXor","bool");return assertAndGetBroadcastShape($a.shape,$b.shape),logicalAnd(logicalOr(a,b),logicalNot(logicalAnd(a,b)))}const logicalXor=op({logicalXor_});function maxPool_(x,filterSize,strides,pad11,dimRoundingMode){const $x=convertToTensor(x,"x","maxPool"),dilations=1;let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=(backend3,save)=>{const convInfo=computePool2DInfo(x4D.shape,filterSize,strides,1,pad11,dimRoundingMode);let y;return convInfo.filterWidth===1&&convInfo.filterHeight===1&&arraysEqual(convInfo.inShape,convInfo.outShape)?y=x4D.clone():y=backend3.maxPool(x4D,convInfo),save([x4D,y]),y},inputs={x:x4D},attrs={filterSize,strides,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad11,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");const $x=convertToTensor(x,"x","maxPool3d");let x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=(backend3,save)=>{dilations==null&&(dilations=[1,1,1]);const convInfo=computePool3DInfo(x5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode,dataFormat),y=backend3.maxPool3d(x5D,convInfo);return save([x5D,y]),y},inputs={x:x5D},attrs={filterSize,strides,pad:pad11,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3D,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}const maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad11,includeBatchInIndex=!1){const $x=convertToTensor(x,"x","maxPoolWithArgmax"),inputs={x:$x},attrs={filterSize,strides,pad:pad11,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}const maxPoolWithArgmax=op({maxPoolWithArgmax_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){const real8=zeros(shape,"float32"),imag8=zeros(shape,"float32");return complex(real8,imag8)}const values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){const real8=ones2(shape,"float32"),imag8=zeros(shape,"float32");return complex(real8,imag8)}const values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function mean_(x,axis=null,keepDims=!1){const $x=convertToTensor(x,"x","mean"),axes=parseAxisParam(axis,$x.shape),shapes=computeOutAndReduceShapes($x.shape,axes),reduceShape=shapes[1],reduceSize=sizeFromShape(reduceShape),inputs={x:$x},attrs={axis,keepDims},forward=()=>{const reduceSizeScalar=scalar(reduceSize),xReduce=reduceSizeScalar.dtype===$x.dtype?$x:cast($x,reduceSizeScalar.dtype),res=div(xReduce,reduceSizeScalar);return sum2(res,axis,keepDims)},customOp=customGrad(x2=>{const value=ENGINE.runKernelFunc(forward,inputs,null,Mean,attrs),gradFunc=dy=>{const expandedDyShape=x2.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});const expandedDy=reshape(dy,expandedDyShape),derX=div(mul(expandedDy,ones2(x2.shape,"float32")),reduceSize);return derX};return{value,gradFunc}});return customOp($x)}const mean=op({mean_});function min_(x,axis=null,keepDims=!1){const $x=convertToTensor(x,"x","min"),forward=(backend3,save)=>{const origAxes=parseAxisParam(axis,$x.shape);let axes=origAxes;const permutedAxes=getAxesPermutation(axes,$x.rank);let minInput=$x;permutedAxes!=null&&(minInput=transpose($x,permutedAxes),axes=getInnerMostAxes(axes.length,$x.rank));const y=backend3.min(minInput,axes);permutedAxes!=null&&minInput.dispose();let res=y;if(keepDims){const expandedShape=expandShapeToKeepDim(res.shape,origAxes);res=reshape(y,expandedShape),y.dispose()}return save([$x,res]),res},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Min,attrs)}const min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=(backend3,save)=>{const res=backend3.minimum($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Minimum)}const minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);const $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);const shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);const attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}const mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);const forward=(backend3,save)=>{const res=backend3.mod($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,Mod)}const mod=op({mod_});function square_(x){const $x=convertToTensor(x,"x","square"),attrs={},inputsToSave=[$x],outputsToSave=[];return ENGINE.runKernelFunc((backend3,save)=>(save([$x]),backend3.square($x)),{x:$x},null,"Square",attrs,inputsToSave,outputsToSave)}const square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");const axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims);let keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));const devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}const moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){const $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell");let input2=$data;const newStates=[];for(let i=0;i<lstmCells.length;i++){const output=lstmCells[i](input2,$c[i],$h[i]);newStates.push(output[0]),newStates.push(output[1]),input2=output[1]}const newC=[],newH=[];for(let i=0;i<newStates.length;i+=2)newC.push(newStates[i]),newH.push(newStates[i+1]);return[newC,newH]}const multiRNNCell=op({multiRNNCell_});function multinomial_(logits,numSamples,seed,normalized=!1){const $logits=convertToTensor(logits,"logits","multinomial"),numOutcomes=$logits.size,origRank=$logits.rank;if(numOutcomes<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);if(origRank>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();const logits2D=origRank===1?reshape($logits,[1,-1]):$logits,res=ENGINE.runKernelFunc(backend3=>backend3.multinomial(logits2D,normalized,numSamples,seed),{logits2D});return origRank===1?reshape(res,[res.size]):res}const multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=backend3=>backend3.notEqual($a,$b),inputs={a:$a,b:$b};return ENGINE.runKernelFunc(forward,inputs,null,NotEqual)}const notEqual=op({notEqual_});function real_(input2){const $input=convertToTensor(input2,"input","real"),forward=backend3=>backend3.real($input),inputs={input:$input};return ENGINE.runKernelFunc(forward,inputs,null,Real)}const real=op({real_});function onesLike_(x){const $x=convertToTensor(x,"x","onesLike"),forward=(backend3,save)=>{if($x.dtype==="complex64"){const r=onesLike(real($x)),i=zerosLike(imag($x));return complex(r,i)}return backend3.onesLike($x)},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,OnesLike)}const onesLike=op({onesLike_});function outerProduct_(v1,v2){const $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);const v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}const outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){const $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");const forward=(backend3,save)=>(save([$x]),backend3.pad($x,paddings,constantValue)),attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,PadV2,attrs)}const pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}const pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}const pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}const pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}const pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){const $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]===0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);const forward=backend3=>backend3.spaceToBatchND($x,blockShape,paddings),inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernelFunc(forward,inputs,null,SpaceToBatchND,attrs)}const spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad11,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad11===0&&(pad11="valid");const $x=convertToTensor(input2,"x","maxPool");let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad11),dilation=[convInfo.dilationHeight,convInfo.dilationWidth];let basePadding;pad11==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];const isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad11:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),forwardOp=poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad),y=forwardOp(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){const padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){const dilatedFilterShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)),padExtraShape=dilatedFilterShape.map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}const pool=op({pool_});function pow_(base2,exp13){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp13,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);const inputs={a:$base,b:$exp},forward=(backend3,save)=>{const y=backend3.pow($base,$exp);return save([$base,$exp,y]),y};return ENGINE.runKernelFunc(forward,inputs,null,Pow)}const pow=op({pow_});function prelu_(x,alpha){const $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),forward=(backend3,save)=>{const res=backend3.prelu($x,$alpha);return save([$x,$alpha]),res},inputs={x:$x,alpha:$alpha};return ENGINE.runKernelFunc(forward,inputs,null,Prelu)}const prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));const forward=backend3=>{const axes=parseAxisParam(axis,$x.shape),permutation=getAxesPermutation(axes,$x.rank);let reductionAxes=axes,permutedX=$x;permutation!=null&&(permutedX=transpose($x,permutation),reductionAxes=getInnerMostAxes(reductionAxes.length,$x.rank));let value=backend3.prod(permutedX,reductionAxes);if(keepDims){const newShape=expandShapeToKeepDim(value.shape,axes);value=reshape(value,newShape)}return value},inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernelFunc(forward,inputs,null,Prod,attrs)}const prod=op({prod_});function rand_(shape,randFunction,dtype){const size=sizeFromShape(shape);let values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i<size;i++)values[i]=randFunction();return ENGINE.makeTensor(values,shape,dtype)}const rand=op({rand_}),seedrandom=__toModule2(require_seedrandom2());class MPRandGauss{constructor(mean7,stdDeviation,dtype,truncated,seed){this.mean=mean7,this.stdDev=stdDeviation,this.dtype=dtype,this.nextVal=NaN,this.truncated=truncated,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);const seedValue=seed||Math.random();this.random=seedrandom.alea(seedValue.toString())}nextValue(){if(!isNaN(this.nextVal)){const value=this.nextVal;return this.nextVal=NaN,value}let resultX,resultY,isValid=!1;for(;!isValid;){let v1,v2,s;do v1=2*this.random()-1,v2=2*this.random()-1,s=v1*v1+v2*v2;while(s>=1||s===0);const mul64=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul64,resultY=this.mean+this.stdDev*v2*mul64,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}}class RandGamma{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;const seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),u<v0||Math.log(u)<v1)break}return v=1/this.beta*this.d*v,this.alpha<1&&(v*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(v)}convertValue(value){return this.dtype==="float32"?value:Math.round(value)}}class UniformRandom{constructor(min8=0,max10=1,dtype,seed){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=min8,this.range=max10-min8,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min8} - ${max10} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}}function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);const rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=rgamma.nextValue();return res.toTensor()}const randomGamma=op({randomGamma_});function randomNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error(`Unsupported data type ${dtype}`);const randGauss=new MPRandGauss(mean7,stdDev,dtype,!1,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}const randomNormal=op({randomNormal_});function randomUniform_(shape,minval=0,maxval=1,dtype="float32",seed){const res=buffer(shape,dtype),random=new UniformRandom(minval,maxval,null,seed);for(let i=0;i<res.values.length;i++)res.values[i]=random.nextValue();return res.toTensor()}const randomUniform=op({randomUniform_});function tensor1d(values,dtype){assertNonNull(values);const inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");const shape=null;return makeTensor(values,shape,inferredShape,dtype)}function range(start,stop,step9=1,dtype="float32"){if(step9===0)throw new Error("Cannot have a step of zero");const forward=()=>{const sameStartStop=start===stop,increasingRangeNegativeStep=start<stop&&step9<0,decreasingRangePositiveStep=stop<start&&step9>1;if(sameStartStop||increasingRangeNegativeStep||decreasingRangePositiveStep)return zeros([0],dtype);const numElements=Math.abs(Math.ceil((stop-start)/step9)),values=makeZerosTypedArray(numElements,dtype);stop<start&&step9===1&&(step9=-1),values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,dtype)},attrs={start,stop,step:step9,dtype};return ENGINE.runKernelFunc(forward,{},null,Range,attrs)}function reciprocal_(x){const $x=convertToTensor(x,"x","reciprocal"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.reciprocal($x);return save([$x]),res},inputs,null,Reciprocal)}const reciprocal=op({reciprocal_});function relu_(x){const $x=convertToTensor(x,"x","relu"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu)}const relu=op({relu_});function relu6_(x){const $x=convertToTensor(x,"x","relu6"),forward=(backend3,save)=>(save([$x]),$x.dtype==="bool"?cast($x,"int32"):backend3.relu6($x)),inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Relu6)}const relu6=op({relu6_});function reverse_(x,axis){const $x=convertToTensor(x,"x","reverse"),forward=backend3=>{const axes=parseAxisParam(axis,$x.shape);if($x.rank===0)return clone($x);const res=backend3.reverse($x,axes);return reshape(res,$x.shape)},inputs={x:$x},attrs={dims:axis};return ENGINE.runKernelFunc(forward,inputs,null,Reverse,attrs)}const reverse=op({reverse_});function reverse1d_(x){const $x=convertToTensor(x,"x","reverse");return assert($x.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}const reverse1d=op({reverse1d_});function reverse2d_(x,axis){const $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}const reverse2d=op({reverse2d_});function reverse3d_(x,axis){const $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}const reverse3d=op({reverse3d_});function reverse4d_(x,axis){const $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}const reverse4d=op({reverse4d_});function round_(x){const $x=convertToTensor(x,"x","round"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.round($x),inputs,null,Round)}const round=op({round_});function rsqrt_(x){const $x=convertToTensor(x,"x","rsqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.rsqrt($x);return save([$x]),res},inputs,null,Rsqrt)}const rsqrt=op({rsqrt_});function selu_(x){const $x=convertToTensor(x,"x","selu"),forward=(backend3,save)=>{const res=backend3.selu($x);return save([$x]),res},inputs={x:$x};return ENGINE.runKernelFunc(forward,inputs,null,Selu)}const selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad11,dilation=[1,1],dataFormat="NHWC"){const $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d");let x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);const inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);const depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad11,dataFormat,dilation),pointwiseStride=1,res=conv2d(depthwise,$pointwiseFilter,pointwiseStride,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){const $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);const xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals);let outputSize=0;for(let i=0;i<xVals.length;i++)ySet.has(xVals[i])||outputSize++;const buffer11=new TensorBuffer([outputSize],$x.dtype),indices=new TensorBuffer([outputSize],"int32");for(let i=0,p2=0;i<xVals.length;i++)ySet.has(xVals[i])||(buffer11.values[p2]=xVals[i],indices.values[p2]=i,p2++);return[buffer11.toTensor(),indices.toTensor()]}const setdiff1dAsync=setdiff1dAsync_;function sign_(x){const $x=convertToTensor(x,"x","sign"),inputs={x:$x};return ENGINE.runKernelFunc(backend3=>backend3.sign($x),inputs,null,Sign)}const sign=op({sign_});function sin_(x){const $x=convertToTensor(x,"x","sin"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.sin($x);return save([$x]),res},inputs,null,Sin)}const sin=op({sin_});function sinh_(x){const $x=convertToTensor(x,"x","sinh"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.sinh($x);return save([$x]),res},inputs,null,Sinh)}const sinh=op({sinh_});function slice1d_(x,begin,size){const $x=convertToTensor(x,"x","slice1d");return assert($x.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}const slice1d=op({slice1d_});function slice2d_(x,begin,size){const $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}const slice2d=op({slice2d_});function slice3d_(x,begin,size){const $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}const slice3d=op({slice3d_});function slice4d_(x,begin,size){const $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}const slice4d=op({slice4d_});function softmax_(logits,dim=-1){const $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);const inputs={logits:$logits},attrs={dim};return ENGINE.runKernelFunc((backend3,save)=>{const y=backend3.softmax($logits,dim);return save([y]),y},inputs,null,Softmax,attrs)}const softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);const inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{const innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=input2.as2D(batch,innerDimensionSize),result=backend3.fft(input2D);return result.reshape(input2.shape)},inputs,null,FFT)}const fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);const inputs={input:input2};return ENGINE.runKernelFunc(backend3=>{const innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,input2D=reshape(input2,[batch,innerDimensionSize]),result=backend3.ifft(input2D);return reshape(result,input2.shape)},inputs,null,IFFT)}const ifft=op({ifft_});function irfft_(input2){const innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize;let ret;if(innerDimensionSize<=2){const complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{const outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){const temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}const irfft=op({irfft_});function prepareSplitSize(x,numOrSizeSplits,axis=0){let splitSizes=[];if(typeof numOrSizeSplits=="number")assert(x.shape[axis]%numOrSizeSplits===0,()=>"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{const numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");const negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){const total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}function split_(x,numOrSizeSplits,axis=0){const $x=convertToTensor(x,"x","split"),forward=(backend3,_)=>{const $axis=parseAxisParam(axis,$x.shape)[0],splitSizes=prepareSplitSize($x,numOrSizeSplits,$axis);return backend3.split($x,splitSizes,$axis)},inputs={x:$x},attr={numOrSizeSplits,axis};return ENGINE.runKernelFunc(forward,inputs,null,SplitV,attr)}const split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1];const batch=input2.size/innerDimensionSize;let adjustedInput;if(fftLength!=null&&fftLength<innerDimensionSize){const begin=input2.shape.map(v=>0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){const zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;const zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}const rfft=op({rfft_});function sqrt_(x){const $x=convertToTensor(x,"x","sqrt"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.sqrt($x);return save([$x]),res},inputs,null,Sqrt)}const sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);const forward=(backend3,save)=>{const res=backend3.squaredDifference($a,$b);return save([$a,$b]),res},inputs={a:$a,b:$b},attrs={};return ENGINE.runKernelFunc(forward,inputs,null,SquaredDifference,attrs)}const squaredDifference=op({squaredDifference_});function squeeze_(x,axis){const $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}const squeeze=op({squeeze_});function stack_(tensors,axis=0){const $tensors=convertToTensorArray(tensors,"tensors","stack");if(assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length===1)return expandDims($tensors[0],axis);const rank=$tensors[0].rank,shape=$tensors[0].shape,dtype=$tensors[0].dtype;assert(axis<=rank,()=>"Axis must be <= rank of the tensor"),$tensors.forEach(t=>{assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});const expandedTensors=$tensors.map(t=>expandDims(t,axis));return concat(expandedTensors,axis)}const stack=op({stack_});function step_(x,alpha=0){const $x=convertToTensor(x,"x","step"),inputs={x:$x},attrs={alpha};return ENGINE.runKernelFunc(backend3=>backend3.step($x,alpha),inputs,null,Step,attrs)}const step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let $x=convertToTensor(x,"x","stridedSlice");const forward=backend3=>{strides==null&&(strides=new Array(begin.length));const ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");const numInterpolatedAxes=$x.rank-begin.length,expandAxes=maskToAxes(newAxisMask),newShape=$x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)}),$x=reshape($x,newShape);const{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes($x.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;const shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});const size=computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided)return reshape(slice($x,begin,size),outShape);const res=backend3.stridedSlice($x,begin,end,strides);return reshape(res,outShape)},inputs={x:$x},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernelFunc(forward,inputs,null,StridedSlice,attrs)}const stridedSlice=op({stridedSlice_});function tan_(x){const $x=convertToTensor(x,"x","tan"),inputs={x:$x};return ENGINE.runKernelFunc((backend3,save)=>{const res=backend3.tan($x);return save([$x]),res},inputs,null,Tan)}const tan=op({tan_});function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");const inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");const inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");const inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");const inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){const $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");const lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);const inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernelFunc(b=>b.topk($x,k,sorted),inputs,null,TopK,attrs);return{values,indices}}const topk=op({topk_});function truncatedNormal_(shape,mean7=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");const randGauss=new MPRandGauss(mean7,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i<res.values.length;i++)res.values[i]=randGauss.nextValue();return res.toTensor()}const truncatedNormal=op({truncatedNormal_});function unique_(x,axis=0){const $x=convertToTensor(x,"x","unique",null);assert($x.rank>0,()=>"The input tensor must be at least 1D");const inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}const unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){const $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");const inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments},forward=(backend3,save)=>{const res=backend3.unsortedSegmentSum($x,$segmentIds,numSegments);return save([$segmentIds]),res};return ENGINE.runKernelFunc(forward,inputs,null,UnsortedSegmentSum,attrs)}const unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){const $x=convertToTensor(x,"x","unstack");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`),axis<0&&(axis+=$x.shape.length);const inputs={value:$x},attrs={axis},forward=backend3=>backend3.unstack($x,axis);return ENGINE.runKernelFunc(forward,inputs,null,Unpack,attrs)}const unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){const indices=[];for(let i=0;i<condVals.length;i++)condVals[i]&&indices.push(i);const inBuffer=buffer(condShape,"int32"),out=buffer([indices.length,condShape.length],"int32");for(let i=0;i<indices.length;i++){const loc=inBuffer.indexToLoc(indices[i]),offset=i*condShape.length;out.values.set(loc,offset)}return out.toTensor()}async function whereAsync_(condition){const $condition=convertToTensor(condition,"condition","whereAsync","bool"),vals=await $condition.data(),res=whereImpl($condition.shape,vals);return condition!==$condition&&$condition.dispose(),res}const whereAsync=whereAsync_;async function booleanMaskAsync_(tensor168,mask,axis){const $tensor=convertToTensor(tensor168,"tensor","boolMask"),$mask=convertToTensor(mask,"mask","boolMask","bool"),axisFrom=axis==null?0:axis,maskDim=$mask.rank,tensorShape=$tensor.shape;assert(maskDim>0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i<axisFrom+maskDim;i++)leadingSize*=tensorShape[i];const targetTensorShape=tensorShape.slice(0,axisFrom).concat([leadingSize],tensorShape.slice(axisFrom+maskDim)),reshapedTensor=reshape($tensor,targetTensorShape),reshapedMask=reshape($mask,[-1]),positivePositions=await whereAsync(reshapedMask),indices=squeeze(positivePositions,[1]),res=gather(reshapedTensor,indices,axisFrom);return tensor168!==$tensor&&$tensor.dispose(),mask!==$mask&&$mask.dispose(),indices.dispose(),reshapedTensor.dispose(),reshapedMask.dispose(),positivePositions.dispose(),res}const booleanMaskAsync=booleanMaskAsync_;function notEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","notEqualStrict"),$b=convertToTensor(b,"b","notEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in notEqualStrict: "),notEqual($a,$b)}function lessStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","lessStrict"),$b=convertToTensor(b,"b","lessStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessStrict: "),less($a,$b)}function equalStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","equalStrict"),$b=convertToTensor(b,"b","equalStrict");return assertShapesMatch($a.shape,$b.shape,"Error in equalStrict: "),equal($a,$b)}function lessEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","lessEqualStrict"),$b=convertToTensor(b,"b","lessEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in lessEqualStrict: "),lessEqual($a,$b)}function greaterStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","greaterStrict"),$b=convertToTensor(b,"b","greaterStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterStrict: "),greater($a,$b)}function greaterEqualStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","greaterEqualStrict"),$b=convertToTensor(b,"b","greaterEqualStrict");return assertShapesMatch($a.shape,$b.shape,"Error in greaterEqualStrict: "),greaterEqual($a,$b)}const equalStrict=op({equalStrict_}),greaterEqualStrict=op({greaterEqualStrict_}),greaterStrict=op({greaterStrict_}),lessEqualStrict=op({lessEqualStrict_}),lessStrict=op({lessStrict_}),notEqualStrict=op({notEqualStrict_});function addStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","addStrict"),$b=convertToTensor(b,"b","addStrict");return assertShapesMatch($a.shape,$b.shape,"Error in addStrict: "),add2($a,$b)}function subStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","subStrict"),$b=convertToTensor(b,"b","subStrict");return assertShapesMatch($a.shape,$b.shape,"Error in subStrict: "),sub($a,$b)}function powStrict_(base2,exp13){return deprecationWarn("strict variants of ops have been deprecated and will be removed in future"),assertShapesMatch(base2.shape,exp13.shape,"Error in powStrict: "),pow(base2,exp13)}function mulStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","mul"),$b=convertToTensor(b,"b","mul");return assertShapesMatch($a.shape,$b.shape,"Error in multiplyStrict: "),mul($a,$b)}function divStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");return assertShapesMatch($a.shape,$b.shape,"Error in divideStrict: "),div($a,$b)}function modStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","modStrict"),$b=convertToTensor(b,"b","modStrict");return assertShapesMatch($a.shape,$b.shape,"Error in modStrict: "),mod($a,$b)}function minimumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","minimumStrict"),$b=convertToTensor(b,"b","minimumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in minimumStrict: "),minimum($a,$b)}function maximumStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","maximumStrict"),$b=convertToTensor(b,"b","maximumStrict");return assertShapesMatch($a.shape,$b.shape,"Error in maximumStrict: "),maximum($a,$b)}function squaredDifferenceStrict_(a,b){deprecationWarn("strict variants of ops have been deprecated and will be removed in future");const $a=convertToTensor(a,"a","squaredDifferenceStrict"),$b=convertToTensor(b,"b","squaredDifferenceStrict");return assertShapesMatch($a.shape,$b.shape,"Error in squaredDifferenceStrict: "),squaredDifference($a,$b)}const addStrict=op({addStrict_}),divStrict=op({divStrict_}),maximumStrict=op({maximumStrict_}),minimumStrict=op({minimumStrict_}),modStrict=op({modStrict_}),mulStrict=op({mulStrict_}),powStrict=op({powStrict_}),squaredDifferenceStrict=op({squaredDifferenceStrict_}),subStrict=op({subStrict_});function norm_(x,ord="euclidean",axis=null,keepDims=!1){x=convertToTensor(x,"x","norm");const norm5=normImpl(x,ord,axis);let keepDimsShape=norm5.shape;if(keepDims){const axes=parseAxisParam(axis,x.shape);keepDimsShape=expandShapeToKeepDim(norm5.shape,axes)}return reshape(norm5,keepDimsShape)}function normImpl(x,p2,axis=null){if(x.rank===0)return abs(x);if(x.rank!==1&&axis===null)return normImpl(reshape(x,[-1]),p2,axis);if(x.rank===1||typeof axis=="number"||Array.isArray(axis)&&axis.length===1){if(p2===1)return sum2(abs(x),axis);if(p2===Infinity)return max(abs(x),axis);if(p2===-Infinity)return min(abs(x),axis);if(p2==="euclidean"||p2===2)return sqrt(sum2(pow(abs(x),scalar(2,"int32")),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}if(Array.isArray(axis)&&axis.length===2){if(p2===1)return max(sum2(abs(x),axis[0]),axis[1]-1);if(p2===Infinity)return max(sum2(abs(x),axis[1]),axis[0]);if(p2===-Infinity)return min(sum2(abs(x),axis[1]),axis[0]);if(p2==="fro"||p2==="euclidean")return sqrt(sum2(square(x),axis));throw new Error(`Error in norm: invalid ord value: ${p2}`)}throw new Error(`Error in norm: invalid axis: ${axis}`)}const norm=op({norm_});function movingAverage_(v,x,decay,step9,zeroDebias=!0){const $v=convertToTensor(v,"v","movingAverage"),$x=convertToTensor(x,"x","movingAverage"),$decay=convertToTensor(decay,"decay","movingAverage");assertTypesMatch($v,$x),assert(arraysEqual($v.shape,$x.shape),()=>"Shape mismatch in v and x");const one=scalar(1),oneMinusDecay=sub(one,$decay);let update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step9!=null,()=>"When using zeroDebias: true, step is required.");const $step=convertToTensor(step9,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}const movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){const $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);const forward=backend3=>backend3.scatterND($indices,$updates,shape),inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernelFunc(forward,inputs,null,ScatterNd,attrs)}const scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);const numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);const numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){const $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);const inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernelFunc(backend3=>backend3.sparseToDense($sparseIndices,$sparseValues,outputShape,$defaultValue),inputs,null,SparseToDense,attrs)}const sparseToDense=op({sparseToDense_});function gatherND_(x,indices){const $indices=convertToTensor(indices,"indices","gatherND","int32"),$x=convertToTensor(x,"x","gatherND"),forward=backend3=>backend3.gatherND($x,$indices),inputs={params:$x,indices:$indices};return ENGINE.runKernelFunc(forward,inputs,null,GatherNd)}const gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){const newDimension=[];for(let i=0;i<x.shape.length;i++)noiseShape[i]==null&&x.shape[i]!=null?newDimension.push(x.shape[i]):newDimension.push(noiseShape[i]);return newDimension}return noiseShape}function dropout_(x,rate,noiseShape,seed){const $x=convertToTensor(x,"x","dropout");if(assert($x.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;const $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}const dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){const even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i<windowLength;++i){const cosArg=2*Math.PI*i/(windowLength+even-1);newValues[i]=a-b*Math.cos(cosArg)}return tensor1d(newValues,"float32")}async function inTopKAsync_(predictions,targets,k=1){const $predictions=convertToTensor(predictions,"predictions","inTopK"),$targets=convertToTensor(targets,"targets","inTopK");assert($predictions.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");const lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);const predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;b<batch;b++){const offset=b*size,vals=predictionsVals.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value),precision3[b]=0;for(let i=0;i<k;i++)if(valAndInd[i].index===targetsVals[b]){precision3[b]=1;break}}return predictions!==$predictions&&$predictions.dispose(),targets!==$targets&&$targets.dispose(),tensor4(precision3,$targets.shape,"bool")}const inTopKAsync=inTopKAsync_,fused_ops_exports={};__export2(fused_ops_exports,{conv2d:()=>conv2d5,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad11,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);const inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=backend3=>{const dilations=1,$dataFormat=convertConv2DDataFormat(dataFormat),convInfo=computeConv2DInfo(x4D.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat);return backend3.conv2dDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv2DBackpropFilter,attrs)}const conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation;const reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);throw new Error(`Unknown fused activation ${activation2}.`)}const shouldFuse=(gradientDepth,activation2)=>{const gradientMode=gradientDepth>0;return!gradientMode||activation2==="linear"};function fusedConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}const $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d");let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);const convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode);let $bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));const grad2=(dy,saved)=>{const[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);const xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11),der=[xDer,filterDer];if($bias2!=null){const biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},forward=backend3=>{const res=backend3.fusedConv2d({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){const customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{const customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}const conv2d5=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad11,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));const forward=backend3=>{const convInfo=computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerFilter(x4D,dy4D,convInfo)},inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad11,dimRoundingMode,dilations,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropFilter,attrs)}const depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad11,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));const forward=backend3=>{const convInfo=computeConv2DInfo(xShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0);return backend3.depthwiseConv2DDerInput(dy4D,filter,convInfo)},inputs={dy:dy4D,filter},attrs={strides,pad:pad11,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernelFunc(forward,inputs,null,DepthwiseConv2dNativeBackpropInput,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad11,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad11,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}const $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d");let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad11,dimRoundingMode,!0);let $bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));const grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);const[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad11,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad11,dilations,dimRoundingMode);if(bias2!=null){const biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},forward=backend3=>{const res=backend3.fusedDepthwiseConv2D({input:x4D,filter:$filter,convInfo,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return res},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2};if(bias==null){const customOp=customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOp(x4D,$filter)}else{const customOpWithBias=customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernelFunc(forward,inputs,null,FusedDepthwiseConv2D,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}});return customOpWithBias(x4D,$filter,$bias)}}const depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);const innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);const outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]);let $bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));const grad2=(dy,saved)=>{const[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2);let aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){const biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},forward=backend3=>{const y=backend3.fusedBatchMatMul({a:a3D,b:b3D,transposeA,transposeB,bias:$bias,activation:activation2,preluActivationWeights:$preluActivationWeights});return y},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2};if(bias==null){const customOp=customGrad((a3D2,b3D2,save)=>{const res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}});return customOp(a3D,b3D)}else{const customOpWithBias=customGrad((a3D2,b3D2,$bias2,save)=>{const res=ENGINE.runKernelFunc(forward,inputs,null,_FusedMatMul,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}});return customOpWithBias(a3D,b3D,$bias)}}const matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}const hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}const hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0;const output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start<signal2.size;){const padLen=start+frameLength-signal2.size,pad11=concat([slice(signal2,start,frameLength-padLen),fill([padLen],padValue)]);output.push(pad11),start+=frameStep}return output.length===0?tensor2d([],[0,frameLength]):reshape(concat(output),[output.length,frameLength])}const frame=op({frame_});function stft_(signal2,frameLength,frameStep,fftLength,windowFn=hannWindow){fftLength==null&&(fftLength=enclosingPowerOfTwo(frameLength));const framedSignal=frame(signal2,frameLength,frameStep),windowedSignal=mul(framedSignal,windowFn(frameLength)),output=[];for(let i=0;i<framedSignal.shape[0];i++)output.push(rfft(slice(windowedSignal,[i,0],[1,frameLength]),fftLength));return concat(output)}const stft=op({stft_});function cropAndResize_(image3,boxes,boxInd,cropSize,method,extrapolationValue){const $image=convertToTensor(image3,"image","cropAndResize"),$boxes=convertToTensor(boxes,"boxes","cropAndResize","float32"),$boxInd=convertToTensor(boxInd,"boxInd","cropAndResize","int32");method=method||"bilinear",extrapolationValue=extrapolationValue||0;const numBoxes=$boxes.shape[0];assert($image.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);const forward=backend3=>backend3.cropAndResize($image,$boxes,$boxInd,cropSize,method,extrapolationValue),inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize},res=ENGINE.runKernelFunc(forward,inputs,null,CropAndResize,attrs);return res}const cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){const $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);const inputs={image:$image},res=ENGINE.runKernel(FlipLeftRight,inputs,{});return res}const flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){const $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);const inputs={image:$image},attrs={radians,fillValue,center},res=ENGINE.runKernel(RotateWithOffset,inputs,attrs);return res}const rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);const numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){const $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;const attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernelFunc(b=>b.nonMaxSuppression($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold),{boxes:$boxes,scores:$scores},null,NonMaxSuppressionV3,attrs)}const nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){const index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a<b?-1:0}function binarySearch_(arr,target,comparator){let left=0,right=arr.length,middle=0,found=!1;for(;left<right;){middle=left+(right-left>>>1);const compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0).selectedIndices}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){const candidates=[];for(let i=0;i<scores.length;i++)scores[i]>scoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);const scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length<maxOutputSize&&candidates.length>0;){const candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore<scoreThreshold)break;let ignoreCandidate=!1;for(let j=selectedIndices.length-1;j>=suppressBeginIndex;--j){const iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}const validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));const result={selectedIndices:tensor1d(selectedIndices,"int32")};return returnScoresTensor&&(result.selectedScores=tensor1d(selectedScores,"float32")),returnValidOutputs&&(result.validOutputs=scalar(validOutputs,"int32")),result}function intersectionOverUnion(boxes,i,j){const iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;const intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){const weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){const $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;const boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}const nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){const $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;const inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}const nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){const $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;const boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],res=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}const nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){const $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}const nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){const $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),res=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),res}const nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1){const $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));const[newHeight,newWidth]=size,forward=(backend3,save)=>(save([batchImages]),backend3.resizeBilinear(batchImages,newHeight,newWidth,alignCorners)),inputs={images:batchImages},attrs={alignCorners,size},res=ENGINE.runKernelFunc(forward,inputs,null,ResizeBilinear,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1){const $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));const[newHeight,newWidth]=size,inputs={images:batchImages},attrs={alignCorners,size},forward=(backend3,save)=>(save([batchImages]),backend3.resizeNearestNeighbor(batchImages,newHeight,newWidth,alignCorners)),res=ENGINE.runKernelFunc(forward,inputs,null,ResizeNearestNeighbor,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1===0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1===0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);const $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);const shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);const i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}const bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const dim=xs[0].shape[0];for(let i=1;i<xs.length;++i)assert(xs[i].shape[0]===dim,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);const ys=[],xs1d=xs;for(let i=0;i<xs.length;++i)ys.push(ENGINE.tidy(()=>{let x=xs1d[i];if(i>0)for(let j=0;j<i;++j){const proj=mul(sum2(mul(ys[j],x)),ys[j]);x=sub(x,proj)}return div(x,norm(x,"euclidean"))}));return inputIsTensor2D?stack(ys,0):ys}const gramSchmidt=op({gramSchmidt_});function qr_(x,fullMatrices=!1){if(assert(x.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{const outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{const[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});const q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);const m=x.shape[0],n=x.shape[1];let q=eye(m),r=clone(x);const one2D=tensor2d([[1]],[1,1]);let w=clone(one2D);const iters=m>=n?n:m;for(let j=0;j<iters;++j){const rTemp=r,wTemp=w,qTemp=q;[w,r,q]=ENGINE.tidy(()=>{const rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);const tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{const rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}const tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{const qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}const qr=op({qr_});var Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses8,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){const $losses=convertToTensor(losses8,"losses","computeWeightedLoss");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));const weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{const broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{const broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}const computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){const $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");const losses8=abs(sub($labels,$predictions));return computeWeightedLoss(losses8,$weights,reduction2)}const absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){const $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");const one=scalar(1),losses8=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses8,$weights,reduction2)}const cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss");const $predictions=convertToTensor(predictions,"predictions","hingeLoss");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");const one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);const losses8=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses8,$weights,reduction2)}const hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){const $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");const deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses8=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses8,$weights,reduction2)}const huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){const $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");const one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log(add2(sub(one,$predictions),epsilonScalar))),losses8=sub(l13,l23);return computeWeightedLoss(losses8,$weights,reduction2)}const logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){const $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError");let $weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");const losses8=squaredDifference($labels,$predictions);return computeWeightedLoss(losses8,$weights,reduction2)}const meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){const $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");const maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy");const $logits=convertToTensor(logits,"logits","sigmoidCrossEntropy");let $weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){const labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}const losses8=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}const sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);const customOp=customGrad((labels2,logits2,save)=>{const keepDims=!0,lse=logSumExp(logits2,[dim],keepDims),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);const costVector=neg(mul(logResult,labels2)),value=sum2(costVector,[dim]),gradFunc=(dy,saved)=>{const[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]};return{value,gradFunc}});return customOp(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy");const $logits=convertToTensor(logits,"logits","softmaxCrossEntropy");let $weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){const labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}const losses8=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses8,$weights,reduction2)}const softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy};class Optimizer extends Serializable{minimize(f,returnCost=!1,varList){const{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){const gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}}Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});class AdadeltaOptimizer extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){const variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{const value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});const gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;const accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{const newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);const newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){const variables5=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);const variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.rho,config2.epsilon)}}AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);class AdagradOptimizer extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){const variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{const value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){const trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}const gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;const accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{const newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);const newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);const trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.initialAccumulatorValue)}}AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);class AdamOptimizer extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){const varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{const oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{const value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});const gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;const firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);const newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){const variables5=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});const variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon)}}AdamOptimizer.className="Adam";registerClass(AdamOptimizer);class AdamaxOptimizer extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){const variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{const oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{const value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});const gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;const firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);const newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon,config2.decay)}}AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);class SGDOptimizer extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){const varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);varNames.forEach((name,i)=>{const gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;const value=ENGINE.registeredVariables[name];tidy(()=>{const newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config2){return new cls(config2.learningRate)}}SGDOptimizer.className="SGD";registerClass(SGDOptimizer);class MomentumOptimizer extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){const variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{const value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){const trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}const accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;tidy(()=>{let newValue;const newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);const trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.momentum,config2.useNesterov)}}MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);class RMSPropOptimizer extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){const variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);variableNames.forEach((name,i)=>{const value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});const gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;const accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{const newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){const accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);const newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{const newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);const newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){const variables5=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables5.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables5.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);const variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.decay,config2.momentum,config2.epsilon,config2.centered)}}RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);class OptimizerConstructors{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}}const train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}const backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,linspaceImpl:()=>linspaceImpl,log:()=>log6,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function getImageCenter(center,imageHeight,imageWidth){const centerX=imageWidth*(typeof center=="number"?center:center[0]),centerY=imageHeight*(typeof center=="number"?center:center[1]);return[centerX,centerY]}function getReshaped(inputShape,blockShape,prod5,batchToSpace=!0){let reshaped=[];if(batchToSpace)reshaped=reshaped.concat(blockShape.slice(0)),reshaped.push(inputShape[0]/prod5),reshaped=reshaped.concat(inputShape.slice(1));else{reshaped=reshaped.concat(inputShape[0]);const spatialLength=blockShape.length;for(let i=0;i<spatialLength;++i)reshaped=reshaped.concat([inputShape[i+1]/blockShape[i],blockShape[i]]);reshaped=reshaped.concat(inputShape.slice(spatialLength+1))}return reshaped}function getPermuted(reshapedRank,blockShapeRank,batchToSpace=!0){const permuted=[];if(batchToSpace){permuted.push(blockShapeRank);for(let i=blockShapeRank+1;i<reshapedRank;++i)i<=2*blockShapeRank?(permuted.push(i),permuted.push(i-(blockShapeRank+1))):permuted.push(i)}else{const permutedBeforeBatch=[],permutedAfterBatch=[];for(let i=1;i<reshapedRank;++i)i>=blockShapeRank*2+1||i%2===1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){const reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i<inputShape.length;++i)i<=blockShape.length?batchToSpace?reshapedPermuted.push(blockShape[i-1]*inputShape[i]):reshapedPermuted.push(inputShape[i]/blockShape[i-1]):reshapedPermuted.push(inputShape[i]);return reshapedPermuted}function getSliceBeginCoords(crops,blockShape){const sliceBeginCoords=[0];for(let i=0;i<blockShape;++i)sliceBeginCoords.push(crops[i][0]);return sliceBeginCoords}function getSliceSize(uncroppedShape,crops,blockShape){const sliceSize=uncroppedShape.slice(0,1);for(let i=0;i<blockShape;++i)sliceSize.push(uncroppedShape[i+1]-crops[i][0]-crops[i][1]);return sliceSize}const SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429;function warn(...msg){env().getBool("IS_TEST")||console.warn(...msg)}function log6(...msg){env().getBool("IS_TEST")||console.log(...msg)}function mergeRealAndImagArrays(real8,imag8){if(real8.length!==imag8.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real8.length}, imag: ${imag8.length}.`);const result=new Float32Array(real8.length*2);for(let i=0;i<result.length;i+=2)result[i]=real8[i/2],result[i+1]=imag8[i/2];return result}function splitRealAndImagArrays(complex11){const real8=new Float32Array(complex11.length/2),imag8=new Float32Array(complex11.length/2);for(let i=0;i<complex11.length;i+=2)real8[i/2]=complex11[i],imag8[i/2]=complex11[i+1];return{real:real8,imag:imag8}}function complexWithEvenIndex(complex11){const len=Math.ceil(complex11.length/4),real8=new Float32Array(len),imag8=new Float32Array(len);for(let i=0;i<complex11.length;i+=4)real8[Math.floor(i/4)]=complex11[i],imag8[Math.floor(i/4)]=complex11[i+1];return{real:real8,imag:imag8}}function complexWithOddIndex(complex11){const len=Math.floor(complex11.length/4),real8=new Float32Array(len),imag8=new Float32Array(len);for(let i=2;i<complex11.length;i+=4)real8[Math.floor(i/4)]=complex11[i],imag8[Math.floor(i/4)]=complex11[i+1];return{real:real8,imag:imag8}}function getComplexWithIndex(complex11,index){const real8=complex11[index*2],imag8=complex11[index*2+1];return{real:real8,imag:imag8}}function assignToTypedArray(data2,real8,imag8,index){data2[index*2]=real8,data2[index*2+1]=imag8}function exponents(n,inverse){const real8=new Float32Array(n/2),imag8=new Float32Array(n/2);for(let i=0;i<Math.ceil(n/2);i++){const x=(inverse?2:-2)*Math.PI*(i/n);real8[i]=Math.cos(x),imag8[i]=Math.sin(x)}return{real:real8,imag:imag8}}function exponent(k,n,inverse){const x=(inverse?2:-2)*Math.PI*(k/n),real8=Math.cos(x),imag8=Math.sin(x);return{real:real8,imag:imag8}}function castTensor(x,dtype,backend3){if(dtype==="complex64"){if(x.dtype==="complex64")return x.clone();const zerosTensor=zeros(x.shape),floatX=cast(x,"float32"),result=backend3.complex(floatX,zerosTensor);return zerosTensor.dispose(),floatX.dispose(),result}if(!hasEncodingLoss(x.dtype,dtype))return ENGINE.makeTensorFromDataId(x.dataId,x.shape,dtype);if(x.dtype==="complex64"){const real8=backend3.real(x),result=cast(real8,dtype);return real8.dispose(),result}if(dtype==="int32")return backend3.int(x);if(dtype==="bool"){const zero=scalar(0,x.dtype),result=backend3.notEqual(x,zero);return zero.dispose(),result}else throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}function reshapeTensor(x,shape){return ENGINE.makeTensorFromDataId(x.dataId,shape,x.dtype)}function linspaceImpl(start,stop,num){const step9=(stop-start)/(num-1),values=makeZerosTypedArray(num,"float32");values[0]=start;for(let i=1;i<values.length;i++)values[i]=values[i-1]+step9;return tensor1d(values,"float32")}const kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,split:()=>split5,tile:()=>tile4,topkImpl:()=>topkImpl,whereImpl:()=>whereImpl});function split5(x,sizeSplits,axis){const begin=new Array(x.rank).fill(0),size=x.shape.slice();return sizeSplits.map(s=>{const sliceSize=[...size];sliceSize[axis]=s;const sliceT=slice(x,begin,sliceSize);return begin[axis]+=s,sliceT})}function tile4(xBuf,reps){const newShape=new Array(xBuf.rank);for(let i=0;i<newShape.length;i++)newShape[i]=xBuf.shape[i]*reps[i];const result=buffer(newShape,xBuf.dtype);for(let i=0;i<result.values.length;++i){const newLoc=result.indexToLoc(i),originalLoc=new Array(xBuf.rank);for(let j=0;j<originalLoc.length;j++)originalLoc[j]=newLoc[j]%xBuf.shape[j];const originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}function topkImpl(x,xShape,xDtype,k,sorted){const lastDim=xShape[xShape.length-1],[batch,size]=[x.length/lastDim,lastDim],allTopKVals=getTypedArrayFromDType(xDtype,batch*k),allTopKIndices=getTypedArrayFromDType("int32",batch*k);for(let b=0;b<batch;b++){const offset=b*size,vals=x.subarray(offset,offset+size),valAndInd=[];for(let i=0;i<vals.length;i++)valAndInd.push({value:vals[i],index:i});valAndInd.sort((a,b2)=>b2.value-a.value);const outOffset=b*k,topKVals=allTopKVals.subarray(outOffset,outOffset+k),topKIndices=allTopKIndices.subarray(outOffset,outOffset+k);for(let i=0;i<k;i++)topKVals[i]=valAndInd[i].value,topKIndices[i]=valAndInd[i].index}const outputShape=xShape.slice();return outputShape[outputShape.length-1]=k,[tensor4(allTopKVals,outputShape,xDtype),tensor4(allTopKIndices,outputShape,"int32")]}const absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>{const a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>{const a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{let res=dy;const reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy;const reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{const ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>{const a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{const d=add2(square(a),square(b));let res=mul(dy,div(b,d));const reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{const d=add2(square(a),square(b));let res=neg(mul(dy,div(a,d)));const reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)};return{a:derA,b:derB}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dBackprop_(dy,input2,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){const $dy=convertToTensor(dy,"dy","avgPool3dBackprop"),$input=convertToTensor(input2,"input","avgPool3dBackprop");let dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=backend3=>{const convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.avgPool3dBackprop(dy5D,input5D,convInfo)},inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}const avgPool3dBackprop=op({avgPool3dBackprop_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const[x]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dBackprop(dy,x,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function avgPoolBackprop_(dy,input2,filterSize,strides,pad11){const $dy=convertToTensor(dy,"dy","avgPoolBackprop"),$input=convertToTensor(input2,"input","avgPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolBackprop: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolBackprop: input must be rank 4 but got rank ${input4D.rank}.`);const forward=backend3=>{const convInfo=computePool2DInfo(input4D.shape,filterSize,strides,1,pad11);return backend3.avgPoolBackprop(dy4D,input4D,convInfo)},inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad11},res=ENGINE.runKernelFunc(forward,inputs,null,AvgPoolBackprop,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}const avgPoolBackprop=op({avgPoolBackprop_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const[x]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>avgPoolBackprop(dy,x,filterSize,strides,pad11)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{const[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{const{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{const broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);const axes=[];for(let i=0;i<reps.length;i++)reps[i]>1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{const shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]),derTensors=split(dy,sizeSplits,$axis);return derTensors.map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{const[x4D,$filter]=saved,{dilations,strides,pad:pad11,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad11,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad11,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{const[dy,filter]=saved,{strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad11,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad11,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad11){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);const forward=backend3=>{const dilations=1,convInfo=computeConv3DInfo(x5D.shape,filterShape,strides,dilations,pad11);return backend3.conv3dDerFilter(x5D,dy5D,convInfo)},inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad11,filterShape};return ENGINE.runKernelFunc(forward,inputs,null,Conv3DBackpropFilterV2,attrs)}const conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{const{dilations,strides,pad:pad11}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);const[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad11),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad11)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const[x]=saved,{axis,exclusive,reverse:reverse12}=attrs;return{x:()=>{const permutation=getAxesPermutation([axis],x.rank);let out=cumsum(dy,axis,exclusive,!reverse12);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{const{dilations,strides,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);const[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad11,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad11,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{const[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},divGradConfig={kernelName:Div,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{const res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32"));const reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));const tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{const[y]=saved,backPropKernelFunc=backend3=>backend3.eluDer(dy,y),inputs={dy,y};return{x:()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,EluGrad)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{const[y]=saved;return{x:()=>mul(dy,y)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{const res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{let res=mul(dy,cast(a,"float32"));const reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));const tmp=square(b);return neg(div(res,cast(tmp,"float32")))};return{a:derA,b:derB}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{const{varianceEpsilon}=attrs,[x,mean7,variance,scale2]=saved,scaleValue=scale2==null?scalar(1):scale2,reductionAxes=getReductionAxes(mean7.shape,x.shape),tileShape=[];if(mean7.rank===1){for(let i=0;i<x.shape.length-1;++i)tileShape.push(x.shape[i]);tileShape.push(1)}const xMinusMean=sub(x,mean7),dyTimesScaleValue=mul(dy,scaleValue),oneOverSqrtVariance=rsqrt(add2(variance,scalar(varianceEpsilon))),minusHalfRCube=mul(mul(mul(oneOverSqrtVariance,oneOverSqrtVariance),oneOverSqrtVariance),scalar(-.5)),derX=()=>mean7.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean7.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),derMean=()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean7.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean7.shape)},derVariance=()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean7.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean7.shape)},derScale=()=>{const xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance);let scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean7.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean7.shape)},derOffset=()=>{let offsetDer=dy;return mean7.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean7.shape)};return{x:derX,mean:derMean,variance:derVariance,scale:derScale,offset:derOffset}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{const[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0],derX=()=>{const paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims);let paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]);const invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad};return{x:derX,indices:()=>indices}}};function arrayRange(start,stop){const result=[];for(let i=start;i<stop;++i)result.push(i);return result}function arrayConcat(arrays){const result=[];for(let i=0;i<arrays.length;++i)for(let j=0;j<arrays[i].length;++j)result.push(arrays[i][j]);return result}const greaterEqualGradConfig={kernelName:GreaterEqual,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{const[value]=saved,{axis}=attrs;return{logits:()=>{const keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){const forward=backend3=>backend3.LRNGrad(dy,x,y,depthRadius,bias,alpha,beta),inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernelFunc(forward,inputs,null,LRNBackprop,attrs)}const localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{const[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rank<xOrig.rank&&(y=reshape(y,expandShapeToKeepDim(y.shape,origAxes))),dy.rank<xOrig.rank&&(dy=reshape(dy,expandShapeToKeepDim(dy.shape,origAxes))),{x:()=>{const dx=mul(dy,cast(equal(xOrig,y),dy.dtype));return dx}}}const maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{const maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,derA=()=>mul(dy,cast(greaterEqual(a,b),"float32")),derB=()=>mul(dy,cast(less(a,b),"float32"));return{a:derA,b:derB}}};function maxPool3dBackprop_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad11,dimRoundingMode){const $dy=convertToTensor(dy,"dy","maxPool3dBackprop"),$input=convertToTensor(input2,"input","maxPool3dBackprop"),$output=convertToTensor(output,"output","maxPool3dBackprop");let dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dBackprop: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dBackprop: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dBackprop: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dBackprop: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPool3dBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=backend3=>{const convInfo=computePool3DInfo(input5D.shape,filterSize,strides,dilations,pad11,dimRoundingMode);return backend3.maxPool3dBackprop(dy5D,input5D,output5D,convInfo)},inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad11,dimRoundingMode},res=ENGINE.runKernelFunc(forward,inputs,null,MaxPool3DBackprop,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}const maxPool3dBackprop=op({maxPool3dBackprop_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{const[x,y]=saved,{filterSize,strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dBackprop(dy,x,y,filterSize,strides,$dilations,pad11,dimRoundingMode)}}};function maxPoolBackprop_(dy,input2,output,filterSize,strides,pad11,dimRoundingMode){const $dy=convertToTensor(dy,"dy","maxPoolBackprop"),$input=convertToTensor(input2,"input","maxPoolBackprop"),$output=convertToTensor(output,"output","maxPoolBackprop");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolBackprop: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolBackprop: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad11),()=>`Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad11}.`);const forward=backend3=>{const convInfo=computePool2DInfo($input.shape,filterSize,strides,1,pad11,dimRoundingMode);return backend3.maxPoolBackprop($dy,$input,$output,convInfo)},inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad11,dimRoundingMode};return ENGINE.runKernelFunc(forward,inputs,null,MaxPoolBackprop,attrs)}const maxPoolBackprop=op({maxPoolBackprop_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{const[x,y]=saved,{filterSize,strides,pad:pad11}=attrs;return{x:()=>maxPoolBackprop(dy,x,y,filterSize,strides,pad11)}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{const minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,derA=()=>mul(dy,cast(lessEqual(a,b),"float32")),derB=()=>mul(dy,cast(greater(a,b),"float32"));return{a:derA,b:derB}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{const reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},derB=()=>{const res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{const[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape),derA=()=>{const res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},derB=()=>{const res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res};return{a:derA,b:derB}}},negateGradConfig={kernelName:Negate,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{const indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{const[a,b,y]=saved,base2=a,exp13=b,outShape=assertAndGetBroadcastShape(base2.shape,exp13.shape),derBase=()=>{const expFloat=cast(exp13,"float32");let res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1)))));const reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},derExp=()=>{const condition=greater(base2,0),logBase=where(condition,log(base2),zerosLike(base2));let res=mul(dy,mul(y,logBase));const reduceAxes=getReductionAxes(exp13.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp13.shape)};return{a:derBase,b:derExp}}},preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{const[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x));const reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}},reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>div(dy,neg(square(x)))}}},relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}},reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}},reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>reshape(dy,x.shape)}}},resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{const[images]=saved,backPropKernelFunc=backend3=>{const{alignCorners}=attrs;return backend3.resizeBilinearBackprop(dy,images,alignCorners)},inputs={images},imagesDer=()=>ENGINE.runKernelFunc(backPropKernelFunc,inputs,null,ResizeBilinearGrad,attrs);return{images:imagesDer}}},resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{const[images]=saved,backPropKernelFunc=backend3=>{const{alignCorners}=attrs;return 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mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale2=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale2),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{const[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{const[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let 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res=dy;const reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},derB=()=>{let res=dy;const reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)};return{a:derA,b:derB}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{const[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs,axes=parseAxisParam(axis,x.shape);axes.forEach(axis2=>{expandedDyShape[axis2]=1});const 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i=0;i<reps[0];++i)for(let j=0;j<reps[1];++j)for(let k=0;k<reps[2];++k)for(let l=0;l<reps[3];++l)xGrad=add2(xGrad,slice(dy,[i*x.shape[0],j*x.shape[1],k*x.shape[2],l*x.shape[3]],[x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);return xGrad};return{x:derX}}},transposeGradConfig={kernelName:Transpose,gradFunc:(dy,saved,attrs)=>{const transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{const unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{const[segmentIds]=saved,derX=()=>gatherDropNegatives(dy,segmentIds);return{x:derX}}};function gatherDropNegatives(x,indices){const zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices);let isPositive=greaterEqual(indices,scalar(0,"int32"));const numIters=gathered.rank-isPositive.rank;for(let i=0;i<numIters;++i)isPositive=expandDims(isPositive,i+1);isPositive=logicalAnd(isPositive,ones2(gathered.shape,"bool"));const zeroSlice=zerosLike(gathered);return where(isPositive,gathered,zeroSlice)}const zerosLikeGradConfig={kernelName:ZerosLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negateGradConfig,oneHotGradConfig,onesLikeGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectV2PoolGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(const gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean7,variance,offset,scale2,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean7,variance,offset,scale2,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min8,max10){return this.throwIfDisposed(),clipByValue(this,min8,max10)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad11,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad11,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad11,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse12){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse12)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad11,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad11,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad11,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad11,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad11,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad11,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding2,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding2,dilationRate,strides)};Tensor.prototype.powStrict=function(exp13){return this.throwIfDisposed(),powStrict(this,exp13)};Tensor.prototype.pow=function(exp13){return this.throwIfDisposed(),pow(this,exp13)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad11,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();const tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};const exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});let _epsilon;function epsilon(){return _epsilon==null&&(_epsilon=backend2().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}class AttributeError extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}}class RuntimeError extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}}class ValueError extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}}class NotImplementedError extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}}class AssertionError extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}}class IndexError extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}}function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;i<numValues;i++)newArray=newArray.concat(value);return newArray}else{const newArray=new Array(numValues);return newArray.fill(value),newArray}}function assert2(val,message){if(!val)throw new AssertionError(message)}function count(array2,refernce){let counter=0;for(const item of array2)item===refernce&&counter++;return counter}function singletonOrArray(xs){return xs.length===1?xs[0]:xs}function toList(x){return Array.isArray(x)?x:[x]}function toSnakeCase(name){const intermediate=name.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2"),insecure=intermediate.replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return insecure[0]!=="_"?insecure:"private"+insecure}function toCamelCase(identifier){return identifier.length<=1||identifier.indexOf("_")===-1?identifier:identifier.replace(/[_]+(\w|$)/g,(m,p1)=>p1.toUpperCase())}let _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;const dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config2){if(config2==null||typeof config2!="object")return;if(Array.isArray(config2))config2.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{const fields=Object.keys(config2);for(const field of fields){const value=config2[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config2[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){const functionName=identifier;let fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{const config2=identifier;if(config2.className==null||config2.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}.
'className' and 'config' must set.`);const className=config2.className;let cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){const customObjectsCombined={};for(const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(const key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];const nestedConfig=config2.config;nestedConfig.customObjects=customObjectsCombined;const backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(const key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config2.config);const returnObj=fromConfig(cls,config2.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{const backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(const key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];const returnObj=new cls(config2.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return a<b?-1:a>b?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique5(xs){if(xs==null)return xs;const out=[];for(const x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(const key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value==null)return;if(values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;const f2=(...args)=>{const now22=util_exports.now();return now22-lastTime<waitMs||(lastTime=now22,lastResult=f(...args)),lastResult};return f2}function mapActivationToFusedKernel(activationName){return activationName==="relu"?"relu":activationName==="linear"?"linear":activationName==="elu"?"elu":null}function calcL2Norms(w,axis){return tidy(()=>sqrt(sum2(mul(w,w),axis,!0)))}class Constraint extends serialization_exports.Serializable{getConfig(){return{}}}class MaxNorm extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{const norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);class UnitNorm extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}}UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);class NonNeg extends Constraint{apply(w){return relu(w)}}NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);class MinMaxNorm extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{const norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}}MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);const CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){const className=identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config2={className,config:{}};return deserializeConstraint(config2)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config2){return new MinMaxNorm(config2)}const exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones8,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros9});const VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}const _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{const val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);const index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){const result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}const tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i<end;++i)prod5*=array2[i];return prod5}function toArray1D(array2){return array2=Array.isArray(array2)?new Float32Array(array2):array2,tensor1d(array2)}function min6(array2){return min(toArray1D(array2)).dataSync()[0]}function max8(array2){return max(toArray1D(array2)).dataSync()[0]}function range4(begin,end){if(end<begin)throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);const out=[];for(let i=begin;i<end;++i)out.push(i);return out}function cast48(x,dtype){return x.asType(dtype)}function expandDims2(x,axis=-1){const outShape=x.shape.slice();return axis<0&&(axis=outShape.length+axis+1),outShape.splice(axis,0,1),x.reshape(outShape)}function repeat(x,n){return tidy(()=>{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);const y=expandDims2(x,1);return tile8(y,[1,n,1])})}function flatten3(x){const newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);const newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile8(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean7=0,stddev=1,dtype,seed){return randomNormal(shape,mean7,stddev,dtype,seed)}function dot5(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){const xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){const transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{const aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);const bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);const outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather7(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square24(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){const biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu6(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{const y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training5=!1){return training5?x():alt()}const VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}class Initializer extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}}class Zeros extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}}Zeros.className="Zeros";serialization_exports.registerClass(Zeros);class Ones extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}}Ones.className="Ones";serialization_exports.registerClass(Ones);class Constant extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}}Constant.className="Constant";serialization_exports.registerClass(Constant);class RandomUniform extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}}RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);class RandomNormal extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);class TruncatedNormal extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);class Identity2 extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}}Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){const receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){const receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{const shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}class VarianceScaling extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){const fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1];let scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){const stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{const limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);class GlorotUniform extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}}GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);class GlorotNormal extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}}GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);class HeNormal extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}}HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);class HeUniform extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}}HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);class LeCunNormal extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}}LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);class LeCunUniform extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}}LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);class Orthogonal extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);const normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32");let q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}}Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);const INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){const className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{const config2={};return config2.className=className,config2.config={},deserializeInitializer(config2)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros9(){return new Zeros}function ones8(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity(args){return new Identity2(args)}function varianceScaling(config2){return new VarianceScaling(config2)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}const exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add31,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d5,conv2d:()=>conv2d10,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d3,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d4,dot:()=>dot6,dropout:()=>dropout3,elu:()=>elu7,embedding:()=>embedding,flatten:()=>flatten4,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum9,minimum:()=>minimum7,multiply:()=>multiply,permute:()=>permute,prelu:()=>prelu6,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape87,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax4,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});let _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}const _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(const weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}const DEFAULT_VARIABLE_NAME_PREFIX="Variable";class LayerVariable{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}}function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{const variable3=variableAndValue[0];variable3.write(variableAndValue[1])})}class InputSpec{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}}class SymbolicTensor{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}}let _nextNodeID=0;class Node{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(const layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){const inboundNames=[];for(const layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let _nextLayerID=0;class Layer extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){const prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;const inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndex<inputs.length;inputIndex++){const x=inputs[inputIndex],spec=inputSpec[inputIndex];if(spec==null)continue;const ndim=x.rank;if(spec.ndim!=null&&ndim!==spec.ndim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);if(spec.maxNDim!=null&&ndim>spec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim<spec.minNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);if(spec.dtype!=null&&x.dtype!==spec.dtype)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);if(spec.axes){const xShape=x.shape;for(const key in spec.axes){const axis=Number(key),value=spec.axes[key],xShapeAtAxis=axis>=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i<spec.shape.length;++i){const specDim=spec.shape[i],dim=x.shape[i];if(specDim!=null&&dim!=null&&specDim!==dim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`)}}}call(inputs,kwargs){return inputs}invokeCallHook(inputs,kwargs){this._callHook!=null&&this._callHook(inputs,kwargs)}setCallHook(callHook){this._callHook=callHook}clearCallHook(){this._callHook=null}apply(inputs,kwargs){kwargs=kwargs||{},this.assertNotDisposed();const inputsList=toList(inputs);let allAreSymbolic=!0;for(const input2 of inputsList)if(!(input2 instanceof SymbolicTensor)){allAreSymbolic=!1;break}let noneAreSymbolic=!0;for(const input2 of inputsList)if(input2 instanceof SymbolicTensor){noneAreSymbolic=!1;break}if(allAreSymbolic===noneAreSymbolic)throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");return nameScope(this.name,()=>{if(!this.built){this.assertInputCompatibility(inputs);const inputShapes=[];for(const xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs);const outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{const inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape);let output;const outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape==null)return;if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);const allOutputShapes=[];for(const node of this.inboundNodes){const shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){const outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{const params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;const weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;i<paramValues.length;++i){const pv=paramValues[i],p2=params[i],w=weights[i];if(!util_exports.arraysEqual(pv.shape,w.shape))throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);weightValueTuples.push([p2,w])}batchSetValue(weightValueTuples)})}addWeight(name,shape,dtype,initializer,regularizer,trainable,constraint){if(this._addedWeightNames.indexOf(name)!==-1)throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);this._addedWeightNames.push(name),dtype==null&&(dtype="float32"),this.fastWeightInitDuringBuild&&(initializer=getInitializer("zeros"));const initValue=initializer.apply(shape,dtype),weight=new LayerVariable(initValue,dtype,name,trainable,constraint);return initValue.dispose(),regularizer!=null&&this.addLoss(()=>regularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses8){if(losses8==null||Array.isArray(losses8)&&losses8.length===0)return;losses8=toList(losses8),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses8)}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){const inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);const inboundLayers=[],nodeIndices=[],tensorIndices=[];for(const x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;i<outputTensors.length;i++)outputTensors[i].sourceLayer=this,outputTensors[i].nodeIndex=this.inboundNodes.length-1,outputTensors[i].tensorIndex=i}getConfig(){const config2={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(config2.batchInputShape=this.batchInputShape),this.dtype!=null&&(config2.dtype=this.dtype),config2}disposeWeights(){return this.weights.forEach(weight=>weight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount===0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}}function collectInputShape(inputTensors){inputTensors=toList(inputTensors);const shapes=[];for(const x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor168,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex),layer.inboundNodes.length===0)return[tensor168];{const node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{const sourceTensors=[];for(let i=0;i<node.inboundLayers.length;i++){const x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],previousSources=getSourceInputs(x,layer2,nodeIndex2);for(const x2 of previousSources)sourceTensors.indexOf(x2)===-1&&sourceTensors.push(x2)}return sourceTensors}}}class InputLayer extends Layer{constructor(args){super({dtype:args.dtype,name:args.name!=null?args.name:getUid("input").toString()});if(args.batchSize==null&&(args.batchSize=null),args.sparse==null&&(args.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=args.sparse,args.inputShape!=null&&args.batchInputShape!=null)throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let batchInputShape=args.batchInputShape;if(batchInputShape==null){if(args.inputShape==null)throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");batchInputShape=[args.batchSize].concat(args.inputShape)}else if(args.batchSize!=null)throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");const dtype=args.dtype||"float32";this.batchInputShape=batchInputShape,this.dtype=dtype,this.inputSpec=[{shape:batchInputShape}];const inputTensor=new SymbolicTensor(this.dtype,this.batchInputShape,this,[],{},this.name);inputTensor.nodeIndex=0,inputTensor.tensorIndex=0,new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[inputTensor],outputTensors:[inputTensor],inputMasks:[null],outputMasks:[null],inputShapes:[batchInputShape],outputShapes:[batchInputShape]})}apply(inputs,kwargs){throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}InputLayer.className="InputLayer";serialization_exports.registerClass(InputLayer);function Input(config2){if(config2.batchShape==null&&config2.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(config2.batchShape!=null&&config2.shape!=null)throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let batchShape=config2.batchShape;config2.shape!=null&&batchShape==null&&(batchShape=[null].concat(config2.shape));let dtype=config2.dtype;dtype==null&&(dtype="float32");const inputLayer2=new InputLayer({batchInputShape:batchShape,name:config2.name,dtype,sparse:config2.sparse}),outputs=inputLayer2.inboundNodes[0].outputTensors;return outputs[0]}async function resolveScalarsInLogs(logs5){if(logs5==null)return;const promises=[],keys=[],scalarsToDispose=[];for(const key in logs5){const value=logs5[key];if(typeof value!="number"){const valueScalar=value;promises.push(valueScalar.data()),keys.push(key),scalarsToDispose.push(valueScalar)}}if(promises.length>0){const values=await Promise.all(promises);for(let i=0;i<values.length;++i)logs5[keys[i]]=values[i][0];dispose(scalarsToDispose)}}function disposeTensorsInLogs(logs5){if(logs5==null)return;for(const key in logs5){const value=logs5[key];typeof value!="number"&&value.dispose()}}var ModelLoggingVerbosity;(function(ModelLoggingVerbosity2){ModelLoggingVerbosity2[ModelLoggingVerbosity2.SILENT=0]="SILENT",ModelLoggingVerbosity2[ModelLoggingVerbosity2.VERBOSE=1]="VERBOSE"})(ModelLoggingVerbosity||(ModelLoggingVerbosity={}));const DEFAULT_YIELD_EVERY_MS=125;class BaseCallback{constructor(){this.validationData=null}setParams(params){this.params=params}async onEpochBegin(epoch,logs5){}async onEpochEnd(epoch,logs5){}async onBatchBegin(batch,logs5){}async onBatchEnd(batch,logs5){}async onTrainBegin(logs5){}async onTrainEnd(logs5){}setModel(model2){}}class CallbackList{constructor(callbacks3,queueLength=10){callbacks3==null&&(callbacks3=[]),this.callbacks=callbacks3,this.queueLength=queueLength}append(callback){this.callbacks.push(callback)}setParams(params){for(const callback of this.callbacks)callback.setParams(params)}setModel(model2){for(const callback of this.callbacks)callback.setModel(model2)}async onEpochBegin(epoch,logs5){logs5==null&&(logs5={});for(const callback of this.callbacks)await callback.onEpochBegin(epoch,logs5)}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={});for(const callback of this.callbacks)await callback.onEpochEnd(epoch,logs5)}async onBatchBegin(batch,logs5){logs5==null&&(logs5={});for(const callback of this.callbacks)await callback.onBatchBegin(batch,logs5)}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});for(const callback of this.callbacks)await callback.onBatchEnd(batch,logs5)}async onTrainBegin(logs5){logs5==null&&(logs5={});for(const callback of this.callbacks)await callback.onTrainBegin(logs5)}async onTrainEnd(logs5){logs5==null&&(logs5={});for(const callback of this.callbacks)await callback.onTrainEnd(logs5)}}class BaseLogger extends BaseCallback{constructor(){super()}async onEpochBegin(epoch){this.seen=0,this.totals={}}async onBatchEnd(batch,logs5){logs5==null&&(logs5={});const batchSize=logs5.size==null?0:logs5.size;this.seen+=batchSize;for(const key in logs5){const value=logs5[key];if(typeof value=="number")this.totals.hasOwnProperty(key)||(this.totals[key]=0),this.totals[key]=this.totals[key]+value*batchSize;else{let oldTotalsToDispose;key in this.totals?oldTotalsToDispose=this.totals[key]:this.totals[key]=0;const total=tidy(()=>add2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs5){if(logs5!=null)for(const key of this.params.metrics){if(this.totals[key]==null)continue;typeof this.totals[key]=="number"?logs5[key]=this.totals[key]/this.seen:tidy(()=>{const log10=mul(div(1,this.seen),this.totals[key]);logs5[key]=log10,this.totals[key].dispose(),keep(logs5[key])})}}}class History extends BaseCallback{async onTrainBegin(logs5){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs5){logs5==null&&(logs5={}),this.epoch.push(epoch);for(const key in logs5)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs5[key])}async syncData(){const promises=[],keys=[],indices=[];for(const key in this.history){const valueArray=this.history[key];for(let i=0;i<valueArray.length;++i)if(typeof valueArray[i]!="number"){const valueScalar=valueArray[i];promises.push(valueScalar.data()),keys.push(key),indices.push(i)}}const values=await Promise.all(promises);for(let n=0;n<values.length;++n){const tensorToDispose=this.history[keys[n]][indices[n]];tensorToDispose.dispose(),this.history[keys[n]][indices[n]]=values[n][0]}}}class CustomCallback extends BaseCallback{constructor(args,yieldEvery){super();if(this.currentEpoch=0,this.yieldEvery=yieldEvery||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=DEFAULT_YIELD_EVERY_MS),this.yieldEvery==="never"&&args.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");util_exports.isNumber(this.yieldEvery)&&(this.maybeWait=debounce(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=args.onTrainBegin,this.trainEnd=args.onTrainEnd,this.epochBegin=args.onEpochBegin,this.epochEnd=args.onEpochEnd,this.batchBegin=args.onBatchBegin,this.batchEnd=args.onBatchEnd,this.yield=args.onYield}async maybeWait(epoch,batch,logs5){const ps=[];this.yield!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.yield(epoch,batch,logs5))),ps.push(nextFrame()),await Promise.all(ps)}async onEpochBegin(epoch,logs5){this.currentEpoch=epoch,this.epochBegin!=null&&(await resolveScalarsInLogs(logs5),await this.epochBegin(epoch,logs5))}async onEpochEnd(epoch,logs5){const ps=[];this.epochEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.epochEnd(epoch,logs5))),this.yieldEvery==="epoch"&&ps.push(nextFrame()),await Promise.all(ps)}async onBatchBegin(batch,logs5){this.batchBegin!=null&&(await resolveScalarsInLogs(logs5),await this.batchBegin(batch,logs5))}async onBatchEnd(batch,logs5){const ps=[];this.batchEnd!=null&&(await resolveScalarsInLogs(logs5),ps.push(this.batchEnd(batch,logs5))),this.yieldEvery==="batch"?ps.push(nextFrame()):util_exports.isNumber(this.yieldEvery)&&ps.push(this.maybeWait(this.currentEpoch,batch,logs5)),await Promise.all(ps)}async onTrainBegin(logs5){this.trainBegin!=null&&(await resolveScalarsInLogs(logs5),await this.trainBegin(logs5))}async onTrainEnd(logs5){this.trainEnd!=null&&(await resolveScalarsInLogs(logs5),await this.trainEnd(logs5))}}function standardizeCallbacks(callbacks3,yieldEvery){if(callbacks3==null&&(callbacks3={}),callbacks3 instanceof BaseCallback)return[callbacks3];if(Array.isArray(callbacks3)&&callbacks3[0]instanceof BaseCallback)return callbacks3;const callbackConfigs=toList(callbacks3);return callbackConfigs.map(callbackConfig=>new CustomCallback(callbackConfig,yieldEvery))}class CallbackConstructorRegistry{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(const levelName in CallbackConstructorRegistry.constructors){const constructors=CallbackConstructorRegistry.constructors[+levelName];constructors.forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){const constructors=[];for(const levelName in CallbackConstructorRegistry.constructors){const level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}}CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){const history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks3!=null&&actualCallbacks.push(...callbacks3),actualCallbacks.push(history);const callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config2,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));const squareSum=sum2(square24(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon()),norm5=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm5)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square24(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{const diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{const clippedPred=clipByValue(yPred,epsilon(),Number.MAX_VALUE),firstLog=log(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon(),Number.MAX_VALUE),secondLog=log(add2(1,clippedTrue));return mean(square24(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{const maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square24(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{const maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{const pos=sum2(mul(yTrue,yPred),-1),neg20=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg20,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{const log22=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log22);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{const outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon(),1-epsilon()),neg(sum2(mul(target.toFloat(),log(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{const flatTarget=floor(flatten3(target)).toInt();output=clipByValue(output,epsilon(),1-epsilon());const outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{const reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon(),1-epsilon()),y=log(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{const clippedTrue=clipByValue(yTrue,epsilon(),1),clippedPred=clipByValue(yPred,epsilon(),1);return sum2(mul(yTrue,log(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{const logPred=log(add2(epsilon(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{const trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}const lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{const threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast48(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast48(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{const tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{const tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}const mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(const key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(const key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){const optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon()),Adam:()=>train.adam(.001,.9,.999,epsilon()),Adamax:()=>train.adamax(.002,.9,.999,epsilon(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}const MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){const out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){const keys=Object.keys(x);for(const key of keys){if(typeof key!="string")return!1;if(!plainObjectCheck(x[key]))return!1}return!0}else if(Array.isArray(x)){for(const item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{const xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){const sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(const depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));const layers=model2.layers;for(let i=0;i<layers.length;++i)sequentialLike?printLayerSummary(layers[i],positions,printFn):printLayerSummaryWithConnections(layers[i],positions,relevantNodes,printFn),printFn((i===layers.length-1?"=":"_").repeat(lineLength));model2.checkTrainableWeightsConsistency();const trainableCount=countTrainableParams(model2),nonTrainableCount=countParamsInWeights(model2.nonTrainableWeights);printFn(`Total params: ${trainableCount+nonTrainableCount}`),printFn(`Trainable params: ${trainableCount}`),printFn(`Non-trainable params: ${nonTrainableCount}`),printFn("_".repeat(lineLength))}function countTrainableParams(model2){let trainableCount;return model2.collectedTrainableWeights!=null?trainableCount=countParamsInWeights(model2.collectedTrainableWeights):trainableCount=countParamsInWeights(model2.trainableWeights),trainableCount}function isModelSequentialLike(model2){let sequentialLike=!0;const nodesByDepth=[],nodes=[];for(const depth in model2.nodesByDepth)nodesByDepth.push(model2.nodesByDepth[depth]);for(const depthNodes of nodesByDepth){if(depthNodes.length>1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(const layer of model2.layers){let flag=!1;for(const node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i<fields.length;++i)i>0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}const name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}const connections=[];for(const node of layer.inboundNodes){if(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1)continue;for(let i=0;i<node.inboundLayers.length;++i){const inboundLayer=node.inboundLayers[i].name,inboundLayerIndex=node.nodeIndices[i],inboundTensorIndex=node.tensorIndices[i];connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`)}}const name=layer.name,className=layer.getClassName(),firstConnection=connections.length===0?"":connections[0],fields=[`${name} (${className})`,outputShape,layer.countParams().toString(),firstConnection];printRow(fields,positions,printFn);for(let i=1;i<connections.length;++i)printRow(["","","",connections[i]],positions,printFn)}function isArrayItemInputOrOutputName(key,index,value){return(key==="inboundNodes"||key==="outputLayers"||key==="inputLayers")&&index===0&&typeof value=="string"}function convertPythonicToTs(pythonicConfig,key){if(pythonicConfig===null)return null;if(typeof pythonicConfig=="string")return toCamelCase(pythonicConfig);if(typeof pythonicConfig=="number"||typeof pythonicConfig=="boolean")return pythonicConfig;if(pythonicConfig instanceof Array){const tsArray=[],arrayLength=pythonicConfig.length;for(let i=0;i<arrayLength;++i){const item=pythonicConfig[i];isArrayItemInputOrOutputName(key,i,item)?tsArray.push(item):tsArray.push(convertPythonicToTs(item,key))}return tsArray}else{const tsDict={};for(const pythonicKey of Object.keys(pythonicConfig)){const pythonicValue=pythonicConfig[pythonicKey];if(pythonicKey==="name"&&typeof pythonicValue=="string")tsDict[pythonicKey]=pythonicValue;else{const tsKey=toCamelCase(pythonicKey);tsDict[tsKey]=convertPythonicToTs(pythonicValue,tsKey)}}return tsDict}}function convertTsToPythonic(tsConfig,key){if(tsConfig==null)return null;if(typeof tsConfig=="string")return toSnakeCase(tsConfig);if(typeof tsConfig=="number"||typeof tsConfig=="boolean")return tsConfig;if(tsConfig instanceof Array){const pyArray=[],arrayLength=tsConfig.length;for(let i=0;i<arrayLength;++i){const item=tsConfig[i];isArrayItemInputOrOutputName(key,i,item)?pyArray.push(item):pyArray.push(convertTsToPythonic(item,key))}return pyArray}else{const pyDict={};for(const tsKey of Object.keys(tsConfig)){const tsValue=tsConfig[tsKey],pyKey=toSnakeCase(tsKey);(tsKey==="name"||tsKey==="className")&&typeof tsValue=="string"?pyDict[pyKey]=tsValue:pyDict[pyKey]=convertTsToPythonic(tsValue,tsKey)}return pyDict}}const version2="2.7.0";function assertFeedCompatibility(key,val){if(key.dtype==null||key.dtype===val.dtype)return val;try{return cast(val,key.dtype)}catch(err){throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`)}}class FeedDict{constructor(feeds){if(this.id2Value={},this.id2Mask={},this.name2Id={},feeds instanceof FeedDict)for(const id in feeds.id2Value)this.id2Value[id]=feeds.id2Value[id],id in feeds.id2Mask&&(this.id2Mask[id]=feeds.id2Mask[id]);else{if(feeds==null)return;for(const feed of feeds)this.add(feed.key,feed.value)}}add(key,value,mask){if(this.id2Value[key.id]==null)this.id2Value[key.id]=assertFeedCompatibility(key,value),this.name2Id[key.name]=key.id,mask!=null&&(this.id2Mask[key.id]=mask);else throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);return this}addFeed(feed){this.add(feed.key,feed.value)}hasKey(key){return this.id2Value[key.id]!=null}names(){return Object.keys(this.name2Id)}getValue(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Value[key.id]}else{const id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Value[id]}}getMask(key){if(key instanceof SymbolicTensor){if(this.id2Value[key.id]==null)throw new ValueError(`Nonexistent key: ${key.name}`);return this.id2Mask[key.id]}else{const id=this.name2Id[key];if(id==null)throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);return this.id2Mask[id]}}disposeMasks(){this.id2Mask!=null&&dispose(this.id2Mask)}}const cachedSorted={},cachedRecipientCounts={};function execute(fetches,feedDict,kwargs,probe){const training5=kwargs==null?!1:kwargs.training,arrayFetches=Array.isArray(fetches),fetchArray=arrayFetches?fetches:[fetches],outputNames=fetchArray.map(t=>t.name),finalOutputs=[],feedNames=feedDict.names();for(const outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);const fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(",");let sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){const out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training5||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);const internalFeedDict=new FeedDict(feedDict);for(let i=0;i<sorted.length;++i){if(probe!=null){const numTensors=memory().numTensors;numTensors>probe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors<probe.minNumTensors&&(probe.minNumTensors=numTensors)}const symbolic=sorted[i],srcLayer=symbolic.sourceLayer;if(srcLayer instanceof InputLayer)continue;const inputValues=[],inputMasks=[],tensorsToDispose=[];let maskExists=!1;for(const input2 of symbolic.inputs){const value=internalFeedDict.getValue(input2),mask=internalFeedDict.getMask(input2);inputValues.push(value),inputMasks.push(mask),mask!=null&&(maskExists=!0),training5||(recipientCounts[input2.name]--,recipientCounts[input2.name]===0&&!feedDict.hasKey(input2)&&outputNames.indexOf(input2.name)===-1&&!value.isDisposed&&input2.sourceLayer.stateful!==!0&&tensorsToDispose.push(value))}maskExists&&(kwargs=kwargs||{},kwargs.mask=inputMasks[0]);const outputTensors=toList(srcLayer.apply(inputValues,kwargs));let outputMask=null;srcLayer.supportsMasking&&(outputMask=srcLayer.computeMask(inputValues,inputMasks));const layerOutputs=getNodeOutputs(symbolic),outputSymbolicTensors=Array.isArray(layerOutputs)?layerOutputs:[layerOutputs];for(let i2=0;i2<outputSymbolicTensors.length;++i2){internalFeedDict.hasKey(outputSymbolicTensors[i2])||internalFeedDict.add(outputSymbolicTensors[i2],outputTensors[i2],Array.isArray(outputMask)?outputMask[0]:outputMask);const index=outputNames.indexOf(outputSymbolicTensors[i2].name);index!==-1&&(finalOutputs[index]=outputTensors[i2])}training5||dispose(tensorsToDispose)}return internalFeedDict.disposeMasks(),arrayFetches?finalOutputs:finalOutputs[0]}function getTopologicalSortAndRecipientCounts(fetches,feedDict){util_exports.assert(fetches!=null&&fetches.length>0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){const out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{const visited=new Set;for(const fetch3 of fetches){const{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(const symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(const name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){const recipientCounts={};for(const name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){const visited=new Set,sorted=[],recipientMap={};for(const key of feedDict.names())visited.add(key);const stack9=[],marks=[];for(stack9.push(fetch3);stack9.length>0;){const top=stack9[stack9.length-1];if(visited.has(top.name)){stack9.pop();continue}const topIsMarked=marks[marks.length-1]===stack9.length-1;if(top.inputs.length===0||topIsMarked)stack9.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack9.length-1);for(const input2 of top.inputs){if(recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),visited.has(input2.name))continue;stack9.push(input2)}}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;i<fetch3.sourceLayer.inboundNodes.length;++i)for(const outputTensor of fetch3.sourceLayer.inboundNodes[i].outputTensors)if(outputTensor.id===fetch3.id){nodeIndex=i;break}layerOutputs=fetch3.sourceLayer.getOutputAt(nodeIndex)}return layerOutputs}class Container extends Layer{constructor(args){super({});if(this.containerNodes=new Set,this.name=args.name,this.name==null){const prefix=this.getClassName().toLowerCase();this.name=getUid(prefix)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(args.inputs)?this.inputs=args.inputs.slice():this.inputs=[args.inputs],Array.isArray(args.outputs)?this.outputs=args.outputs.slice():this.outputs=[args.outputs],unique5(this.inputs).length!==this.inputs.length)throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(x=>x.name)}`);unique5(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(const x of this.outputs){const layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(const x of this.inputs){const layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;i<this.inputLayers.length;i++){const layer=this.inputLayers[i];if(!(layer instanceof InputLayer))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);this.inputNames.push(layer.name),this.feedInputShapes.push(layer.batchInputShape),this.feedInputNames.push(layer.name)}for(const layer of this.outputLayers)this.outputNames.push(layer.name);this.internalInputShapes=this.inputs.map(x=>x.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);const nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor168,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor168.sourceLayer,nodeIndex=tensor168.nodeIndex,tensorIndex=tensor168.tensorIndex);const node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor168.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);const numInboundLayers=node.inboundLayers.length;for(let i=0;i<numInboundLayers;i++){const x=node.inputTensors[i],layer2=node.inboundLayers[i],nodeIndex2=node.nodeIndices[i],tensorIndex2=node.tensorIndices[i];buildMapOfGraph(x,finishedNodes2,nodesInProgress2,layer2,nodeIndex2,tensorIndex2)}for(finishedNodes2.push(node);nodesInProgress2.indexOf(node)>=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(const x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);const reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(const node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id];const previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;i<node.inboundLayers.length;i++){const inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],inboundNode=inboundLayer.inboundNodes[nodeIndex],previousDepth2=nodesDepths[inboundNode.id]==null?0:nodesDepths[inboundNode.id];nodesDepths[inboundNode.id]=Math.max(depth+1,previousDepth2),nodeIDToNode[inboundNode.id]=inboundNode}}const nodesByDepth={};for(const nodeID in nodesDepths){const depth=nodesDepths[nodeID];depth in nodesByDepth||(nodesByDepth[depth]=[]),nodesByDepth[depth].push(nodeIDToNode[nodeID])}const layersByDepth={};for(const layerID in layersDepths){const depth=layersDepths[layerID];depth in layersByDepth||(layersByDepth[depth]=[]),layersByDepth[depth].push(layerIDToLayer[layerID])}let depthKeys=Object.keys(layersByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(const depth of depthKeys){const layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{const aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndex<bIndex?-1:aIndex>bIndex?1:0});for(const layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);const computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(const depth of depthKeys)for(const node of nodesByDepth[depth]){const layer=node.outboundLayer;if(layer!=null){for(const x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(const x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;const allNames=this.layers.map(x=>x.name);for(const name of allNames){const numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();const result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(const layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(const container2 of this.internalContainerRefs)result.numDisposedVariables+=container2.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(const layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){const weights=[];for(const layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){const trainableWeights=[];for(const layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){const nameToWeight={};let totalWeightsCount=0;for(const layer of this.layers)for(const weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}const weightValueTuples=[];for(const name in weights){let validatedName=name;if(nameToWeight[name]==null){const tokens=name.split("/"),shortenNameArray=tokens.slice(0,-2).concat([tokens[tokens.length-1]]);validatedName=shortenNameArray.join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){const unsetNames=[];for(const name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){const theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version2}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){const modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);const feedDict=new FeedDict;for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i]);return execute(this.outputs,feedDict,kwargs)})}computeMask(inputs,mask){return tidy(()=>{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){const inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);const layersToOutputShapes={};for(let i=0;i<inputShapes.length;i++){const layer=this.inputLayers[i],inputShape2=inputShapes[i],shapeKey=layer.name+"_0_0";layersToOutputShapes[shapeKey]=inputShape2}const depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(const depth of depthKeys){const nodes=this.nodesByDepth[depth];for(const node of nodes){const layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;const inputShapes2=[];for(let j=0;j<node.inboundLayers.length;j++){const inboundLayer=node.inboundLayers[j],nodeIndex2=node.nodeIndices[j],tensorIndex=node.tensorIndices[j],shapeKey=`${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`,inputShape2=layersToOutputShapes[shapeKey];inputShapes2.push(inputShape2)}const outputShape=layer.computeOutputShape(singletonOrArray(inputShapes2)),outputShapes2=normalizeShapeList(outputShape),nodeIndex=layer.inboundNodes.indexOf(node);for(let j=0;j<outputShapes2.length;j++){const shapeKey=`${layer.name}_${nodeIndex}_${j}`;layersToOutputShapes[shapeKey]=outputShapes2[j]}}}const outputShapes=[],outputShapeKeys=[];for(let i=0;i<this.outputLayers.length;i++){const layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],tensorIndex=this.outputLayersTensorIndices[i],shapeKey=`${layer.name}_${nodeIndex}_${tensorIndex}`;outputShapeKeys.push(shapeKey)}for(let i=0;i<outputShapeKeys.length;i++){const key=outputShapeKeys[i];assert2(key in layersToOutputShapes),outputShapes.push(layersToOutputShapes[key])}return singletonOrArray(outputShapes)}runInternalGraph(inputs,masks){masks==null&&(masks=pyListRepeat(null,inputs.length));const tensorMap={};for(let i=0;i<this.inputs.length;++i){const x=this.inputs[i],y=inputs[i],mask=masks[i];tensorMap[x.id]=[y,mask]}const depthKeys=Object.keys(this.nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);for(const depth of depthKeys){const nodes=this.nodesByDepth[depth];for(const node of nodes){const layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(const x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){const[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i<referenceOutputTensors.length;++i){const x=referenceOutputTensors[i],y=outputTensors2[i],mask=outputMasks2[i];tensorMap[x.id]=[y,mask]}}}}const outputTensors=[],outputMasks=[],outputShapes=[];for(const x of this.outputs){assert2(x.id in tensorMap,`Could not compute output ${x.name} : ${x.id}`);const[tensor168,mask]=tensorMap[x.id];outputShapes.push(tensor168.shape),outputTensors.push(tensor168),outputMasks.push(mask)}return[outputTensors,outputMasks,outputShapes]}buildNodeConversionMap(layers){const nodeConversionMap={};let keptNodes;for(const layer of this.layers){keptNodes=layer instanceof Container?1:0;for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){const nodeKey=Container.nodeKey(layer,originalNodeIndex);this.containerNodes.has(nodeKey)&&(nodeConversionMap[nodeKey]=keptNodes,keptNodes+=1)}}return nodeConversionMap}getLayer(name,index){if(index!=null){if(this.layers.length<=index)throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);return this.layers[index]}else if(name==null)throw new ValueError("Provide either a layer name or layer index");for(const layer of this.layers)if(layer.name===name)return layer;throw new ValueError(`No such layer: ${name}`)}calculateLosses(){return tidy(()=>{const losses8=[];for(const layer of this.layers)for(let nodeIndex=0;nodeIndex<layer.inboundNodes.length;++nodeIndex){const nodeKey=Container.nodeKey(layer,nodeIndex);this.containerNodes.has(nodeKey)&&losses8.push(...layer.calculateLosses())}return losses8})}getConfig(){const config2={name:this.name},nodeConversionMap=this.buildNodeConversionMap(this.layers),layerConfigs=[];for(const layer of this.layers){const layerClassName=layer.getClassName(),layerConfig=layer.getConfig(),filteredInboundNodes=[];for(let originalNodeIndex=0;originalNodeIndex<layer.inboundNodes.length;originalNodeIndex++){const node=layer.inboundNodes[originalNodeIndex],nodeKey=Container.nodeKey(layer,originalNodeIndex);let kwargs={};if(this.containerNodes.has(nodeKey)){if(node.callArgs)try{JSON.stringify(node.callArgs),kwargs=node.callArgs}catch(err){console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),kwargs={}}if(node.inboundLayers.length>0){const nodeData=[];for(let i=0;i<node.inboundLayers.length;i++){const inboundLayer=node.inboundLayers[i],nodeIndex=node.nodeIndices[i],tensorIndex=node.tensorIndices[i],nodeKey2=Container.nodeKey(inboundLayer,nodeIndex);let newNodeIndex=nodeConversionMap[nodeKey2];newNodeIndex==null&&(newNodeIndex=0),nodeData.push([inboundLayer.name,newNodeIndex,tensorIndex,kwargs])}filteredInboundNodes.push(nodeData)}}}const dict={};dict.name=layer.name,dict.className=layerClassName,dict.config=layerConfig,dict.inboundNodes=filteredInboundNodes,layerConfigs.push(dict)}config2.layers=layerConfigs;const modelInputs=[];for(let i=0;i<this.inputLayers.length;i++){const layer=this.inputLayers[i],nodeIndex=this.inputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);const tensorIndex=this.inputLayersTensorIndices[i];modelInputs.push([layer.name,newNodeIndex,tensorIndex])}config2.inputLayers=modelInputs;const modelOutputs=[];for(let i=0;i<this.outputLayers.length;i++){const layer=this.outputLayers[i],nodeIndex=this.outputLayersNodeIndices[i],nodeKey=Container.nodeKey(layer,nodeIndex);if(!this.containerNodes.has(nodeKey))continue;let newNodeIndex=nodeConversionMap[nodeKey];newNodeIndex==null&&(newNodeIndex=0);const tensorIndex=this.outputLayersTensorIndices[i];modelOutputs.push([layer.name,newNodeIndex,tensorIndex])}return config2.outputLayers=modelOutputs,config2}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){const createdLayers={},unprocessedNodes={};function addUnprocessedNode(layer,nodeData){layer.name in unprocessedNodes?unprocessedNodes[layer.name].push(nodeData):unprocessedNodes[layer.name]=[nodeData]}function processNode(layer,nodeData){const inputTensors2=[];let kwargs;for(const inputData of nodeData){const inboundLayerName=inputData[0],inboundNodeIndex=inputData[1],inboundTensorIndex=inputData[2];if(kwargs=inputData[3]==null?{}:inputData[3],!(inboundLayerName in createdLayers)){addUnprocessedNode(layer,nodeData);return}const inboundLayer=createdLayers[inboundLayerName];if(inboundLayer.inboundNodes.length<=inboundNodeIndex){addUnprocessedNode(layer,nodeData);return}const inboundNode=inboundLayer.inboundNodes[inboundNodeIndex];inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex])}inputTensors2.length>0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){const layerName=layerData.name,layer=deserialize(layerData,config2.customObjects!=null?config2.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer;const inboundNodesData=layerData.inboundNodes;inboundNodesData.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}const name=config2.name,layersFromConfig=config2.layers;for(const layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(const layerData of layersFromConfig){const layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){const currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(const nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}const inputTensors=[],outputTensors=[],inputLayersFromConfig=config2.inputLayers;for(const layerData of inputLayersFromConfig){const layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);const layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}const outputLayersFromConfig=config2.outputLayers;for(const layerData of outputLayersFromConfig){const layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);const layer=createdLayers[layerName],layerOutputTensors=layer.inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(const layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}}function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){const numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){const output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){const yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){const axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);const classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses8,sampleWeights){return mul(losses8,sampleWeights)}const DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys;const iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);const flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex<flattenedXs.length;xIndex++)util_exports.assert(flattenedXs[xIndex].shape[0]===batchSize,()=>`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex<flattenedYs.length;yIndex++)util_exports.assert(flattenedYs[yIndex].shape[0]===batchSize,()=>`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{const result=[];for(const name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset5,args){const hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{const doValidation=args.validationData!=null;let valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{const validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}const trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames();let callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();const callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks3,verbose,args.epochs,null,null,getStepsPerEpoch(dataset5,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset5.iterator();for(;epoch<args.epochs;){const epochLogs={};await callbackList.onEpochBegin(epoch);let stepsDone=0,batchIndex=0;for(hasBatchesPerEpoch||(dataIterator=await dataset5.iterator());hasBatchesPerEpoch?stepsDone<args.batchesPerEpoch:!0;){const iteratorOut=await dataIterator.next();if(hasBatchesPerEpoch&&iteratorOut.done){console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch*args.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(iteratorOut.value!=null){const{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),batchLogs={};batchLogs.batch=batchIndex,batchLogs.size=xs[0].shape[0],await callbackList.onBatchBegin(batchIndex,batchLogs);const sampleWeights=[];if(args.classWeight!=null){const standardClassWeights=standardizeClassWeights(args.classWeight,model2.outputNames);for(let i=0;i<standardClassWeights.length;++i)sampleWeights.push(await standardizeWeights(ys[i],null,standardClassWeights[i]))}const ins=xs.concat(ys).concat(sampleWeights),outs=trainFunction(ins);dispose(ins);for(let i=0;i<outLabels.length;++i){const label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),batchIndex++,stepsDone++}if(hasBatchesPerEpoch?stepsDone>=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i<model2.metricsNames.length;++i)epochLogs[`val_${model2.metricsNames[i]}`]=valOuts[i]}break}if(model2.stopTraining_)break}if(await callbackList.onEpochEnd(epoch,epochLogs),epoch++,model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}finally{model2.isTraining=!1}}function getStepsPerEpoch(dataset5,args){let stepsPerEpoch=null;return args.batchesPerEpoch!=null?stepsPerEpoch=args.batchesPerEpoch:Number.isFinite(dataset5.size)&&(stepsPerEpoch=dataset5.size),stepsPerEpoch}function isDatasetObject(dataset5){return typeof dataset5.iterator=="function"}function isLazyIteratorObject(iterator){return typeof iterator.next=="function"}async function evaluateDataset(model2,dataset5,args){args=args||{};const hasBatches=args.batches!=null,f=model2.testFunction;let outs=[];if(args.verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);const dataIterator=isLazyIteratorObject(dataset5)?dataset5:await dataset5.iterator();let numExamples=0,batch=0;for(;hasBatches?batch<args.batches:!0;){const iteratorOut=await dataIterator.next();if(outs=tidy(()=>{if(iteratorOut.value){const{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));const batchSize=xsAndYs[0].shape[0];for(let i=0;i<batchOuts.length;++i){const batchOut=batchOuts[i],oldScalar=outs[i];outs[i]=tidy(()=>add2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i<outs.length;++i){const oldScalar=outs[i];outs[i]=div(outs[i],numExamples),dispose(oldScalar)}return singletonOrArray(outs)}function checkBatchSize(batchSize){util_exports.assert(batchSize>0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather7(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){const output=[];let batchStart=0,batchEnd=null;for(;batchStart<size;)batchEnd=batchStart+batchSize,batchEnd>=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks3,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");const numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch");let indexArray;numTrainSamples!=null&&(indexArray=range4(0,numTrainSamples)),verbose==null&&(verbose=1);const{callbackList,history}=configureCallbacks(callbacks3,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch<epochs;++epoch){await callbackList.onEpochBegin(epoch);const epochLogs={};if(stepsPerEpoch!=null)throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");{if(shuffle2==="batch")throw new NotImplementedError("batch shuffling is not implemneted yet");shuffle2&&util_exports.shuffle(indexArray);const epochIndexArray1D=tensor1d(indexArray),batches=makeBatches(numTrainSamples,batchSize);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){const batchLogs={};if(await callbackList.onBatchBegin(batchIndex,batchLogs),tidy(()=>{const batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;const insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i<outLabels.length;++i){const label=outLabels[i],out=outs[i];batchLogs[label]=out,keep(out)}if(batchIndex===batches.length-1&&doValidation){const valOuts=model2.testLoop(valF,valIns,batchSize);for(let i=0;i<outLabels.length;++i){const label=outLabels[i],out=valOuts[i];keep(out),epochLogs["val_"+label]=out}}}),await callbackList.onBatchEnd(batchIndex,batchLogs),disposeTensorsInLogs(batchLogs),model2.stopTraining_)break}epochIndexArray1D.dispose()}if(await callbackList.onEpochEnd(epoch,epochLogs),model2.stopTraining_)break}return await callbackList.onTrainEnd(),await model2.history.syncData(),model2.history}async function fitTensors(model2,x,y,args={}){if(model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;let inputs,targets,inputValX,inputValY,valX,valY,sampleWeights;try{const batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);const checkBatchAxis=!1,standardizedOuts=await model2.standardizeUserData(x,y,args.sampleWeight,args.classWeight,checkBatchAxis,batchSize);inputs=standardizedOuts[0],targets=standardizedOuts[1],sampleWeights=standardizedOuts[2];let doValidation=!1,valIns;if(args.validationData!=null&&args.validationData.length>0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);const checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;const splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);const ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();const trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames();let valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());const callbacks3=standardizeCallbacks(args.callbacks,args.yieldEvery),out=await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks3,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null);return out}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){const outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;i<tensors.length;++i){const tensor168=tensors[i];if(tensor168.rank===1)outs.push(expandDims2(tensor168,1));else{if(tensor168.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");outs.push(tensor168)}}return outs}function disposeNewTensors(tensors,refTensors){if(tensors==null)return;const oldTensorIds=[];if(refTensors instanceof Tensor)oldTensorIds.push(refTensors.id);else if(Array.isArray(refTensors))refTensors.forEach(t=>oldTensorIds.push(t.id));else if(refTensors!=null)for(const name in refTensors){const oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}const tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(const name in tensors){const tensor168=tensors[name];oldTensorIds.indexOf(tensor168.id)===-1&&tensorsToDispose.push(tensor168)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(const key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(const name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;const array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;const dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim>=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){const setX=unique5(inputs.map(input2=>input2.shape[0]));setX.sort();const setY=unique5(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){const keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i<targets.length;++i){const y=targets[i],loss=lossFns[i],shape=outputShapes[i];if(loss==null)continue;if(loss===categoricalCrossentropy&&y.shape[y.shape.length-1]===1)throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(keyLosses.indexOf(loss)!==-1){const slicedYShape=y.shape.slice(1),slicedShape=shape.slice(1);for(let j=0;j<slicedYShape.length;++j){const targetDim=slicedYShape[j],outDim=slicedShape[j];if(outDim!=null&&targetDim!==outDim)throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`)}}}}function checkInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){let arrays;if(Array.isArray(data2)){if(data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data2.length} Tensors(s).`);arrays=data2}else{if(names.length>1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i<names.length;++i){if(shapes[i]==null)continue;const array2=arrays[i];if(array2.shape.length!==shapes[i].length)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);for(let j=0;j<shapes[i].length;++j){if(j===0&&!checkBatchAxis)continue;const dim=array2.shape[j],refDim=shapes[i][j];if(refDim!=null&&refDim!==dim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`)}}}function collectMetrics(metrics2,outputNames){if(metrics2==null||Array.isArray(metrics2)&&metrics2.length===0)return outputNames.map(name=>[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{const nestedMetrics=[];for(const name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}const LAYERS_MODEL_FORMAT_NAME="layers-model";class LayersModel extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(const name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(const name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);const theLosses=args.loss;lossFunctions=theLosses.map(l=>get(l))}else{const lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){const shape=this.internalOutputShapes[i],name=this.outputNames[i];this.feedOutputNames.push(name),this.feedOutputShapes.push(shape),this.feedLossFns.push(this.lossFunctions[i])}const skipTargetIndices=[];this.metrics=args.metrics,this.metricsNames=["loss"],this.metricsTensors=[],nameScope("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;const weightedLoss=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});const nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(skipTargetIndices.indexOf(i)!==-1)continue;const outputMetrics=nestedMetrics[i],handleMetrics=metrics2=>{const metricNamePrefix="";let metricName,accFn,weightedMetricFn;for(const metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){const outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else{const metricFn=get2(metric);weightedMetricFn=metricFn,metricName=metricNamePrefix+getLossOrMetricName(metric)}let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}};handleMetrics(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){if(this.collectedTrainableWeights==null)return;this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){const batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);const checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{const ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();const f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset5,args){return this.makeTestFunction(),evaluateDataset(this,dataset5,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");const outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)feedDict.add(this.inputs[i],inputs[i])}else for(const input2 of this.inputs){const tensorValue=inputs[input2.name];if(tensorValue==null)throw new ValueError(`No value is provided for the model's input ${input2.name}`);feedDict.add(input2,tensorValue)}const executeOutputs=execute(outputSymbolicTensors,feedDict);return outputsIsArray?executeOutputs:executeOutputs[0]}retrieveSymbolicTensors(symbolicTensorNames){const outputSymbolicTensors=pyListRepeat(null,symbolicTensorNames.length);let outputsRemaining=symbolicTensorNames.length;for(const layer of this.layers){const layerOutputs=Array.isArray(layer.output)?layer.output:[layer.output],layerOutputNames=layerOutputs.map(output=>output.name);for(let i=0;i<symbolicTensorNames.length;++i){const index=layerOutputNames.indexOf(symbolicTensorNames[i]);if(index!==-1&&(outputSymbolicTensors[i]=layerOutputs[index],outputsRemaining--),outputsRemaining===0)break}if(outputsRemaining===0)break}if(outputsRemaining>0){const remainingNames=[];throw outputSymbolicTensors.forEach((tensor168,i)=>{tensor168==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{const numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");const batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex<batches.length;++batchIndex){const batchOuts=tidy(()=>{const batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;i<insBatch.length;++i)feeds.push({key:this.inputs[i],value:insBatch[i]});else feeds.push({key:this.inputs[0],value:insBatch});const feedDict=new FeedDict(feeds);return execute(this.outputs,feedDict)});batchOuts.forEach((batchOut,i)=>outsBatches[i].push(batchOut))}return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){const xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{const batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);const batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");const outputShapes=[];for(let i=0;i<this.feedOutputShapes.length;++i){const outputShape=this.feedOutputShapes[i],lossFn=this.feedLossFns[i];lossFn===sparseCategoricalCrossentropy?outputShapes.push(outputShape.slice(0,outputShape.length-1).concat([1])):outputShapes.push(outputShape)}if(x=standardizeInputData(x,this.feedInputNames,this.feedInputShapes,!1,"input"),y=standardizeInputData(y,this.feedOutputNames,outputShapes,!1,"target"),checkArrayLengths(x,y,null),checkLossAndTargetCompatibility(y,this.feedLossFns,this.feedOutputShapes),this.stateful&&batchSize!=null&&batchSize>0&&x[0].shape[0]%batchSize!==0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){const[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){const classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i<classWeights.length;++i)standardSampleWeights.push(await standardizeWeights(standardYs[i],null,classWeights[i]))}return[standardXs,standardYs,standardSampleWeights]}testLoop(f,ins,batchSize,verbose=0,steps){return tidy(()=>{const numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{const batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range4(0,numSamples));for(let batchIndex=0;batchIndex<batches.length;++batchIndex){const batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(indexArray,batchStart,batchEnd-batchStart),insBatch=sliceArraysByIndices(ins,batchIds),batchOuts=f(insBatch);if(batchIndex===0)for(let i=0;i<batchOuts.length;++i)outs.push(scalar(0));for(let i=0;i<batchOuts.length;++i){const batchOut=batchOuts[i];outs[i]=add2(outs[i],mul(batchEnd-batchStart,batchOut))}}for(let i=0;i<outs.length;++i)outs[i]=div(outs[i],numSamples)}return outs})}getDedupedMetricsNames(){const outLabels=this.metricsNames,dedupedOutLabels=[];for(let i=0;i<outLabels.length;++i){const label=outLabels[i];let newLabel=label;if(count(outLabels,label)>1){const dupIndex=count(outLabels.slice(0,i),label);newLabel+=`_${dupIndex}`}dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{const lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{const feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});const feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict,{training:!0});let totalLoss;for(let i=0;i<this.lossFunctions.length;++i){const lossFunction=this.lossFunctions[i];let loss=lossFunction(targets[i],outputs[i]);sampleWeights[i]!=null&&(loss=computeWeightedLoss2(loss,sampleWeights[i]));const meanLoss=mean(loss);lossValues.push(meanLoss),i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss)}for(let i=0;i<this.metricsTensors.length;++i){let weightedMetric;if(this.outputs.length>1&&i<this.outputs.length)weightedMetric=lossValues[i];else{const metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1];weightedMetric=mean(metric(targets[outputIndex],outputs[outputIndex]))}keep(weightedMetric),metricsValues.push(weightedMetric)}return totalLoss=mean(totalLoss),this.calculateLosses().forEach(regularizerLoss=>{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables5=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0,totalLossValue=this.optimizer_.minimize(totalLossFunction,returnCost,variables5);return[totalLossValue].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{const valOutputs=[];let totalLoss;const inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;i<this.inputs.length;++i)feeds.push({key:this.inputs[i],value:inputs[i]});const feedDict=new FeedDict(feeds),outputs=execute(this.outputs,feedDict);for(let i=0;i<this.lossFunctions.length;++i){const lossFunction=this.lossFunctions[i],loss=mean(lossFunction(targets[i],outputs[i]));i===0?totalLoss=loss:totalLoss=add2(totalLoss,loss),valOutputs.push(totalLoss)}for(let i=0;i<this.metricsTensors.length;++i){const metric=this.metricsTensors[i][0],outputIndex=this.metricsTensors[i][1],meanMetric=mean(metric(targets[outputIndex],outputs[outputIndex]));valOutputs.push(meanMetric)}return valOutputs})}async fit(x,y,args={}){return fitTensors(this,x,y,args)}async fitDataset(dataset5,args){return fitDataset(this,dataset5,args)}async trainOnBatch(x,y){const standardizeOut=await this.standardizeUserData(x,y),inputs=standardizeOut[0],targets=standardizeOut[1],trainFunction=this.makeTrainFunction(),losses8=trainFunction(inputs.concat(targets)),lossValues=[];for(const loss of losses8){const v=await loss.data();lossValues.push(v[0])}return dispose(losses8),singletonOrArray(lossValues)}getNamedWeights(config2){const namedWeights=[],trainableOnly=config2!=null&&config2.trainableOnly,weights=trainableOnly?this.trainableWeights:this.weights,weightValues=this.getWeights(trainableOnly);for(let i=0;i<weights.length;++i){if(trainableOnly&&!weights[i].trainable)continue;namedWeights.push({name:weights[i].originalName,tensor:weightValues[i]})}return namedWeights}set stopTraining(stop){this.stopTraining_=stop}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(optimizer7){this.optimizer_!==optimizer7&&(this.optimizer_=optimizer7,this.isOptimizerOwned=!1)}dispose(){const result=super.dispose();if(result.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){const numTensorsBeforeOptmizerDisposal=memory().numTensors;this.optimizer_.dispose(),result.numDisposedVariables+=numTensorsBeforeOptmizerDisposal-memory().numTensors}return result}getLossIdentifiers(){let lossNames;if(typeof this.loss=="string")lossNames=toSnakeCase(this.loss);else if(Array.isArray(this.loss)){for(const loss of this.loss)if(typeof loss!="string")throw new Error("Serialization of non-string loss is not supported.");lossNames=this.loss.map(name=>toSnakeCase(name))}else{const outputNames=Object.keys(this.loss);lossNames={};const losses8=this.loss;for(const outputName of outputNames)if(typeof losses8[outputName]=="string")lossNames[outputName]=toSnakeCase(losses8[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{const metricsIdentifiers={};for(const key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");const tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer7=deserialize(tsConfig);let loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(const key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(const key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer:optimizer7})}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){const handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");const weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config2)),returnString=!1,unusedArg=null,modelConfig=this.toJSON(unusedArg,returnString),modelArtifacts={modelTopology:modelConfig,format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version2}`,convertedBy:null},includeOptimizer=config2==null?!1:config2.includeOptimizer;if(includeOptimizer&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();const weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){const checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}}LayersModel.className="Model";serialization_exports.registerClass(LayersModel);class Functional extends LayersModel{}Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);const tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){const weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(const weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){const handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const artifacts=await handler.load();let modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);const strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");const{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer11,specs){const name2Tensor=io_exports.decodeWeights(buffer11,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}class Sequential extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(const layer of args.layers)this.add(layer)}checkShape(layer){const shape=layer.inboundNodes[0].outputTensors[0].shape;if(shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){const isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel;let modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");const x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{const outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{const lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset5,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer7){this.model.optimizer=optimizer7}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset5,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset5,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config2 instanceof Array){if(!(config2[0].className!=null)||config2[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config2}else util_exports.assert(config2.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config2.layers,delete config2.layers,extraModelConfig=config2;const model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(const conf of configArray){const customObjects2=void 0,layer=deserialize(conf,customObjects2,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){const layers=[];for(const layer of this.layers){const dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}}Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config2){return new Sequential(config2)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config2){return Input(config2)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}class Activation extends serialization_exports.Serializable{getConfig(){return{}}}class Elu2 extends Activation{apply(x,alpha=1){return elu6(x,alpha)}}Elu2.className="elu";serialization_exports.registerClass(Elu2);class Selu2 extends Activation{apply(x){return selu(x)}}Selu2.className="selu";serialization_exports.registerClass(Selu2);class Relu2 extends Activation{apply(x){return relu(x)}}Relu2.className="relu";serialization_exports.registerClass(Relu2);class Relu62 extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}}Relu62.className="relu6";serialization_exports.registerClass(Relu62);class Linear extends Activation{apply(x){return x}}Linear.className="linear";serialization_exports.registerClass(Linear);class Sigmoid2 extends Activation{apply(x){return sigmoid(x)}}Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);class HardSigmoid extends Activation{apply(x){return hardSigmoid(x)}}HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);class Softplus2 extends Activation{apply(x){return softplus(x)}}Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);class Softsign extends Activation{apply(x){return softsign(x)}}Softsign.className="softsign";serialization_exports.registerClass(Softsign);class Tanh2 extends Activation{apply(x){return tanh2(x)}}Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);class Softmax2 extends Activation{apply(x,axis=-1){return softmax(x,axis)}}Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);class LogSoftmax2 extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}}LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);class Swish extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}}Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){const config2={};return config2.className="linear",config2.config={},deserializeActivation(config2)}if(typeof identifier=="string"){const config2={};return config2.className=identifier,config2.config={},deserializeActivation(config2)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}class Regularizer extends serialization_exports.Serializable{}class L1L2 extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square24(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config2){return new cls({l1:config2.l1,l2:config2.l2})}}L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}const REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){const className=identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config2={className,config:{}};return deserializeRegularizer(config2)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}class ReLU extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){const config2={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}ReLU.className="ReLU";serialization_exports.registerClass(ReLU);class LeakyReLU extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){const x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){const config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);class PReLU extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);const paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(const i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const axes={};if(this.sharedAxes!=null)for(let i=1;i<inputShape.length;++i)axes[i]=inputShape[i];this.inputSpec=[new InputSpec({ndim:inputShape.length,axes})],this.built=!0}call(inputs,kwargs){return inputs=getExactlyOneTensor(inputs),prelu(inputs,this.alpha.read())}getConfig(){const config2={alphaInitializer:serializeInitializer(this.alphaInitializer),alphaRegularizer:serializeRegularizer(this.alphaRegularizer),alphaConstraint:serializeConstraint(this.alphaConstraint),sharedAxes:this.sharedAxes},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}PReLU.className="PReLU";serialization_exports.registerClass(PReLU);class ELU extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA=1,args==null&&(args={}),args.alpha!=null&&args.alpha!==this.DEFAULT_ALPHA)throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){const x=getExactlyOneTensor(inputs);return elu(x)}computeOutputShape(inputShape){return inputShape}getConfig(){const config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}ELU.className="ELU";serialization_exports.registerClass(ELU);class ThresholdedReLU extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_THETA=1,args==null&&(args={}),this.theta=args.theta==null?this.DEFAULT_THETA:args.theta}call(inputs,kwargs){const x=getExactlyOneTensor(inputs);return x.mul(cast48(x.greater(this.theta),"float32"))}computeOutputShape(inputShape){return inputShape}getConfig(){const config2={theta:this.theta},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}ThresholdedReLU.className="ThresholdedReLU";serialization_exports.registerClass(ThresholdedReLU);class Softmax3 extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_AXIS=1,args==null&&(args={}),this.softmax=new Softmax2().apply,this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis}call(inputs,kwargs){const x=getExactlyOneTensor(inputs);return this.softmax(x,this.axis)}computeOutputShape(inputShape){return inputShape}getConfig(){const config2={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Softmax3.className="Softmax";serialization_exports.registerClass(Softmax3);function normalizeArray(value,n,name){if(typeof value=="number")return pyListRepeat(value,n);if(value.length!==n)throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);for(let i=0;i<n;++i){const singleValue=value[i];if(!isInteger(singleValue))throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`)}return value}function convOutputLength(inputLength,filterSize,padding2,stride,dilation=1){if(inputLength==null)return inputLength;const dilatedFilterSize=filterSize+(filterSize-1)*(dilation-1);let outputLength;return padding2==="same"?outputLength=inputLength:outputLength=inputLength-dilatedFilterSize+1,Math.floor((outputLength+stride-1)/stride)}function deconvLength(dimSize,strideSize,kernelSize,padding2){if(dimSize==null)return null;if(padding2==="valid")dimSize=dimSize*strideSize+max8([kernelSize-strideSize,0]);else if(padding2==="same")dimSize=dimSize*strideSize;else throw new ValueError(`Unsupport padding mode: ${padding2}.`);return dimSize}function preprocessConv2DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding2="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding2==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding2="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding2==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding2==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding2==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}class BaseConv extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){const config2={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}class Conv extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);const channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);const inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs;const biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i<space.length;++i){const newDim=convOutputLength(space[i],this.kernelSize[i],this.padding,this.strides[i],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[i]);newSpace.push(newDim)}let outputShape=[inputShape[0]];return this.dataFormat==="channelsLast"?(outputShape=outputShape.concat(newSpace),outputShape.push(this.filters)):(outputShape.push(this.filters),outputShape=outputShape.concat(newSpace)),outputShape}getConfig(){const config2={filters:this.filters,kernelInitializer:serializeInitializer(this.kernelInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static verifyArgs(args){if(!("filters"in args)||typeof args.filters!="number"||args.filters<1)throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`)}}class Conv2D2 extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){const config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}}Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);class Conv3D2 extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){const config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}}Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);class Conv2DTranspose extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));const channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");const inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);const inputShape=input2.shape,batchSize=inputShape[0];let hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);const height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const outputShape=inputShape.slice();let channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);const kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){const config2=super.getConfig();return delete config2.dilationRate,config2}}Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);class SeparableConv extends Conv{constructor(rank,config2){super(rank,config2);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config2.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config2.kernelInitializer!=null||config2.kernelRegularizer!=null||config2.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config2.padding!=null&&config2.padding!=="same"&&config2.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);this.depthMultiplier=config2.depthMultiplier==null?1:config2.depthMultiplier,this.depthwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config2.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config2.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config2.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config2.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<this.rank+2)throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(inputShape)}`);const channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);const inputDim=inputShape[channelAxis],depthwiseKernelShape=this.kernelSize.concat([inputDim,this.depthMultiplier]),pointwiseKernelShape=[];for(let i=0;i<this.rank;++i)pointwiseKernelShape.push(1);pointwiseKernelShape.push(inputDim*this.depthMultiplier,this.filters);const trainable=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,trainable,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",pointwiseKernelShape,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,trainable,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,trainable,this.biasConstraint):this.bias=null,this.inputSpec=[new InputSpec({ndim:this.rank+2,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){const config2=super.getConfig();return delete config2.rank,delete config2.kernelInitializer,delete config2.kernelRegularizer,delete config2.kernelConstraint,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config2.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config2}}SeparableConv.className="SeparableConv";class SeparableConv2D extends SeparableConv{constructor(args){super(2,args)}}SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);class Conv1D extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){const config2=super.getConfig();return delete config2.rank,delete config2.dataFormat,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}}Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);class Cropping2D extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){const hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{const hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){const config2={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);class UpSampling2D extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){const height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{const height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);const inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);const height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=input2.resizeNearestNeighbor([height,width]);return transpose(resized,[0,3,1,2])}else{const height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return input2.resizeNearestNeighbor([height,width])}})}getConfig(){const config2={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d3(x,depthwiseKernel,strides=[1,1],padding2="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding2==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}class DepthwiseConv2D extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);const channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);const inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d3(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){const config2=super.getConfig();return config2.depthMultiplier=this.depthMultiplier,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config2}}DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{const ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);const axes=[1,0].concat(range4(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));const perStepOutputs=[];let lastOutput,states=initialStates;const timeSteps=inputs.shape[0],perStepInputs=unstack(inputs);let perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;t<timeSteps;++t){const currentInput=perStepInputs[t],stepOutputs=tidy(()=>stepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{const maskedOutputs=tidy(()=>{const stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state6,i)=>stepOutputs[1][i].mul(stepMask).add(state6.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;if(needPerStepOutputs){const axis=1;outputs=stack(perStepOutputs,axis)}return[lastOutput,outputs,states]})}class RNN extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){const numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range4(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);const outputDim=stateSize[0];let outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){const stateShape=[];for(const dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);const outputMask=this.returnSequences?mask:null;if(this.returnState){const stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){const numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;i<numStates;++i)output.push(null);return output}else return this.states_}set states(s){this.states_=s}build(inputShape){const constantShape=null;if(this.numConstants!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;const batchSize=this.stateful?inputShape[0]:null,inputDim=inputShape.slice(2);this.inputSpec[0]=new InputSpec({shape:[batchSize,null,...inputDim]});const stepInputShape=[inputShape[0]].concat(inputShape.slice(2));if(constantShape!=null)throw new NotImplementedError("Constants support is not implemented in RNN yet.");this.cell.build(stepInputShape);let stateSize;if(Array.isArray(this.cell.stateSize)?stateSize=this.cell.stateSize:stateSize=[this.cell.stateSize],this.stateSpec!=null){if(!util_exports.arraysEqual(this.stateSpec.map(spec=>spec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");const batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){const value=states[index],dim=Array.isArray(this.cell.stateSize)?this.cell.stateSize[index]:this.cell.stateSize,expectedShape=[batchSize,dim];if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});const standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(const state6 of initialState)this.stateSpec.push(new InputSpec({shape:state6.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length);const isTensor=additionalInputs[0]instanceof SymbolicTensor;if(isTensor){const fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;const output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{const mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training;let initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));const numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const cellCallKwargs={training:training5},step9=(inputs2,states2)=>{const outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},rnnOutputs=rnn(step9,inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training5);const output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims2(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile8(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile8(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){const baseConfig=super.getConfig(),config2={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config2.numConstants=this.numConstants);const cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config2.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){const cellConfig=config2.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config2,{cell}))}}RNN.className="RNN";serialization_exports.registerClass(RNN);class RNNCell extends Layer{}class SimpleRNNCell extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];const training5=kwargs.training==null?!1:kwargs.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(prevOutput),rate:this.recurrentDropout,training:training5}));let h;const dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot5(mul(inputs,dpMask),this.kernel.read()):h=dot5(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot5(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){const baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config2)}}SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);class SimpleRNN extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return new cls(config2)}}SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);class GRUCell extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);const inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);const training5=kwargs.training==null?!1:kwargs.training;let hTMinus1=inputs[1];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:3}));const dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;let z,r,hh;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let matrixX=dot5(inputs,this.kernel.read());this.useBias&&(matrixX=biasAdd(matrixX,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0]));const recurrentKernelValue=this.recurrentKernel.read(),[rk1,rk2]=split(recurrentKernelValue,[2*this.units,this.units],recurrentKernelValue.rank-1),matrixInner=dot5(hTMinus1,rk1),[xZ,xR,xH]=split(matrixX,3,matrixX.rank-1),[recurrentZ,recurrentR]=split(matrixInner,2,matrixInner.rank-1);z=this.recurrentActivation.apply(add2(xZ,recurrentZ)),r=this.recurrentActivation.apply(add2(xR,recurrentR));const recurrentH=dot5(mul(r,hTMinus1),rk2);hh=this.activation.apply(add2(xH,recurrentH));const h=add2(mul(z,hTMinus1),mul(add2(1,neg(z)),hh));return[h,h]})}getConfig(){const baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},baseConfig,config2)}}GRUCell.className="GRUCell";serialization_exports.registerClass(GRUCell);class GRU extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new GRUCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}}GRU.className="GRU";serialization_exports.registerClass(GRU);class LSTMCell extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min6([1,max8([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min6([1,max8([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);const inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){const capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){const bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{const training5=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1];const cTMinus1=inputs[2];inputs=inputs[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(inputs),rate:this.dropout,training:training5,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:4}));const dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;let i,f,c,o;0<this.dropout&&this.dropout<1&&(inputs=mul(inputs,dpMask[0]));let z=dot5(inputs,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(hTMinus1=mul(hTMinus1,recDpMask[0])),z=add2(z,dot5(hTMinus1,this.recurrentKernel.read())),this.useBias&&(z=biasAdd(z,this.bias.read()));const[z0,z1,z2,z3]=split(z,4,z.rank-1);i=this.recurrentActivation.apply(z0),f=this.recurrentActivation.apply(z1),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(z2))),o=this.recurrentActivation.apply(z3);const h=mul(o,this.activation.apply(c));return[h,h,c]})}getConfig(){const baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),recurrentActivation:serializeActivation(this.recurrentActivation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},baseConfig,config2)}}LSTMCell.className="LSTMCell";serialization_exports.registerClass(LSTMCell);class LSTM extends RNN{constructor(args){args.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),args.cell=new LSTMCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}}LSTM.className="LSTM";serialization_exports.registerClass(LSTM);class StackedRNNCells extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){const stateSize=[];for(const cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1);const nestedStates=[];for(const cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();const newNestedStates=[];let callInputs;for(let i=0;i<this.cells.length;++i){const cell=this.cells[i];states=nestedStates[i],i===0?callInputs=[inputs[0]].concat(states):callInputs=[callInputs[0]].concat(states),callInputs=cell.call(callInputs,kwargs),newNestedStates.push(callInputs.slice(1))}states=[];for(const cellStates of newNestedStates.slice().reverse())states.push(...cellStates);return[callInputs[0]].concat(states)})}build(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let outputDim;this.cells.forEach((cell,i)=>{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){const baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),cellConfigs=this.cells.map(getCellConfig),config2={cells:cellConfigs};return Object.assign({},baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){const cells=[];for(const cellConfig of config2.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];const weights=[];for(const cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){const weights=[];for(const cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){const trainableWeights=[];for(const cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){const weights=[];for(const cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){const tuples=[];for(const cell of this.cells){const numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;i<cell.weights.length;++i)tuples.push([cell.weights[i],inputWeights[i]])}batchSetValue(tuples)}}StackedRNNCells.className="StackedRNNCells";serialization_exports.registerClass(StackedRNNCells);function generateDropoutMask(args){const{ones:ones9,rate,training:training5=!1,count:count2=1}=args,droppedInputs=()=>dropout2(ones9(),rate),createMask=()=>inTrainPhase(droppedInputs,ones9,training5);if(!count2||count2<=1)return keep(createMask().clone());const masks=Array(count2).fill(void 0).map(createMask);return masks.map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i<p2.length;i++)e.indexOf(p2[i])<0&&Object.prototype.propertyIsEnumerable.call(s,p2[i])&&(t[p2[i]]=s[p2[i]]);return t};class ConvRNN2D extends RNN{constructor(args){if(args.unroll)throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(args.cell))throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");super(args);this.inputSpec=[new InputSpec({ndim:5})]}call(inputs,kwargs){return tidy(()=>{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");const mask=kwargs==null?null:kwargs.mask,training5=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training:training5,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{const{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training5=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");const inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],batchSize=inputShape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training5?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;index<this.states_.length;++index){const value=states[index],expectedShape=stateShape;if(!util_exports.arraysEqual(value.shape,expectedShape))throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);this.states_[index]=value}}this.states_=this.states_.map(state6=>keep(state6.clone()))})}computeSingleOutputShape(inputShape){const{dataFormat,filters,kernelSize,padding:padding2,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding2,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding2,strides[1],dilationRate[1]),outShape=[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]];return outShape}}ConvRNN2D.className="ConvRNN2D";class ConvLSTM2DCell extends LSTMCell{constructor(args){const{filters,kernelSize,strides,padding:padding2,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding2||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);const channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);const inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){const init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){const biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);const training5=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=generateDropoutMask({ones:()=>onesLike(x),rate:this.dropout,training:training5,count:numOfKernels}));const dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2);let xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=generateDropoutMask({ones:()=>onesLike(hTMinus1),rate:this.recurrentDropout,training:training5,count:numOfKernels}));const recDropoutMask=this.recurrentDropoutMask;let hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3);const kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);const[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);const i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){const _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config2={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config2)}inputConv(x,w,b,padding2){const out=conv2d(x,w,this.strides,padding2||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){const strides=1;return conv2d(x,w,strides,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);class ConvLSTM2D extends ConvRNN2D{constructor(args){const cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config2){return new cls(config2)}}ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);class Dropout extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;const inputShape=input2.shape,noiseShape=[];for(let i=0;i<this.noiseShape.length;++i)noiseShape.push(this.noiseShape[i]==null?inputShape[i]:this.noiseShape[i]);return noiseShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs);if(0<this.rate&&this.rate<1){const training5=kwargs.training==null?!1:kwargs.training,noiseShape=this.getNoiseShape(input2),output=inTrainPhase(()=>dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training5);return output}return inputs})}getConfig(){const config2={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}dispose(){return super.dispose()}}Dropout.className="Dropout";serialization_exports.registerClass(Dropout);class SpatialDropout1D extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){const inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}}SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);class Dense extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);const inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());let output;return fusedActivationName!=null?output=dot5(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot5(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){const config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Dense.className="Dense";serialization_exports.registerClass(Dense);class Flatten extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(const dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){const permutation=[0];for(let i=2;i<input2.rank;++i)permutation.push(i);permutation.push(1),input2=input2.transpose(permutation)}return batchFlatten(input2)})}getConfig(){const config2={};this.dataFormat!=null&&(config2.dataFormat=this.dataFormat);const baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Flatten.className="Flatten";serialization_exports.registerClass(Flatten);class Activation2 extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.activation=getActivation(args.activation)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){const config2={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Activation2.className="Activation";serialization_exports.registerClass(Activation2);class RepeatVector extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){const config2={n:this.n},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);class Reshape2 extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i<this.targetShape.length;++i)this.isUnknown(this.targetShape[i])&&(this.targetShape[i]=null)}isUnknown(dim){return dim<0||dim==null}fixUnknownDimension(inputShape,outputShape){const errorMsg="Total size of new array must be unchanged.",finalShape=outputShape.slice();let known=1,unknown=null;for(let i=0;i<finalShape.length;++i){const dim=finalShape[i];if(this.isUnknown(dim))if(unknown===null)unknown=i;else throw new ValueError("Can only specifiy one unknown dimension.");else known*=dim}const originalSize=arrayProd(inputShape);if(unknown!==null){if(known===0||originalSize%known!==0)throw new ValueError(errorMsg);finalShape[unknown]=originalSize/known}else if(originalSize!==known)throw new ValueError(errorMsg);return finalShape}computeOutputShape(inputShape){let anyUnknownDims=!1;for(let i=0;i<inputShape.length;++i)if(this.isUnknown(inputShape[i])){anyUnknownDims=!0;break}return anyUnknownDims?inputShape.slice(0,1).concat(this.targetShape):inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape))}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){const config2={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);class Permute extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);const expectedSortedIndices=range4(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){const config2={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Permute.className="Permute";serialization_exports.registerClass(Permute);class Masking extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){const baseConfig=super.getConfig(),config2={maskValue:this.maskValue};return Object.assign(config2,baseConfig),config2}computeMask(inputs,mask){const input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims),output=input2.mul(booleanMask.asType(input2.dtype));return output})}}Masking.className="Masking";serialization_exports.registerClass(Masking);class Embedding extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];const inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k<inLens.length;++k){const s1=inLens[k],s2=inputShape[k+1];if(s1!=null&&s2!=null&&s1!==s2)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);s1==null&&(inLens[i]=s2),i++}}return[inputShape[0],...inLens,this.outputDim]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);input2.dtype!=="int32"&&(input2=cast48(input2,"int32"));const output=gather7(this.embeddings.read(),input2.as1D());return output.reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){const config2={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Embedding.className="Embedding";serialization_exports.registerClass(Embedding);class Merge extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length<shape2.length)return this.computeElementwiseOpOutputShape(shape2,shape1);if(shape2.length===0)return shape1;const outputShape=shape1.slice(0,shape1.length-shape2.length);for(let k=0;k<shape2.length;++k){const i=shape1[shape1.length-shape2.length+k],j=shape2[k];if(i==null||j==null||i<0||j<0)outputShape.push(null);else if(i===1)outputShape.push(j);else if(j===1)outputShape.push(i);else{if(i!==j)throw new ValueError("Operands could not be broadcast together with shapes "+JSON.stringify(shape1)+" "+JSON.stringify(shape2));outputShape.push(i)}}return outputShape}build(inputShape){if(Array.isArray(inputShape)&&!Array.isArray(inputShape[0])&&(inputShape=[getExactlyOneShape(inputShape)]),inputShape=inputShape,inputShape.length<2)throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);let batchSizes=[];for(const shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);if(batchSizes=unique5(batchSizes),batchSizes.length>1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){const shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}const allRanks=inputShape.map(shape=>shape.length);inputShape.indexOf(null)===-1&&unique5(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){const reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){const maxNDim=max8(inputDims);for(let x of inputs){const xNDim=x.rank;for(let k=0;k<maxNDim-xNDim;++k)x=expandDims2(x,1);reshapedInputs.push(x)}return this.mergeFunction(reshapedInputs)}else{let transposed=!1;for(const x of inputs){const xNDim=x.rank;if(xNDim==null){const xShape=x.shape,batchSize=xShape[0],newShape=xShape.slice(1).concat([batchSize]);let xTransposed=x.reshape([batchSize].concat(arrayProd(xShape.slice(1))));xTransposed=transpose(xTransposed,[1,0]),xTransposed=xTransposed.reshape(newShape),reshapedInputs.push(xTransposed),transposed=!0}else if(xNDim>1){const dims=range4(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs);const yNDim=y.rank;if(transposed){if(yNDim==null){const yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){const dims=[yNDim-1].concat(range4(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i<inputShape.length;++i){const shape=inputShape[i]==null?null:inputShape[i].slice(1);outputShape=this.computeElementwiseOpOutputShape(outputShape,shape)}let batchSizes=[];for(const shape of inputShape)shape!=null&&shape[0]!==null&&batchSizes.push(shape[0]);return batchSizes=unique5(batchSizes),batchSizes.length===1?outputShape=batchSizes.concat(outputShape):outputShape=[null].concat(outputShape),outputShape}computeMask(inputs,mask){return tidy(()=>{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i<mask.length-1;++i)output=logicalAnd(output,mask[i]);return output})}}class Add2 extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return output})}}Add2.className="Add";serialization_exports.registerClass(Add2);class Multiply2 extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=mul(output,inputs[i]);return output})}}Multiply2.className="Multiply";serialization_exports.registerClass(Multiply2);class Average extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0].clone();for(let i=1;i<inputs.length;++i)output=add2(output,inputs[i]);return mul(1/inputs.length,output)})}}Average.className="Average";serialization_exports.registerClass(Average);class Maximum2 extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=maximum(output,inputs[i]);return output})}}Maximum2.className="Maximum";serialization_exports.registerClass(Maximum2);class Minimum2 extends Merge{constructor(args){super(args)}mergeFunction(inputs){return tidy(()=>{let output=inputs[0];for(let i=1;i<inputs.length;++i)output=minimum(output,inputs[i]);return output})}}Minimum2.className="Minimum";serialization_exports.registerClass(Minimum2);class Concatenate extends Merge{constructor(args){super(args);this.DEFAULT_AXIS=-1,args==null&&(args={}),this.axis=args.axis==null?this.DEFAULT_AXIS:args.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0]))||inputShape.length===1)throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");inputShape=inputShape;let allNoneShape=!0;for(const shape of inputShape)if(shape!=null){allNoneShape=!1;break}if(allNoneShape)return;const shapeSet=[];for(let i=0;i<inputShape.length;++i){const shapeWithoutConcatAxis=inputShape[i].slice();shapeWithoutConcatAxis.splice(this.axis,1);let exists=!1;for(const shape of shapeSet)if(util_exports.arraysEqual(shape,shapeWithoutConcatAxis)){exists=!0;break}exists||shapeSet.push(shapeWithoutConcatAxis)}if(shapeSet.length>1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");const inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(const shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;const outputMasks=[];for(let i=0;i<inputs.length;++i)mask[i]==null?outputMasks.push(onesLike(inputs[i]).asType("bool")):mask[i].rank<inputs[i].rank?outputMasks.push(expandDims(mask[i],-1)):outputMasks.push(mask[i]);const concatenatedMasks=concat(outputMasks,this.axis);return all(concatenatedMasks,-1,!1)})}getConfig(){const config2={axis:this.axis},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Concatenate.className="Concatenate";serialization_exports.registerClass(Concatenate);function interpretAxis(axis,dim){for(;axis<0;)axis+=dim;return axis}function batchDot(x,y,axes){if(x.shape.length>3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");const xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);const axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;const diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);y=y.reshape(y.shape.concat(diffShape))}else if(yNDim>xNDim){diff=yNDim-xNDim;const diffShape=[];for(let i=0;i<diff;++i)diffShape.push(1);x=x.reshape(x.shape.concat(diffShape))}else diff=0;let out;if(x.shape.length===2&&y.shape.length===2)axesArray[0]===axesArray[1]?out=x.mul(y).sum(axesArray[0]):out=x.transpose([1,0]).mul(y).sum(axesArray[1]);else{const adjX=axesArray[0]!==x.shape.length-1,adjY=axesArray[1]===y.shape.length-1;out=x.matMul(y,adjX,adjY)}if(diff>0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;const squeezeAxes=[];for(let i=idx;i<idx+diff;++i)squeezeAxes.push(i);out=out.squeeze(squeezeAxes)}return out.shape.length===1&&(out=out.expandDims(1)),out})}class Dot extends Merge{constructor(args){super(args);this.axes=args.axes,this.normalize=args.normalize==null?!1:args.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");const axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");const shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");const axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);const outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){const config2={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}Dot.className="Dot";serialization_exports.registerClass(Dot);class GaussianNoise extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){const baseConfig=super.getConfig(),config2={stddev:this.stddev};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs),noised=()=>randomNormal2(input2.shape,0,this.stddev).add(input2),output=inTrainPhase(noised,()=>input2,kwargs.training||!1);return output})}}GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);class GaussianDropout extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){const baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);const input2=getExactlyOneTensor(inputs);if(this.rate>0&&this.rate<1){const noised=()=>{const stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))};return inTrainPhase(noised,()=>input2,kwargs.training||!1)}return input2})}}GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);class AlphaDropout extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){const baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){const noiseShape=this._getNoiseShape(inputs),droppedInputs=()=>{const input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2;let keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast48(keptIdx,"float32");const a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate,x=input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP));return x.mul(a).add(b)};return inTrainPhase(droppedInputs,()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}}AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean7,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean7,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean7,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean7,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{const meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,normed=batchNormalization(x,mean7,variance,beta,gamma,epsilon3);return[normed,mean7,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{const meanAndVariance=moments(x,reductionAxes),mean7=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(const axis of range4(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);const broadcastMean=mean7.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape),normed=batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3);return[normed,mean7,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range4(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}class BatchNormalization extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);const axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];const shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{const training5=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range4(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);const broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];const sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();const needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range4(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){const broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training5)return normalizeInference();const[normedTraining,mean7,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable3,value,momentum)=>{tidy(()=>{const decay=1-momentum,origValue=variable3.read(),updateDelta=origValue.sub(value).mul(decay);variable3.write(origValue.sub(updateDelta))})},updateMovingMeanAndVariance=()=>{doMovingAverage(this.movingMean,mean7,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)};return updateMovingMeanAndVariance(),normedTraining})}getConfig(){const config2={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);class LayerNormalization extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(const axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);const nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i<this.axis.length;++i)this.axis[i]<0&&(this.axis[i]+=nDims);for(const axis of this.axis)if(axis<0||axis>=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique5(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){const input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{const keepDims=!0;let{mean:mean7,variance}=moments(input2,this.axis,keepDims);const broadcastShape=pyListRepeat(1,nDims);for(const dim of this.axis)broadcastShape[dim]=inputShape[dim];const broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v;let scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read());const momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i<nDims;++i)this.axis.indexOf(i)!==-1?(momentsTiling.push(inputShape[i]),scaleOffsetTiling.push(1)):(momentsTiling.push(1),scaleOffsetTiling.push(inputShape[i]));return mean7=mean7.tile(momentsTiling),variance=variance.tile(momentsTiling),scale2=scale2.tile(scaleOffsetTiling),offset=offset.tile(scaleOffsetTiling),batchNormalization(input2,mean7,variance,offset,scale2,this.epsilon)})}getConfig(){const config2={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}LayerNormalization.className="LayerNormalization";serialization_exports.registerClass(LayerNormalization);function spatial2dPadding(x,padding2,dataFormat){return tidy(()=>{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding2==null&&(padding2=[[1,1],[1,1]]),padding2.length!==2||padding2[0].length!==2||padding2[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding2[0],padding2[1]]:pattern=[[0,0],padding2[0],padding2[1],[0,0]],pad(x,pattern)})}class ZeroPadding2D extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){const config2={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y;const paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d(x,poolSize,strides,padding2,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding2),strides==null&&(strides=[1,1,1]),padding2==null&&(padding2="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y;const paddingString=padding2==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}class Pooling1D extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims2(getExactlyOneTensor(inputs),2);const output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){const config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}class MaxPooling1D extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}}MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);class AveragePooling1D extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}}AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);class Pooling2D extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}class MaxPooling2D extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"max")}}MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);class AveragePooling2D extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool2d(inputs,poolSize,strides,padding2,dataFormat,"avg")}}AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);class Pooling3D extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){const config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}class MaxPooling3D extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"max")}}MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);class AveragePooling3D extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding2,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding2),pool3d(inputs,poolSize,strides,padding2,dataFormat,"avg")}}AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);class GlobalPooling1D extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}}class GlobalAveragePooling1D extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{const input2=getExactlyOneTensor(inputs);return mean(input2,1)})}}GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);class GlobalMaxPooling1D extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{const input2=getExactlyOneTensor(inputs);return max(input2,1)})}}GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);class GlobalPooling2D extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){const config2={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}}class GlobalAveragePooling2D extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{const input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}}GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);class GlobalMaxPooling2D extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{const input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}}GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);class Wrapper extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){const config2={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config2,customObjects={}){const layerConfig=config2.layer,layer=deserialize(layerConfig,customObjects);delete config2.layer;const newConfig={layer};return Object.assign(newConfig,config2),new cls(newConfig)}}class TimeDistributed extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];const childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);const childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);const step9=(inputs2,states)=>{const output=getExactlyOneTensor(this.layer.call(inputs2,kwargs));return[output,[]]},rnnOutputs=rnn(step9,inputs,[],!1,null,null,!1,!0),y=rnnOutputs[1];return y})}}TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}const DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat";class Bidirectional extends Wrapper{constructor(args){super(args);const layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=!(layerConfig.goBackwards===!0);const backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){const numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});const standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);const additionalInputs=[],additionalSpecs=[];if(initialState!=null){const numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);const stateSpecs=initialState.map(state6=>new InputSpec({shape:state6.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");const isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(const tensor168 of additionalInputs)if(tensor168 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){const fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;const output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{const initialState=kwargs.initialState;let y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{const forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){const states=this.forwardLayer.states,stateMask=states.map(state6=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){const config2={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static fromConfig(cls,config2){const rnnLayer=deserialize(config2.layer);if(delete config2.layer,config2.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");const newConfig=config2;return newConfig.layer=rnnLayer,new cls(newConfig)}}Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu7(args){return new ELU(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu6(args){return new PReLU(args)}function softmax4(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d5(args){return new Conv1D(args)}function conv2d10(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d3(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d4(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten4(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape87(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add31(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum9(args){return new Maximum2(args)}function minimum7(args){return new Minimum2(args)}function multiply(args){return new Multiply2(args)}function dot6(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}const globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}const exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}const exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});const exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config2){return new L1L2(config2)}function l12(config2){return l1(config2)}function l22(config2){return l2(config2)}class Callback extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}}function less7(currVal,prevVal){return currVal<prevVal}function greater11(currVal,prevVal){return currVal>prevVal}class EarlyStopping extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less7:this.mode==="max"?this.monitorFunc=greater11:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater11:this.monitorFunc=less7,this.monitorFunc===less7&&(this.minDelta*=-1)}async onTrainBegin(logs5){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less7?Infinity:-Infinity}async onEpochEnd(epoch,logs5){await resolveScalarsInLogs(logs5);const current=this.getMonitorValue(logs5);if(current==null)return;this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0))}async onTrainEnd(logs5){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs5){logs5==null&&(logs5={});const monitorValue=logs5[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs5)}`),monitorValue}}function earlyStopping(args){return new EarlyStopping(args)}const callbacks={earlyStopping};var DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));const CUSTOM_OPS={};function registerOp(name,opFunc){const opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){const inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){const start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors"){const inputs=node.inputNames.slice(start,end);return inputs.map(name=>getTensor(name,tensorMap,context,resourceManager))}const tensor168=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor168.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor168.shape,data2)}const attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){const[nodeName,index]=parseNodeName(name);if(resourceManager!=null){const tensor168=resourceManager.getHashTableHandleByName(nodeName);if(tensor168!=null)return tensor168}const contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){const[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){const parts=name.split(":");if(parts.length===1)return[name,0];const nodeName=parts[0];return[nodeName,Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad11=getParamValue("pad",node,tensorMap,context);if(pad11==="explicit"){pad11=getParamValue("explicitPaddings",node,tensorMap,context);const explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad11[i*2],explicitPadding[i][1]=pad11[i*2+1];return explicitPadding}return pad11}function cloneTensor(tensor168){return tensor168.kept?tensor168:clone(tensor168)}const arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});const json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});const json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"clip_value_min",name:"clipValueMin",type:"number"},{tfName:"clip_value_max",name:"clipValueMax",type:"number"}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"clipValueMin",name:"clipValueMin",type:"number",defaultValue:0},{tfName:"clipValueMax",name:"clipValueMax",type:"number",defaultValue:6}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});const 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json4=[{tfOpName:"AvgPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPoolWithArgmax",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"include_batch_in_index",name:"includeBatchInIndex",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AvgPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MaxPool3D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"ksize",name:"kernelSize",type:"number[]"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Conv1D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"stride",name:"stride",type:"number"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NWC"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"dilation",name:"dilation",type:"number",defaultValue:1}]},{tfOpName:"Conv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"useCudnnOnGpu",name:"useCudnnOnGpu",type:"bool"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"_FusedConv2D",category:"convolution",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"use_cudnn_on_gpu",name:"useCudnnOnGpu",type:"bool",defaultValue:!0},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"dilations",name:"dilations",type:"number[]",defaultValue:[1,1,1,1]},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4}]},{tfOpName:"Conv2DBackpropInput",category:"convolution",inputs:[{start:2,name:"x",type:"tensor"},{start:1,name:"filter",type:"tensor"},{start:0,name:"outputShape",type:"number[]"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]}]},{tfOpName:"DepthwiseConv2d",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName:"DepthwiseConv2dNative",category:"convolution",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"filter",type:"tensor"}],attrs:[{tfName:"strides",name:"strides",type:"number[]"},{tfName:"padding",name:"pad",type:"string"},{tfName:"data_format",name:"dataFormat",type:"string",defaultValue:"NHWC"},{tfName:"explicit_paddings",name:"explicitPaddings",type:"number[]",defaultValue:[]},{tfName:"dilations",name:"dilations",type:"number[]"}]},{tfOpName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json5=[{tfOpName:"Fill",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"},{start:1,name:"value",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"LinSpace",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"num",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"OneHot",category:"creation",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"depth",type:"number"},{start:2,name:"onValue",type:"number",defaultValue:1},{start:3,name:"offValue",type:"number",defaultValue:0}],attrs:[{tfName:"axis",name:"axis",type:"number",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ones",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"OnesLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"RandomUniform",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number",defaultValue:0},{tfName:"maxval",name:"maxval",type:"number",defaultValue:1},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Range",category:"creation",inputs:[{start:0,name:"start",type:"number"},{start:1,name:"stop",type:"number"},{start:2,name:"step",type:"number",defaultValue:0}],attrs:[{tfName:"Tidx",name:"dtype",type:"dtype"}]},{tfOpName:"TruncatedNormal",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"means",name:"mean",type:"number",defaultValue:0},{tfName:"stddev",name:"stdDev",type:"number",defaultValue:1},{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,notSupported:!0},{tfName:"dtype",name:"dtype",type:"dtype"},{tfName:"T",name:"T",type:"number",notSupported:!0}]},{tfOpName:"Zeros",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"ZerosLike",category:"creation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype"}]},{tfOpName:"Multinomial",category:"creation",inputs:[{start:0,name:"logits",type:"tensor"},{start:1,name:"numSamples",type:"number"}],attrs:[{tfName:"seed",name:"seed",type:"number"},{tfName:"seed2",name:"seed2",type:"number"},{tfName:"T",name:"dtype",type:"dtype"},{tfName:"output_dtype",name:"output_dtype",type:"dtype"}]}],dynamic_exports={};__export2(dynamic_exports,{json:()=>json6});const json6=[{tfOpName:"NonMaxSuppressionV2",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV3",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}]},{tfOpName:"NonMaxSuppressionV4",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"T_threshold",name:"threshold",type:"dtype",notSupported:!0},{tfName:"pad_to_max_output_size",name:"padToMaxOutputSize",type:"bool"}]},{tfOpName:"NonMaxSuppressionV5",category:"dynamic",inputs:[{start:0,name:"boxes",type:"tensor"},{start:1,name:"scores",type:"tensor"},{start:2,name:"maxOutputSize",type:"number"},{start:3,name:"iouThreshold",type:"number"},{start:4,name:"scoreThreshold",type:"number"},{start:5,name:"softNmsSigma",type:"number"}]},{tfOpName:"Where",category:"dynamic",inputs:[{start:0,name:"condition",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ListDiff",category:"dynamic",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],evaluation_exports={};__export2(evaluation_exports,{json:()=>json7});const json7=[{tfOpName:"TopKV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"k",type:"number"}],attrs:[{tfName:"sorted",name:"sorted",type:"bool"}]},{tfOpName:"Unique",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"UniqueV2",category:"evaluation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]}],graph_exports={};__export2(graph_exports,{json:()=>json8});const json8=[{tfOpName:"PlaceholderWithDefault",category:"graph",inputs:[{start:0,name:"default",type:"tensor"}],attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Placeholder",category:"graph",attrs:[{tfName:"shape",name:"shape",type:"shape"},{tfName:"dtype",name:"dtype",type:"dtype"}]},{tfOpName:"Const",category:"graph"},{tfOpName:"Identity",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IdentityN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Snapshot",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Rank",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Size",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"Shape",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"ShapeN",category:"graph",inputs:[{start:0,end:0,name:"x",type:"tensors"}]},{tfOpName:"Print",category:"graph",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"data",type:"tensors"}],attrs:[{tfName:"message",name:"message",type:"string"},{tfName:"first_n",name:"firstN",type:"number",notSupported:!0},{tfName:"summarize",name:"summarize",type:"number",defaultValue:3}]},{tfOpName:"NoOp",category:"graph",inputs:[]},{tfOpName:"StopGradient",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"FakeQuantWithMinMaxVars",category:"graph",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"min",name:"min",type:"number"},{tfName:"max",name:"max",type:"number"}]}],hash_table_exports={};__export2(hash_table_exports,{json:()=>json9});const json9=[{tfOpName:"HashTable",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"HashTableV2",category:"hash_table",inputs:[],attrs:[{tfName:"shared_name",name:"sharedName",type:"string"},{tfName:"use_node_name_sharing",name:"useNodeNameSharing",type:"bool"},{tfName:"key_dtype",name:"keyDType",type:"dtype"},{tfName:"value_dtype",name:"valueDType",type:"dtype"}]},{tfOpName:"LookupTableImport",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableImportV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"values",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFind",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]},{tfOpName:"LookupTableFindV2",category:"hash_table",inputs:[{start:0,name:"tableHandle",type:"tensor"},{start:1,name:"keys",type:"tensor"},{start:2,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"Tin",name:"tIn",type:"dtype",notSupported:!0},{tfName:"Tout",name:"tOut",type:"dtype",notSupported:!0}]}],image_exports={};__export2(image_exports,{json:()=>json10});const json10=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}],logical_exports={};__export2(logical_exports,{json:()=>json11});const json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});const json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});const json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});const json14=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});const json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});const json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});const json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];class OperationMapper{static get Instance(){return this._instance||(this._instance=new this)}constructor(){const ops69=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops69.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){const tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{});let inputs=[];const outputs=[];let inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));const allNodes=Object.keys(nodes);allNodes.forEach(key=>{const node=nodes[key];node.inputNames.forEach(name=>{const[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{const node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{const[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{const[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));const result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){const mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});const newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{const type=param.type;let value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){const tfNodes=functionDef.nodeDef,placeholders=[],weights=[];let nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));const inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{const[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node});const allNodes=Object.keys(nodes);allNodes.forEach(key=>{const node=nodes[key];node.inputNames.forEach(name=>{const[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});const returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{const[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});const signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}}function decodeBase64(text){const global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){const value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){const param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){const param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){const param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){typeof value=="string"&&(value=DataType[value]);switch(value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){const param=attrs[name];return param&&param.func?param.func.name:def}function getDtypeParam(attrs,name,def){const param=attrs[name];return param&&param.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){const param=attrs[name];return param&&param.list&&param.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){return shape.unknownRank?void 0:shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){const param=attrs[name];return param&&param.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){const param=attrs[name];return param?((param.list.f&&param.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){const param=attrs[name];return param&&param.list&&param.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){const param=attrs[name];return param&&param.list&&param.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){const param=attrs[name];return param&&param.list&&param.list.b?param.list.b:def}class NodeValueImpl{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){const value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}}const executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"Relu6":case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i<n1.length;i++)if(n1[i]!==-1&&n2[i]!==-1&&n1[i]!==n2[i])return!1;return!0}class TensorArray{constructor(name,dtype,maxSize,elementShape,identicalElementShapes,dynamicSize,clearAfterRead){this.name=name,this.dtype=dtype,this.maxSize=maxSize,this.elementShape=elementShape,this.identicalElementShapes=identicalElementShapes,this.dynamicSize=dynamicSize,this.clearAfterRead=clearAfterRead,this.tensors=[],this.closed_=!1,this.idTensor=scalar(0),keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.tensor.id))&&tensor168.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);const tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor168){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);const t=this.tensors[index]||{};if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
          because the value dtype is ${tensor168.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor168.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor168,keep(tensor168),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i<this.size();i++)indices.push(i)}if(indices.length===0)return tensor4([],[0].concat(this.elementShape));const tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,"TensorArray shape mismatch: "),stack(tensors,0)}concat(dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);if(this.size()===0)return tensor4([],[0].concat(this.elementShape));const indices=[];for(let i=0;i<this.size();i++)indices.push(i);const tensors=this.readMany(indices);return assertShapesMatchAllowUndefinedSize(this.elementShape,tensors[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`),concat(tensors,0)}scatter(indices,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);const maxIndex=Math.max(...indices);if(!this.dynamicSize&&maxIndex>=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor168,0))}split(length,tensor168){if(tensor168.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor168.dtype}`);let totalLength=0;const cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);const elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=[];tidy(()=>{tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){const previousLength=i===0?0:cumulativeLengths[i-1],indices2=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors[i]=reshape(slice(tensor168,indices2,sizes),this.elementShape)}return tensors});const indices=[];for(let i=0;i<length.length;i++)indices[i]=i;this.writeMany(indices,tensors)}}class TensorList{constructor(tensors,elementShape,elementDtype,maxNumElements=-1){this.tensors=tensors,this.elementShape=elementShape,this.elementDtype=elementDtype,tensors!=null&&tensors.forEach(tensor168=>{if(elementDtype!==tensor168.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor168.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor168=>{(keepIds==null||!keepIds.has(tensor168.id))&&tensor168.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{const reshapedTensors=this.tensors.map(tensor168=>reshape(tensor168,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");const tensor168=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor168.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor168,elementShape)}pushBack(tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor168.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor168),this.tensors.push(tensor168)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor168){if(tensor168.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor168.shape,"TensorList shape mismatch: "),keep(tensor168),this.tensors[elementIndex]=tensor168}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{const tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor4([],[0].concat(this.elementShape)):tidy(()=>{const tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}}function fromTensor(tensor168,elementShape,elementDtype){const dtype=tensor168.dtype;if(tensor168.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor168.shape}`);if(tensor168.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor168.dtype}, but list elements ${elementDtype}`);const outputShape=tensor168.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");const tensorList=unstack(tensor168);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor168,indices,elementShape,numElements){if(indices.length!==tensor168.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor168.shape[0]}`);const maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);const list=new TensorList([],elementShape,tensor168.dtype,numElements),tensors=unstack(tensor168,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split9(tensor168,length,elementShape){let totalLength=0;const cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor168.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${totalLength}, and tensor's shape is: ${tensor168.shape}`);const elementPerRow=totalLength===0?0:tensor168.size/totalLength,tensors=tidy(()=>{const tensors2=[];tensor168=reshape(tensor168,[1,totalLength,elementPerRow]);for(let i=0;i<length.length;++i){const previousLength=i===0?0:cumulativeLengths[i-1],indices=[0,previousLength,0],sizes=[1,length[i],elementPerRow];tensors2[i]=reshape(slice(tensor168,indices,sizes),elementShape)}return tensor168.dispose(),tensors2}),list=new TensorList([],elementShape,tensor168.dtype,length.length);for(let i=0;i<tensors.length;i++)list.setItem(i,tensors[i]);return list}const executeOp3=async(node,tensorMap,context)=>{switch(node.op){case"If":case"StatelessIf":{const thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condValue=await cond.data();return condValue[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{const bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor168=>tensor168.id);let condValue=await condResult[0].data();condResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});let result=args;for(;condValue[0];){const origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);const resultIds=result.map(tensor168=>tensor168.id);origResult.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()});const condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor168=>{!tensor168.kept&&argIds.indexOf(tensor168.id)===-1&&resultIds.indexOf(tensor168.id)===-1&&tensor168.dispose()})}return result}case"LoopCond":{const pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{const pred=getParamValue("pred",node,tensorMap,context);let data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{const inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){const data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{const frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{const data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{const data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{const size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{const id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{const readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),readTensorArray=context.getTensorArray(readId.id);return[readTensorArray.read(readIndex)]}case"TensorArrayGatherV3":{const gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context),gatherTensorArray=context.getTensorArray(gatherId.id);return[gatherTensorArray.gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{const scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{const concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{const splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{const sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{const closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{const idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{const idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{const scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":{const elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{const gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=context.getTensorList(gatherId.id);return[tensorList.gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{const idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{const tensor168=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor168,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{const concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{const idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{const idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return[tensorList.popBack(elementShape,elementDType)]}case"TensorListSplit":{const splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split9(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){const[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");const stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}}const executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{const stride=getParamValue("stride",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad11,dataFormat,dilation)]}case"Conv2D":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{const{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"FusedDepthwiseConv2dNative":{const{stride,pad:pad11,dataFormat,dilations,biasArg,preluArg,activationFunc}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad11,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad11)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad11,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad11,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPool":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11)]}case"MaxPoolWithArgmax":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad11,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"MaxPool3D":{const stride=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad11)]}case"Dilation2D":{const strides=getParamValue("strides",node,tensorMap,context),pad11=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad11,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{const shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{const start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{const logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{const indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{const start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step9=getParamValue("step",node,tensorMap,context);return[range(start,stop,step9,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{const shape=getParamValue("shape",node,tensorMap,context),mean7=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean7,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){const boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}const executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{const{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{const{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{const condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{const x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{const x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{const x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":const def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{const data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":const snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":const input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;i<data2.length;i++)console.log(Array.prototype.slice.call(data2[i].dataSync()).slice(0,summarize));return[input2];default:throw TypeError(`Node type ${node.op} is not implemented`)}};class HashTable{constructor(keyDType,valueDType){this.keyDType=keyDType,this.valueDType=valueDType,this.handle=scalar(0),this.tensorMap=new Map,keep(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);const $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{const $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i<keysLength;i++){const key=$keys[i],value=$values[i];keep(value),this.tensorMap.set(key,value)}return this.handle})}async find(keys,defaultValue){this.checkKeyAndValueTensor(keys,defaultValue);const $keys=await keys.data();return tidy(()=>{const result=[];for(let i=0;i<$keys.length;i++){const key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){const result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}}const executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{const keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{const handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{const handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context),hashTable2=resourceManager.getHashTableById(handle.id);return[await hashTable2.find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{const images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners)]}case"ResizeNearestNeighbor":{const images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners)]}case"CropAndResize":{const image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":const[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{const axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{const axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{const axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{const axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse12=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse12)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{const n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context);let inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"GatherV2":case"Gather":{const axis=getParamValue("axis",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis)]}case"ReverseV2":case"Reverse":{const axis=getParamValue("axis",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context);return[reverse(input2,axis)]}case"Slice":{const begin=getParamValue("begin",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[slice(getParamValue("x",node,tensorMap,context),begin,size)]}case"StridedSlice":{const begin=getParamValue("begin",node,tensorMap,context),end=getParamValue("end",node,tensorMap,context),strides=getParamValue("strides",node,tensorMap,context),beginMask=getParamValue("beginMask",node,tensorMap,context),endMask=getParamValue("endMask",node,tensorMap,context),ellipsisMask=getParamValue("ellipsisMask",node,tensorMap,context),newAxisMask=getParamValue("newAxisMask",node,tensorMap,context),shrinkAxisMask=getParamValue("shrinkAxisMask",node,tensorMap,context),tensor168=getParamValue("x",node,tensorMap,context);return[stridedSlice(tensor168,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)]}case"Pack":return tidy(()=>{const axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor168=>{const sameShape=util_exports.arraysEqual(tensor168.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor168).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor168:reshape(tensor168,shape)});return[stack(mapped,axis)]});case"Unpack":{const axis=getParamValue("axis",node,tensorMap,context),tensor168=getParamValue("tensor",node,tensorMap,context);return unstack(tensor168,axis)}case"Tile":{const reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{const axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor168=getParamValue("x",node,tensorMap,context);return split(tensor168,numOrSizeSplits,axis)}case"ScatterNd":{const indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{const x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{const indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{const axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{const axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{const blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{const blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{const blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){const value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":const opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}class ExecutionContext{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const names=[];for(let i=0;i<this.contexts.length-1;i++){const contexts2=this.contexts.slice(0,this.contexts.length-i);names.push(this.contextIdforContexts(contexts2))}names.push(""),this._currentContextIds=names}contextIdforContexts(contexts2){return contexts2?contexts2.map(context=>context.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;const context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(const key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(const key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}}function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){const usedNodes=new Set,missingInputs=[];let dynamicNode=null,syncInputs=null;const seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]);let initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));const frontier=[...outputs];for(;frontier.length>0;){const node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]!=null)continue;if(inputNodeNames.indexOf(node.name)!==-1)continue;if(initNodeNames.indexOf(node.name)!==-1)continue;if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{if(seen.has(input2.name))return;seen.add(input2.name),frontier.push(input2)})}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){const{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});const seen=new Set,orderedNodes=[];for(;frontier.length>0;){const node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}const CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}class GraphExecutor{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){const weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor168=>tensor168.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{const name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){const sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){const executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){const outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);const names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);const inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]);let outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);const compilationKey=this.getCompilationKey(inputNodes,outputNodes);let orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));const tensorArrayMap={},tensorListMap={};return tidy(()=>{const context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{const[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});const tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;i<orderedNodes.length;i++){const node=orderedNodes[i];if(!tensorsMap[node.name]){const tensors=executeOp18(node,tensorsMap,context,this._resourceManager);if(util_exports.isPromise(tensors))throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);tensorsMap[node.name]=tensors,this.checkTensorForDisposal(node.name,node,tensorsMap,context,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount)}}return this.parent==null&&context.dispose(tensorsToKeep),outputs.map(name=>getTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){const ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor168=>tensor168.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){if(node.category==="control"||outputNames.indexOf(nodeName)!==-1)return;tensorMap[nodeName].forEach(tensor168=>{tensor168!=null&&(intermediateTensorConsumerCount[tensor168.id]=(intermediateTensorConsumerCount[tensor168.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){const tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor168=>{if(tensor168&&!tensorsToKeep.has(tensor168.id)){const count2=intermediateTensorConsumerCount[tensor168.id];count2===1?(tensor168.dispose(),delete intermediateTensorConsumerCount[tensor168.id]):count2!=null&&intermediateTensorConsumerCount[tensor168.id]--}})}})}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));const context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{const tensorArray=tensorMap[key];tensorArray.forEach(tensor168=>{tensor168&&!tensor168.isDisposed&&!keepIds.has(tensor168.id)&&tensor168.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){const mappedInputs=inputs.reduce((map,tensor168,index)=>(map[this.inputs[index].name]=tensor168,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){const names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]);let outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);const{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack9=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{const[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});const intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack9.length>0;){const promises=this.processStack(inputNodes,stack9,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack9,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){const promises=[];for(;stack9.length>0;){const item=stack9.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){const tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));const currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack9,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack9,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack9,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack9,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{const[nodeName]=getNodeNameAndIndex(childNode.name,context);if(added[nodeName]||!usedNodes.has(childNode.name))return;childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack9.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack9.push({contexts:context.currentContext,node:childNode}))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor168=>tensor168.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{const input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){const shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){const result={};for(const inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){const tensor168=this._signature.inputs[inputName];result[tensor168.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){const notInGraph=Object.keys(inputs).filter(name=>{const[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null){const tensor168=this._signature.outputs[name];return tensor168.name}return name},{})}checkOutputs(outputs){outputs.forEach(name=>{const[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}}class ResourceManager{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(const key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(const name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}}const TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json";class GraphModel{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){const path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{const handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;const graph2=this.artifacts.modelTopology;let signature={};this.artifacts.userDefinedMetadata!=null&&(signature=this.artifacts.userDefinedMetadata.signature),this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;const weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null){const initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){const handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config2){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);const result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);const result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);const model2=new GraphModel(modelUrl,options);return await model2.load(),model2}const version6="2.7.0",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version8,webcam:()=>webcam,zip:()=>zip});const seedrandom3=__toModule2(require_seedrandom4()),seedrandom2=__toModule2(require_seedrandom4());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);const result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){const mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(const k in input2){const child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){const input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");const result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){const mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(const k in input2){const children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){const seen=new Map;deepMapInternal(input2,mapFn,seen);for(const key of Array.from(seen.keys())){const value=seen.get(key);if(util_exports.isPromise(value)){const mappedValue=await value;seen.set(key,mappedValue)}}const result=deepMapInternal(input2,mapFn,seen);return result}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container2){return deepMap(container2,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}class RingBuffer{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(const value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}}class GrowingRingBuffer extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){const newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;i<len;i++)newData[i]=this.get(this.wrap(this.begin+i));this.data=newData,this.capacity=newCapacity,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=len}}GrowingRingBuffer.INITIAL_CAPACITY=32;function iteratorFromItems(items){return new ArrayIterator(items)}function iteratorFromFunction(func2){return new FunctionCallIterator(func2)}function iteratorFromConcatenated(baseIterators,baseErrorHandler){return new ChainedIterator(baseIterators,baseErrorHandler)}function iteratorFromZipped(iterators,mismatchMode=ZipMismatchMode.FAIL){return new ZipIterator(iterators,mismatchMode)}class LazyIterator{async toArray(){const result=[];let x=await this.next();for(;!x.done;)result.push(x.value),x=await this.next();return result}async toArrayForTest(){const stream=this.prefetch(100),result=[];let x=await stream.next();for(;!x.done;)result.push(x.value),x=await stream.next();return result}async resolveFully(){let x=await this.next();for(;!x.done;)x=await this.next()}async resolveWhile(predicate){let x=await this.next(),shouldContinue=predicate(x.value);for(;!x.done&&shouldContinue;)x=await this.next(),shouldContinue=predicate(x.value)}handleErrors(handler){return new ErrorHandlingLazyIterator(this,handler)}filter(predicate){return new FilterIterator(this,predicate)}map(transform){return new MapIterator(this,transform)}mapAsync(transform){return new AsyncMapIterator(this,transform)}serialMapAsync(transform){return new AsyncMapIterator(this,transform).serial()}flatmap(transform){return new FlatmapIterator(this,transform)}async forEachAsync(f){return this.map(f).resolveFully()}async serialForEach(f){return this.serialMapAsync(f).resolveWhile(x=>x===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){const rowBatches=this.rowMajorBatch(batchSize,smallLastBatch);return rowBatches.map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}}class ArrayIterator extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}}class FunctionCallIterator extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class SerialIterator extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class SkipIterator extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){const skipped=await this.upstream.next();if(skipped.done)return skipped;dispose(skipped.value)}return this.upstream.next()}}class TakeIterator extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class RowMajorBatchIterator extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){const batch=[];for(;batch.length<this.batchSize;){const item=await this.upstream.next();if(item.done)return this.enableSmallLastBatch&&batch.length>0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}}class FilterIterator extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){const item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}}class MapIterator extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){const item=await this.upstream.next();if(item.done)return{value:null,done:!0};const inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(const t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}}class ErrorHandlingLazyIterator extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class AsyncMapIterator extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const item=await this.upstream.next();if(item.done)return{value:null,done:!0};const inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(const t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}}class OneToManyIterator extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class FlatmapIterator extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const item=await this.upstream.next();if(item.done)return!1;const inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(const t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}}class ChainedIterator extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){const upstreamSummaries="TODO: fill in upstream of chained summaries";return`${upstreamSummaries} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){const iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}}var ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));class ZipIterator extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){const upstreamSummaries="TODO: fill in upstream of zip summaries";return`{${upstreamSummaries}} -> Zip`}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container2){if(container2 instanceof LazyIterator){const result=container2.next();return{value:result.then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}}else return{value:null,recurse:!0}}const mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class PrefetchIterator extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}}class ShuffleIterator extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom2.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max10){return Math.floor(this.random()*max10)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}}class Dataset{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){const base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is
      ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset5){const base2=this;let size;return this.size===Infinity||dataset5.size===Infinity?size=Infinity:this.size!=null&&dataset5.size!=null?size=this.size+dataset5.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset5.iterator()),size)}filter(predicate){const base2=this;let size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){const base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){const base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){const base2=this;let size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{const iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){const base2=this;let size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size<count2||count2===void 0||count2<0)?size=0:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const base2=this,random=seedrandom3.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){const base2=this;let size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i<datasets.length;i++)size=size==null?datasets[i].size:Math.min(size,datasets[i].size);else if(datasets instanceof Object)for(const ds in datasets)size=size==null?datasets[ds].size:Math.min(size,datasets[ds].size);return datasetFromIteratorFn(async()=>{const streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;const exampleRow=rows[0];if(canTensorify(exampleRow)){const value=batchConcat(rows);return{value,recurse:!1}}return{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor4(arrays)}class TextLineDataset extends Dataset{constructor(input2){super();this.input=input2}async iterator(){const inputIterator=await this.input.iterator(),utf8Iterator=inputIterator.decodeUTF8(),lineIterator=utf8Iterator.split(`
`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line));return lineIterator}}const CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote");class CSVDataset extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=!(csvConfig.hasHeader===!1),this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);const counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs)for(const key of Object.keys(this.columnConfigs)){const index=this.fullColumnNames.indexOf(key);if(index===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const iter=await this.base.iterator(),firstElement=await iter.next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");const firstLine=firstElement.value,headers=this.parseRow(firstLine,!1);return headers}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){const values=this.parseRow(line),features={},labels={};for(let i=0;i<this.fullColumnNames.length;i++){const key=this.fullColumnNames[i],config2=this.columnConfigs?this.columnConfigs[key]:null;if(this.configuredColumnsOnly&&!config2)continue;{const value=values[i];let parsedValue=null;if(value==="")if(config2&&config2.default!==void 0)parsedValue=config2.default;else{if(config2&&(config2.required||config2.isLabel))throw new Error(`Required column ${key} is empty in this line: ${line}`);parsedValue=void 0}else{const valueAsNum=Number(value);if(isNaN(valueAsNum))config2&&config2.dtype==="bool"?parsedValue=this.getBoolean(value):parsedValue=value;else if(!config2||!config2.dtype)parsedValue=valueAsNum;else switch(config2.dtype){case"float32":parsedValue=valueAsNum;break;case"int32":parsedValue=Math.floor(valueAsNum);break;case"bool":parsedValue=this.getBoolean(value);break;default:parsedValue=valueAsNum}}config2&&config2.isLabel?labels[key]=parsedValue:features[key]=parsedValue}}return Object.keys(labels).length===0?features:{xs:features,ys:labels}}getBoolean(value){return value==="1"||value.toLowerCase()==="true"?1:0}parseRow(line,validateElementCount=!0){const result=[];let readOffset=0;const readLength=line.length;let currentState=STATE_OUT;for(let i=0;i<readLength;i++)switch(currentState){case STATE_OUT:switch(line.charAt(i)){case CODE_QUOTE:readOffset=i+1,currentState=STATE_QUOTE;break;case this.delimiter:if(readOffset=i+1,this.delimiter===" "&&this.delimWhitespace)break;result.push(""),currentState=STATE_OUT;break;default:currentState=STATE_FIELD,readOffset=i;break}break;case STATE_FIELD:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i)),currentState=STATE_OUT,readOffset=i+1;break;default:}break;case STATE_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE_AFTER_QUOTE;break;default:}break;case STATE_QUOTE_AFTER_QUOTE:switch(line.charAt(i)){case this.delimiter:result.push(line.substring(readOffset,i-1)),currentState=STATE_OUT,readOffset=i+1;break;case CODE_QUOTE:currentState=STATE_QUOTE;break;default:currentState=STATE_WITHIN_QUOTE_IN_QUOTE;break}break;case STATE_WITHIN_QUOTE_IN_QUOTE:switch(line.charAt(i)){case CODE_QUOTE:currentState=STATE_QUOTE;break;default:}break;default:}if(currentState===STATE_QUOTE_AFTER_QUOTE?result.push(line.substring(readOffset,readLength-1)):result.push(line.substring(readOffset)),validateElementCount&&result.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);return result}}class MicrophoneIterator extends LazyIterator{constructor(microphoneConfig){super();this.microphoneConfig=microphoneConfig,this.isClosed=!1,this.fftSize=microphoneConfig.fftSize||1024;const fftSizeLog2=Math.log2(this.fftSize);if(this.fftSize<0||fftSizeLog2<4||fftSizeLog2>14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=!(microphoneConfig.includeSpectrogram===!1),this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);const streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor;const audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){const freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const freqDataQueue=[],timeDataQueue=[];let currentFrames=0;return new Promise(resolve=>{const intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){const frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){const vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor4(vals,shape)}}class WebcamIterator extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){const widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}const webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{const expandedImage=img.toFloat().expandDims(0);let resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){const tracks=this.stream.getTracks();tracks.forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class DataSource{}class StringIterator extends LazyIterator{split(separator){return new SplitIterator(this,separator)}}class SplitIterator extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class SplitIteratorImpl extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);const lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(const line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}}class ByteChunkIterator extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}}class Utf8Iterator extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class Utf8IteratorImpl extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const chunkResult=await this.upstream.next();let chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}}class FileChunkIterator extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const chunk=new Promise((resolve,reject)=>{const end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{const fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));const slice21=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice21)}this.offset=end});return{value:await chunk,done:!1}}}async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));const response=await util_exports.fetch(urlString,requestInit);if(response.ok){const uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}const getRequestInitFromRequest=request=>{const init2={method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity};return init2};function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}class FileDataSource extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){const fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}}class URLDataSource extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}}function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){const iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{const gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}const version8="2.7.0",seedrandom4=__toModule2(require_seedrandom6());function assertNotComplex(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}const nonMaxSuppressionV3Impl2=kernel_impls_exports.nonMaxSuppressionV3Impl,split10=kernel_impls_exports.split,tile9=kernel_impls_exports.tile,topkImpl2=kernel_impls_exports.topkImpl,whereImpl2=kernel_impls_exports.whereImpl;class MathBackendCPU extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine15())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));const dataId={};return this.data.set(dataId,{values,dtype,refCount:1}),dataId}makeTensorInfo(shape,dtype,values){let outId;if(dtype==="string"&&values!=null&&values.length>0&&util_exports.isString(values[0])){const encodedValues=values.map(d=>util_exports.encodeString(d));outId=this.write(encodedValues,shape,dtype)}else outId=this.write(values,shape,dtype);return{dataId:outId,shape,dtype}}incRef(dataId){const tensorData=this.data.get(dataId);tensorData.refCount++}decRef(dataId){if(this.data.has(dataId)){const tensorData=this.data.get(dataId);tensorData.refCount--}}move(dataId,values,shape,dtype){this.data.set(dataId,{values,dtype,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(dataId){return this.readSync(dataId)}readSync(dataId){const{dtype,complexTensorInfos}=this.data.get(dataId);if(dtype==="complex64"){const realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);return backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}return this.data.get(dataId).values}bufferSync(t){const data2=this.readSync(t.dataId);let decodedData=data2;if(t.dtype==="string")try{decodedData=data2.map(d=>util_exports.decodeString(d))}catch(_a){throw new Error("Failed to decode encoded string bytes into utf-8")}return buffer(t.shape,t.dtype,decodedData)}makeOutput(values,shape,dtype){const dataId=this.write(values,shape,dtype);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}disposeData(dataId){if(this.data.has(dataId)){const{complexTensorInfos}=this.data.get(dataId);complexTensorInfos!=null&&(this.disposeData(complexTensorInfos.real.dataId),this.disposeData(complexTensorInfos.imag.dataId)),this.data.delete(dataId)}}disposeIntermediateTensorInfo(tensorInfo){const dataId=tensorInfo.dataId;if(this.data.has(dataId)){const tensorData=this.data.get(dataId);tensorData.refCount--,tensorData.refCount<1&&this.disposeData(dataId)}}async time(f){const start=util_exports.now();f();const kernelMs=util_exports.now()-start;return{kernelMs}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}stridedSlice(x,begin,end,strides){assertNotComplex(x,"stridedSlice");const outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);const buffer11=buffer(outShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){const loc=buffer11.indexToLoc(i),newLoc=new Array(loc.length);for(let j=0;j<newLoc.length;j++)newLoc[j]=loc[j]*strides[j]+begin[j];buffer11.set(xBuf.get(...newLoc),...loc)}return buffer11.toTensor()}diag(x){const xVals=this.readSync(x.dataId),buffer11=buffer([x.size,x.size],x.dtype),vals=buffer11.values;for(let i=0;i<xVals.length;i++)vals[i*x.size+i]=xVals[i];return buffer11.toTensor()}unstack(x,axis){const num=x.shape[axis],outShape=new Array(x.rank-1);let outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);const begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;const res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=slice(x,begin,size).reshape(outShape);return res}reverse(x,axis){assertNotComplex(x,"reverse");const buffer11=buffer(x.shape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<buffer11.size;i++){const outLoc=buffer11.indexToLoc(i),inLoc=outLoc.slice();axis.forEach(ax=>inLoc[ax]=x.shape[ax]-1-inLoc[ax]),buffer11.set(xBuf.get(...inLoc),...outLoc)}return buffer11.toTensor()}neg(x){return assertNotComplex(x,"neg"),mul(scalar(-1),x)}addN(tensors){assertNotComplex(tensors,"addN");const vals=tensors.map(t=>this.readSync(t.dataId)),result=buffer(tensors[0].shape,tensors[0].dtype),resultVals=result.values;for(let i=0;i<tensors.length;i++){const currVals=vals[i];for(let j=0;j<resultVals.length;j++)resultVals[j]+=currVals[j]}return result.toTensor()}softmax(logits,dim){const axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}pow(a,b){return assertNotComplex([a,b],"pow"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.pow(aValue,bValue))}floorDiv(a,b){assertNotComplex([a,b],"floorDiv");const op2=(a6,b2)=>Math.floor(a6/b2),outputDtype="int32";return this.broadcastedBinaryOp(a,b,outputDtype,op2)}sum(x,axes){assertNotComplex(x,"sum"),backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let sum29=0;for(let j=0;j<reduceSize;++j)sum29+=aVals[offset+j];vals[i]=sum29}return result}prod(x,axes){assertNotComplex(x,"sum");const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),resultDtype=upcastType(x.dtype,"int32"),result=zeros(outShape,resultDtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let prod5=1;for(let j=0;j<reduceSize;++j)prod5*=aVals[offset+j];vals[i]=prod5}return result}unsortedSegmentSum(x,segmentIds,numSegments){assertNotComplex(x,"unsortedSegmentSum");const res=[],numIters=x.rank-segmentIds.rank;for(let i=0;i<numIters;++i)segmentIds=segmentIds.expandDims(i+1);for(let i=0;i<numSegments;++i){const segmentId=scalar(i,"int32"),mask=equal(segmentId,segmentIds).asType("float32"),sum29=mask.mul(x).sum(0);res.push(sum29)}return stack(res)}argMin(x,axis){assertNotComplex(x,"argMin");const axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMin",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let min8=aVals[offset],minIndex=0;for(let j=0;j<reduceSize;++j){const value=aVals[offset+j];value<min8&&(min8=value,minIndex=j)}vals[i]=minIndex}return result}argMax(x,axis){assertNotComplex(x,"argMax");const axes=[axis];backend_util_exports.assertAxesAreInnerMostDims("argMax",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,"int32"),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let max10=aVals[offset],maxIndex=0;for(let j=0;j<reduceSize;++j){const value=aVals[offset+j];value>max10&&(max10=value,maxIndex=j)}vals[i]=maxIndex}return result}cumsum(x,axis,exclusive,reverse12){if(assertNotComplex(x,"cumsum"),axis!==x.rank-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${x.rank-1} but got axis=${axis}`);const resultDtype=upcastType(x.dtype,"int32"),result=zeros(x.shape,resultDtype),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId),finalDim=x.shape[x.rank-1],indexAdjuster=reverse12?(i,j)=>i+finalDim-j-1:(i,j)=>i+j;for(let i=0;i<aVals.length;i+=finalDim)for(let j=0;j<finalDim;j++){const idx=indexAdjuster(i,j);if(j===0)vals[idx]=exclusive?0:aVals[idx];else{const prevIdx=indexAdjuster(i,j-1);vals[idx]=exclusive?aVals[prevIdx]+vals[prevIdx]:aVals[idx]+vals[prevIdx]}}return result}equal(a,b){return assertNotComplex([a,b],"equal"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal===bVal?1:0)}notEqual(a,b){return assertNotComplex([a,b],"notEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal!==bVal?1:0)}less(a,b){return assertNotComplex([a,b],"less"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<bVal?1:0)}lessEqual(a,b){return assertNotComplex([a,b],"lessEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal<=bVal?1:0)}greater(a,b){return assertNotComplex([a,b],"greater"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>bVal?1:0)}greaterEqual(a,b){return assertNotComplex([a,b],"greaterEqual"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal>=bVal?1:0)}logicalAnd(a,b){return assertNotComplex([a,b],"logicalAnd"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal&&bVal)}logicalOr(a,b){return assertNotComplex([a,b],"logicalOr"),this.broadcastedBinaryOp(a,b,"bool",(aVal,bVal)=>aVal||bVal)}select(condition,a,b){assertNotComplex([condition,a,b],"select");const values=this.readSync(condition.dataId),aValues=this.readSync(a.dataId),bValues=this.readSync(b.dataId),result=zeros(a.shape,upcastType(a.dtype,b.dtype)),newValues=this.readSync(result.dataId);let index=0;const offset=condition.rank===0||condition.rank>1||a.rank===1?1:util_exports.sizeFromShape(a.shape.slice(1));for(let i=0;i<values.length;i++)for(let j=0;j<offset;j++)values[i]===1?newValues[index++]=aValues[i]:newValues[index++]=bValues[i];return result}where(condition){assertNotComplex([condition],"where");const condVals=this.readSync(condition.dataId);return whereImpl2(condition.shape,condVals)}topk(x,k,sorted){assertNotComplex(x,"topk");const xVals=this.readSync(x.dataId);return topkImpl2(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){assertNotComplex(x,"min"),backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let min8=aVals[offset];for(let j=0;j<reduceSize;++j){const value=aVals[offset+j];value<min8&&(min8=value)}vals[i]=min8}return result}minimum(a,b){return assertNotComplex([a,b],"minimum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.min(aVal,bVal))}mod(a,b){return assertNotComplex([a,b],"mod"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{const rem=aVal%bVal;return aVal<0&&bVal<0||aVal>=0&&bVal>=0?rem:(rem+bVal)%bVal})}maximum(a,b){return assertNotComplex([a,b],"maximum"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>Math.max(aVal,bVal))}all(x,axes){assertNotComplex(x,"all"),backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let all5=aVals[offset];for(let j=0;j<reduceSize;++j){const value=aVals[offset+j];all5=all5&&value}vals[i]=all5}return result}any(x,axes){assertNotComplex(x,"any"),backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),result=zeros(outShape,x.dtype),reduceSize=util_exports.sizeFromShape(reduceShape),vals=this.readSync(result.dataId),aVals=this.readSync(x.dataId);for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let anyVal=aVals[offset];for(let j=0;j<reduceSize;++j){const value=aVals[offset+j];anyVal=anyVal||value}vals[i]=anyVal}return result}squaredDifference(a,b){return assertNotComplex([a,b],"squaredDifference"),this.broadcastedBinaryOp(a,b,a.dtype,(aVal,bVal)=>{const diff=aVal-bVal;return diff*diff})}eluDer(dy,y){assertNotComplex([dy,y],"eluDer");const resultValues=new Float32Array(y.size),values=this.readSync(y.dataId),dyValues=this.readSync(dy.dataId);for(let i=0;i<values.length;++i){const v=values[i];v>=1?resultValues[i]=dyValues[i]:resultValues[i]=dyValues[i]*(v+1)}return this.makeOutput(resultValues,y.shape,"float32")}atan2(a,b){return assertNotComplex([a,b],"atan2"),this.broadcastedBinaryOp(a,b,a.dtype,(aValue,bValue)=>Math.atan2(aValue,bValue))}tile(x,reps){return assertNotComplex(x,"tile"),tile9(this.bufferSync(x),reps)}gather(x,indices,axis){assertNotComplex([x,indices],"gather");const newShape=x.shape.slice(),indicesValues=this.readSync(indices.dataId);newShape[axis]=indicesValues.length;const result=buffer(newShape,x.dtype),xBuf=this.bufferSync(x);for(let i=0;i<result.size;++i){const newLoc=result.indexToLoc(i),originalLoc=newLoc.slice();originalLoc[axis]=indicesValues[newLoc[axis]];const originalIndex=xBuf.locToIndex(originalLoc);result.values[i]=xBuf.values[originalIndex]}return result.toTensor()}batchToSpaceND(x,blockShape,crops){assertNotComplex([x],"batchToSpaceND");const prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}pool3d(x,convInfo,poolType){assertNotComplex(x,"pool3d");const strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,xValues=this.readSync(x.dataId),output=buffer(convInfo.outShape,x.dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputDepthStrides=convInfo.outShape[2]*convInfo.outShape[3]*convInfo.outShape[4],outputRowStrides=convInfo.outShape[3]*convInfo.outShape[4],outputColStrides=convInfo.outShape[4];for(let batch=0;batch<convInfo.batchSize;++batch){const outputBatchOffset=batch*outputBatchStrides,inputBatchOffset=batch*x.strides[0];for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){const xDepthCorner=yDepth*strideDepth-padFront;let xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;const xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner),outputDepthOffset=outputBatchOffset+yDepth*outputDepthStrides;for(let yRow=0;yRow<convInfo.outHeight;++yRow){const xRowCorner=yRow*strideHeight-padTop;let xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;const xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner),outputRowOffset=outputDepthOffset+yRow*outputRowStrides;for(let yCol=0;yCol<convInfo.outWidth;++yCol){const xColCorner=yCol*strideWidth-padLeft;let xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;const xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner),outputColOffset=outputRowOffset+yCol*outputColStrides;let minMaxValue=initialValue,avgValue=0,count2=0;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){const xDepthOffset=inputBatchOffset+xDepth*x.strides[1];for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){const xRowOffset=xDepthOffset+xRow*x.strides[2];for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){const xColOffset=xRowOffset+xCol*x.strides[3],pixel=xValues[xColOffset+channel];if(poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++),isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}if(isNaN(minMaxValue))break}const outputOffset=outputColOffset+channel;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}}return output.toTensor()}avgPool3d(x,convInfo){return assertNotComplex(x,"avgPool3d"),this.pool3d(x,convInfo,"avg").toFloat()}avgPool3dBackprop(dy,x,convInfo){assertNotComplex([dy,x],"avgPool3dBackprop");const strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterDepth*filterHeight*filterWidth),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){const dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft;let dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){const dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){const dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){const dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;const pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel}}}dx.set(dotProd*avgMultiplier,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}maxPool3d(x,convInfo){return assertNotComplex(x,"maxPool3d"),this.pool3d(x,convInfo,"max").toFloat()}maxPool3dPositions(x,convInfo){const maxPositions=buffer(convInfo.outShape,"int32"),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=this.bufferSync(x);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let yDepth=0;yDepth<convInfo.outDepth;++yDepth){const xDepthCorner=yDepth*strideDepth-padFront;let xDepthMin=xDepthCorner;for(;xDepthMin<0;)xDepthMin+=dilationDepth;const xDepthMax=Math.min(convInfo.inDepth,effectiveFilterDepth+xDepthCorner);for(let yRow=0;yRow<convInfo.outHeight;++yRow){const xRowCorner=yRow*strideHeight-padTop;let xRowMin=xRowCorner;for(;xRowMin<0;)xRowMin+=dilationHeight;const xRowMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRowCorner);for(let yCol=0;yCol<convInfo.outWidth;++yCol){const xColCorner=yCol*strideWidth-padLeft;let xColMin=xColCorner;for(;xColMin<0;)xColMin+=dilationWidth;const xColMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xColCorner);let maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xDepth=xDepthMin;xDepth<xDepthMax;xDepth+=dilationDepth){const wDepth=xDepth-xDepthCorner;for(let xRow=xRowMin;xRow<xRowMax;xRow+=dilationHeight){const wRow=xRow-xRowCorner;for(let xCol=xColMin;xCol<xColMax;xCol+=dilationWidth){const wCol=xCol-xColCorner,pixel=xBuf.get(batch,xDepth,xRow,xCol,channel);pixel>=maxValue&&(maxValue=pixel,maxPosition=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterHeight+wCol)}}}maxPositions.set(maxPosition,batch,yDepth,yRow,yCol,channel)}}}return maxPositions.toTensor()}maxPool3dBackprop(dy,x,y,convInfo){assertNotComplex([x,y],"maxPool3dBackprop");const maxPositions=this.maxPool3dPositions(x,convInfo),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),maxPosBuf=this.bufferSync(maxPositions),dyBuf=this.bufferSync(dy);for(let batch=0;batch<convInfo.batchSize;++batch)for(let channel=0;channel<convInfo.inChannels;++channel)for(let dxDepth=0;dxDepth<convInfo.inDepth;++dxDepth)for(let dxRow=0;dxRow<convInfo.inHeight;++dxRow)for(let dxCol=0;dxCol<convInfo.inWidth;++dxCol){const dyDepthCorner=dxDepth-padFront,dyRowCorner=dxRow-padTop,dyColCorner=dxCol-padLeft;let dotProd=0;for(let wDepth=0;wDepth<effectiveFilterDepth;wDepth+=dilationDepth){const dyDepth=(dyDepthCorner+wDepth)/strideDepth;if(dyDepth<0||dyDepth>=convInfo.outDepth||Math.floor(dyDepth)!==dyDepth)continue;for(let wRow=0;wRow<effectiveFilterHeight;wRow+=dilationHeight){const dyRow=(dyRowCorner+wRow)/strideHeight;if(dyRow<0||dyRow>=convInfo.outHeight||Math.floor(dyRow)!==dyRow)continue;for(let wCol=0;wCol<effectiveFilterWidth;wCol+=dilationWidth){const dyCol=(dyColCorner+wCol)/strideWidth;if(dyCol<0||dyCol>=convInfo.outWidth||Math.floor(dyCol)!==dyCol)continue;const maxPos=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(batch,dyDepth,dyRow,dyCol,channel),curPos=wDepth*effectiveFilterHeight*effectiveFilterWidth+wRow*effectiveFilterWidth+wCol,mask=maxPos===curPos?1:0;if(mask===0)continue;const pixel=dyBuf.get(batch,dyDepth,dyRow,dyCol,channel);dotProd+=pixel*mask}}}dx.set(dotProd,batch,dxDepth,dxRow,dxCol,channel)}return dx.toTensor()}resizeBilinear(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeBilinear");const[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),result=new Float32Array(util_exports.sizeFromShape([batch,newHeight,newWidth,numChannels])),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];let outputIdx=0;const effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];for(let b=0;b<batch;b++)for(let r=0;r<newHeight;r++){const sourceFracRow=effectiveRowSizeRatio*r,sourceRowFloor=Math.floor(sourceFracRow),rowFrac=sourceFracRow-sourceRowFloor,sourceRowCeil=Math.min(oldHeight-1,Math.ceil(sourceFracRow)),topRowOffset=b*x.strides[0]+sourceRowFloor*x.strides[1],botRowOffset=b*x.strides[0]+sourceRowCeil*x.strides[1];for(let c=0;c<newWidth;c++){const sourceFracCol=effectiveColSizeRatio*c,sourceColFloor=Math.floor(sourceFracCol),colFrac=sourceFracCol-sourceColFloor,sourceColCeil=Math.min(oldWidth-1,Math.ceil(sourceFracCol)),topLeftOffest=topRowOffset+sourceColFloor*x.strides[2],botLeftOffset=botRowOffset+sourceColFloor*x.strides[2],topRightOffset=topRowOffset+sourceColCeil*x.strides[2],botRightOffest=botRowOffset+sourceColCeil*x.strides[2];for(let d=0;d<numChannels;d++){const topLeft=xValues[topLeftOffest+d],bottomLeft=xValues[botLeftOffset+d],topRight=xValues[topRightOffset+d],bottomRight=xValues[botRightOffest+d],top=topLeft+(topRight-topLeft)*colFrac,bottom=bottomLeft+(bottomRight-bottomLeft)*colFrac,newValue=top+(bottom-top)*rowFrac;result[outputIdx++]=newValue}}}return tensor4(result,[batch,newHeight,newWidth,numChannels])}resizeBilinearBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeBilinearBackprop");const[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],dyValues=this.readSync(dy.dataId);let offset=0;for(let b=0;b<batch;b++){const bOffset=b*x.strides[0];for(let r=0;r<yHeight;r++){const dxR=r*heightScale,topDxRIndex=Math.floor(dxR),bottomDxRIndex=Math.min(Math.ceil(dxR),xHeight-1),topDxROffset=bOffset+topDxRIndex*x.strides[1],bottomDxROffset=bOffset+bottomDxRIndex*x.strides[1],dxRLerp=dxR-topDxRIndex,inverseDxRLerp=1-dxRLerp;for(let c=0;c<yWidth;c++){const dxC=c*widthScale,leftDxCIndex=Math.floor(dxC),rightDxCIndex=Math.min(Math.ceil(dxC),xWidth-1),dxCLerp=dxC-leftDxCIndex,inverseDxCLerp=1-dxCLerp,topLeftRCOffset=topDxROffset+leftDxCIndex*x.strides[2],topRightRCOffset=topDxROffset+rightDxCIndex*x.strides[2],bottomLeftRCOffset=bottomDxROffset+leftDxCIndex*x.strides[2],bottomRightRCOffset=bottomDxROffset+rightDxCIndex*x.strides[2],inverseDxRLerpTimesInverseDxCLerp=inverseDxRLerp*inverseDxCLerp,inverseDxRLerpTimesDxCLerp=inverseDxRLerp*dxCLerp,dxRLerpTimesInverseDxCLerp=dxRLerp*inverseDxCLerp,dxRLerpTimesDxCLerp=dxRLerp*dxCLerp;for(let d=0;d<depth;d++){const dyVal=dyValues[offset++];output[topLeftRCOffset+d]+=dyVal*inverseDxRLerpTimesInverseDxCLerp,output[topRightRCOffset+d]+=dyVal*inverseDxRLerpTimesDxCLerp,output[bottomLeftRCOffset+d]+=dyVal*dxRLerpTimesInverseDxCLerp,output[bottomRightRCOffset+d]+=dyVal*dxRLerpTimesDxCLerp}}}}return tensor4d(output,[batch,xWidth,xHeight,depth],x.dtype)}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){assertNotComplex(x,"resizeNearestNeighbor");const[batch,oldHeight,oldWidth,numChannels]=x.shape,xValues=this.readSync(x.dataId),output=new Float32Array(batch*newHeight*newWidth*numChannels),effectiveInputSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutputSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],effectiveRowSizeRatio=effectiveInputSize[0]/effectiveOutputSize[0],effectiveColSizeRatio=effectiveInputSize[1]/effectiveOutputSize[1];let outputOffset=0;for(let b=0;b<batch;b++){const batchOffset=b*x.strides[0];for(let r=0;r<newHeight;r++){const sourceFracRow=effectiveRowSizeRatio*r,sourceNearestRow=Math.min(oldHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow)),rowOffset=batchOffset+sourceNearestRow*x.strides[1];for(let c=0;c<newWidth;c++){const sourceFracCol=effectiveColSizeRatio*c,sourceNearestCol=Math.min(oldWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol)),colOffset=rowOffset+sourceNearestCol*x.strides[2];for(let d=0;d<numChannels;d++){const newVal=xValues[colOffset+d];output[outputOffset++]=newVal}}}}return tensor4(output,[batch,newHeight,newWidth,numChannels],x.dtype)}resizeNearestNeighborBackprop(dy,x,alignCorners){assertNotComplex([dy,x],"resizeNearestNeighborBackprop");const[batch,xHeight,xWidth,depth]=x.shape,[,yHeight,yWidth]=dy.shape,output=new Float32Array(batch*xHeight*xWidth*depth),dyValues=this.readSync(dy.dataId),effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;for(let b=0;b<batch;b++){const batchOffset=b*x.strides[0];for(let r=0;r<xHeight;r++){const rowOffset=batchOffset+r*x.strides[1],startRLerp=Math.floor(r*invHeightScale),startDyR=Math.floor(startRLerp-winHeight/2);for(let c=0;c<xWidth;c++){const colOffset=rowOffset+c*x.strides[2],startCLerp=Math.floor(c*invWidthScale),startDyC=Math.floor(startCLerp-winWidth/2);for(let d=0;d<depth;d++){let accum=0;for(let dyRIndex=0;dyRIndex<winHeight;dyRIndex++){const dyR=dyRIndex+startDyR;if(dyR<0||dyR>=yHeight)continue;const dyROffset=batchOffset+dyR*dy.strides[1],sourceFracRow=dyR*heightScale,sourceNearestRow=Math.min(xHeight-1,alignCorners?Math.round(sourceFracRow):Math.floor(sourceFracRow));if(r!==sourceNearestRow)continue;for(let dyCIndex=0;dyCIndex<winWidth;dyCIndex++){const dyC=dyCIndex+startDyC;if(dyC<0||dyC>=yWidth)continue;const dyCOffset=dyROffset+dyC*dy.strides[2],sourceFracCol=dyC*widthScale,sourceNearestCol=Math.min(xWidth-1,alignCorners?Math.round(sourceFracCol):Math.floor(sourceFracCol));c===sourceNearestCol&&(accum+=dyValues[dyCOffset+d])}}output[colOffset+d]=accum}}}}return tensor4d(output,x.shape,x.dtype)}localResponseNormalization4D(x,depthRadius,bias,alpha,beta){assertNotComplex(x,"localResponseNormalization4D");const channels=x.shape[3],maxD=channels-1,xValues=this.readSync(x.dataId),size=x.size,result=new Float32Array(size);function sumAcrossChannels(offset){const currentChannel=offset%channels;let beginSumOffset=offset-currentChannel+Math.max(0,currentChannel-depthRadius);const endSumOffset=offset-currentChannel+Math.min(currentChannel+depthRadius,maxD);let sum29=0;for(;beginSumOffset<=endSumOffset;beginSumOffset++){const z=xValues[beginSumOffset];sum29+=z*z}return sum29}for(let offset=0;offset<size;offset++){const sum29=sumAcrossChannels(offset),val=xValues[offset]*Math.pow(bias+alpha*sum29,-beta);result[offset]=val}return tensor4d(result,x.shape)}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){assertNotComplex(dy,"LRNGrad");const channels=dy.shape[3],dyValues=this.readSync(dy.dataId),inputImageValues=this.readSync(inputImage.dataId),outputImageValues=this.readSync(outputImage.dataId),result=new Float32Array(dy.size),size=dy.size;for(let offset=0;offset<size;offset++){const currentChannel=offset%channels,depthBegin=offset-currentChannel+Math.max(0,currentChannel-depthRadius),depthEnd=offset-currentChannel+Math.min(channels,currentChannel+depthRadius+1);let norm5=0;for(let k=depthBegin;k<depthEnd;k++)norm5+=Math.pow(inputImageValues[k],2);norm5=alpha*norm5+bias;for(let k=depthBegin;k<depthEnd;k++){let dyi=-2*alpha*beta*inputImageValues[k]*outputImageValues[offset]/norm5;offset===k&&(dyi+=Math.pow(norm5,-beta)),dyi*=dyValues[offset],result[k]+=dyi}}return tensor4d(result,dy.shape)}multinomial(logits,normalized,numSamples,seed){assertNotComplex(logits,"multinomial");const probabilities=normalized?logits:softmax(logits),batchSize=probabilities.shape[0],numEvents=probabilities.shape[1],res=zeros([batchSize,numSamples],"int32"),resVals=this.readSync(res.dataId),probVals=this.readSync(probabilities.dataId);for(let b=0;b<batchSize;++b){const offset=b*numEvents,cdf=new Float32Array(numEvents-1);cdf[0]=probVals[offset];for(let event=1;event<cdf.length;++event)cdf[event]=cdf[event-1]+probVals[offset+event];const random=seedrandom4.alea(seed.toString()),outOffset=b*numSamples;for(let sampleId=0;sampleId<numSamples;++sampleId){const r=random();resVals[outOffset+sampleId]=cdf.length;for(let event=0;event<cdf.length;event++)if(r<cdf[event]){resVals[outOffset+sampleId]=event;break}}}return res}oneHot(indices,depth,onValue,offValue){assertNotComplex(indices,"oneHot");const res=new Float32Array(indices.size*depth);res.fill(offValue);const indicesVal=this.readSync(indices.dataId);for(let event=0;event<indices.size;++event)indicesVal[event]>=0&&indicesVal[event]<depth&&(res[event*depth+indicesVal[event]]=onValue);return tensor2d(res,[indices.size,depth],"int32")}nonMaxSuppression(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){assertNotComplex(boxes,"nonMaxSuppression");const boxesVals=this.readSync(boxes.dataId),scoresVals=this.readSync(scores.dataId);return nonMaxSuppressionV3Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold)}depthToSpace(x,blockSize,dataFormat){util_exports.assert(dataFormat==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`),util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);const batchSize=x.shape[0],inputHeight=x.shape[1],inputWidth=x.shape[2],inputDepth=x.shape[3],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),xValues=this.readSync(x.dataId),result=new Float32Array(batchSize*outputHeight*outputWidth*outputDepth);let outputIdx=0;for(let b=0;b<batchSize;++b)for(let h=0;h<outputHeight;++h){const inH=Math.floor(h/blockSize),offsetH=h%blockSize;for(let w=0;w<outputWidth;++w){const inW=Math.floor(w/blockSize),offsetW=w%blockSize,offsetD=(offsetH*blockSize+offsetW)*outputDepth;for(let d=0;d<outputDepth;++d){const inD=d+offsetD,inputIdx=inD+inputDepth*(inW+inputWidth*(inH+inputHeight*b));result[outputIdx++]=xValues[inputIdx]}}}return tensor4d(result,[batchSize,outputHeight,outputWidth,outputDepth])}broadcastedBinaryOp(a,b,dtype,op2){const newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),result=buffer(newShape,dtype),aVals=this.readSync(a.dataId),bVals=this.readSync(b.dataId),aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),resVals=result.values;if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resVals.length;++i)resVals[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else{const aBuf=this.bufferSync(a),bBuf=this.bufferSync(b);for(let i=0;i<resVals.length;++i){const loc=result.indexToLoc(i),aLoc=loc.slice(-a.rank);aBroadcastDims.forEach(d=>aLoc[d]=0);const aIndex=aBuf.locToIndex(aLoc),bLoc=loc.slice(-b.rank);bBroadcastDims.forEach(d=>bLoc[d]=0);const bIndex=bBuf.locToIndex(bLoc);resVals[i]=op2(aVals[aIndex],bVals[bIndex])}}return result.toTensor()}split(x,sizeSplits,axis){return split10(x,sizeSplits,axis)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(images,boxes,boxIndex,cropSize,method,extrapolationValue){const[batch,imageHeight,imageWidth,numChannels]=images.shape,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,output=buffer([numBoxes,cropHeight,cropWidth,numChannels],"float32"),boxVals=this.readSync(boxes.dataId),boxIndVals=this.readSync(boxIndex.dataId),imageVals=this.readSync(images.dataId),inStride=images.strides,outStride=output.strides;for(let b=0;b<numBoxes;b++){const startInd=b*4,y1=boxVals[startInd],x1=boxVals[startInd+1],y2=boxVals[startInd+2],x2=boxVals[startInd+3],bInd=boxIndVals[b];if(bInd>=batch)continue;const heightScale=cropHeight>1?(y2-y1)*(imageHeight-1)/(cropHeight-1):0,widthScale=cropWidth>1?(x2-x1)*(imageWidth-1)/(cropWidth-1):0;for(let y=0;y<cropHeight;y++){const yInd=cropHeight>1?y1*(imageHeight-1)+y*heightScale:.5*(y1+y2)*(imageHeight-1);if(yInd<0||yInd>imageHeight-1){for(let x=0;x<cropWidth;x++)for(let c=0;c<numChannels;c++){const ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}if(method==="bilinear"){const topInd=Math.floor(yInd),bottomInd=Math.ceil(yInd),yLerp=yInd-topInd;for(let x=0;x<cropWidth;x++){const xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){const ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}const leftInd=Math.floor(xInd),rightInd=Math.ceil(xInd),xLerp=xInd-leftInd;for(let c=0;c<numChannels;c++){let ind=c+leftInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];const topLeft=imageVals[ind];ind=c+rightInd*inStride[2]+topInd*inStride[1]+bInd*inStride[0];const topRight=imageVals[ind];ind=c+leftInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];const bottomLeft=imageVals[ind];ind=c+rightInd*inStride[2]+bottomInd*inStride[1]+bInd*inStride[0];const bottomRight=imageVals[ind],top=topLeft+(topRight-topLeft)*xLerp,bottom=bottomLeft+(bottomRight-bottomLeft)*xLerp;ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0],output.values[ind]=top+(bottom-top)*yLerp}}}else for(let x=0;x<cropWidth;++x){const xInd=cropWidth>1?x1*(imageWidth-1)+x*widthScale:.5*(x1+x2)*(imageWidth-1);if(xInd<0||xInd>imageWidth-1){for(let c=0;c<numChannels;c++){const ind=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[ind]=extrapolationValue}continue}const closestX=Math.round(xInd),closestY=Math.round(yInd);for(let c=0;c<numChannels;c++){const inInd=c+closestX*inStride[2]+closestY*inStride[1]+bInd*inStride[0],outInd=c+x*outStride[2]+y*outStride[1]+b*outStride[0];output.values[outInd]=imageVals[inInd]}}}}return output.toTensor()}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){const{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1;return this.scatter(sparseIndices,sparseValues,outputShape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}gatherND(x,indices){const indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices);if(numSlices===0)return tensor4([],resultShape,x.dtype);const buffer11=new TensorBuffer([numSlices,sliceSize],x.dtype),indicesData=this.readSync(indices.dataId),xData=this.readSync(x.dataId);for(let i=0;i<numSlices;i++){const index=[];let flattenIndex=0;for(let j=0;j<sliceRank;j++){const dim=indicesData[i*sliceRank+j];flattenIndex+=dim*strides[j],index.push(dim)}if(flattenIndex<0||flattenIndex>=x.size/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${x.shape}`);for(let k=0;k<sliceSize;k++)buffer11.values[i*sliceSize+k]=xData[flattenIndex*sliceSize+k]}return buffer11.toTensor().reshape(resultShape)}scatterND(indices,updates,shape){const{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),defaultValue=scalar(0),sumDupeIndices=!0;return this.scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices)}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported for string tensors");return fill(x.shape,1,x.dtype)}zerosLike(x){const values=util_exports.getArrayFromDType(x.dtype,util_exports.sizeFromShape(x.shape));return this.makeOutput(values,x.shape,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}scatter(indices,updates,shape,outputSize,sliceSize,numUpdates,sliceRank,strides,defaultValue,sumDupeIndices){const flattenShape=[outputSize/sliceSize,sliceSize],indicesData=this.readSync(indices.dataId),updatesData=this.readSync(updates.dataId);if(outputSize===0)return tensor4([],shape,updates.dtype);const buffer11=new TensorBuffer(flattenShape,updates.dtype);buffer11.values.fill(this.readSync(defaultValue.dataId)[0]);for(let i=0;i<numUpdates;i++){const index=[];let flattenIndex=0;for(let j=0;j<sliceRank;j++){const dim=indicesData[i*sliceRank+j];index.push(dim),flattenIndex+=dim*strides[j]}if(flattenIndex<0||flattenIndex>=outputSize/sliceSize)throw new Error(`Invalid indices: ${index} does not index into ${shape}`);for(let k=0;k<sliceSize;k++)sumDupeIndices?buffer11.values[flattenIndex*sliceSize+k]+=updatesData[i*sliceSize+k]:buffer11.values[flattenIndex*sliceSize+k]=updates.rank===0?updatesData[0]:updatesData[i*sliceSize+k]}return buffer11.toTensor().reshape(shape)}}const shared_exports={};__export2(shared_exports,{addImpl:()=>addImpl,ceilImpl:()=>ceilImpl,expImpl:()=>expImpl,expm1Impl:()=>expm1Impl,floorImpl:()=>floorImpl,logImpl:()=>logImpl,maxImpl:()=>maxImpl,multiplyImpl:()=>multiplyImpl,notEqualImpl:()=>notEqualImpl,rsqrtImpl:()=>rsqrtImpl,simpleAbsImpl:()=>simpleAbsImpl,sliceImpl:()=>sliceImpl,squaredDifferenceImpl:()=>squaredDifferenceImpl,subImpl:()=>subImpl,transposeImpl:()=>transposeImpl,uniqueImpl:()=>uniqueImpl});function simpleAbsImpl(vals){const resultValues=new Float32Array(vals.length);for(let i=0;i<vals.length;++i)resultValues[i]=Math.abs(vals[i]);return resultValues}const abs9=args=>{const{x}=args.inputs,cpuBackend=args.backend;let resultValues=new Float32Array(util_exports.sizeFromShape(x.shape));if(x.dtype!=="complex64"){const values=cpuBackend.data.get(x.dataId).values;resultValues=simpleAbsImpl(values)}else{const complexVals=cpuBackend.data.get(x.dataId),real8=complexVals.complexTensorInfos.real,imag8=complexVals.complexTensorInfos.imag,realVals=cpuBackend.data.get(real8.dataId).values,imagVals=cpuBackend.data.get(imag8.dataId).values;for(let i=0;i<realVals.length;i++){const real9=realVals[i],imag9=imagVals[i];resultValues[i]=Math.hypot(real9,imag9)}}return cpuBackend.makeOutput(resultValues,x.shape,"float32")},absConfig={kernelName:Abs,backendName:"cpu",kernelFunc:abs9};function createSimpleBinaryKernelImpl(op2){return(aShape,bShape,aVals,bVals,dtype)=>{const newShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultRank=newShape.length,resultStrides=util_exports.computeStrides(newShape),resultSize=util_exports.sizeFromShape(newShape),result=util_exports.getTypedArrayFromDType(dtype,resultSize),aRank=aShape.length,bRank=bShape.length,aStrides=util_exports.computeStrides(aShape),bStrides=util_exports.computeStrides(bShape),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,newShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<result.length;++i)result[i]=op2(aVals[i%aVals.length],bVals[i%bVals.length]);else for(let i=0;i<result.length;++i){const loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);const aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);const bIndex=util_exports.locToIndex(bLoc,bRank,bStrides);result[i]=op2(aVals[aIndex],bVals[bIndex])}return[result,newShape]}}function complex9(args){const{inputs,backend:backend3}=args,{real:real8,imag:imag8}=inputs,realVals=backend3.data.get(real8.dataId).values,imagVals=backend3.data.get(imag8.dataId).values,complexInfo=backend3.makeTensorInfo(real8.shape,"complex64"),complex11=backend3.data.get(complexInfo.dataId);return complex11.complexTensorInfos={real:backend3.makeTensorInfo(real8.shape,"float32",realVals),imag:backend3.makeTensorInfo(imag8.shape,"float32",imagVals)},complexInfo}const complexConfig={kernelName:Complex,backendName:"cpu",kernelFunc:complex9};function identity2(args){const{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}const identityConfig={kernelName:Identity,backendName:"cpu",kernelFunc:identity2};function real6(args){const{inputs,backend:backend3}=args,{input:input2}=inputs,real8=backend3.data.get(input2.dataId).complexTensorInfos.real,realVal=backend3.data.get(real8.dataId).values;return backend3.makeTensorInfo(real8.shape,real8.dtype,realVal)}const realConfig={kernelName:Real,backendName:"cpu",kernelFunc:real6};function cast49(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity2({inputs:{x},backend:backend3});const zerosTensor=zeros(x.shape),floatX=cast49({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex9({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){const realPart=real6({inputs:{input:x},backend:backend3}),result=cast49({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){const result=identity2({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32"){const values=backend3.data.get(x.dataId).values,resultValues=Int32Array.from(values);return backend3.makeTensorInfo(x.shape,"int32",resultValues)}if(dtype==="bool"){const xVals=backend3.data.get(x.dataId).values,zero=util_exports.toTypedArray([0],x.dtype),[resultData,resultShape]=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0)(x.shape,[],xVals,zero,"bool");return backend3.makeTensorInfo(resultShape,"bool",resultData)}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}const castConfig={kernelName:Cast,backendName:"cpu",kernelFunc:cast49};function binaryKernelFunc(name,simpleImpl,complexImpl,dtype){return complexImpl==null?({inputs,backend:backend3})=>{const{a,b}=inputs,cpuBackend=backend3;assertNotComplex([a,b],name);const aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}:({inputs,backend:backend3})=>{const{a,b}=inputs,cpuBackend=backend3;if(a.dtype==="complex64"||b.dtype==="complex64"){const $aComplex=cast49({inputs:{x:a},backend:cpuBackend,attrs:{dtype:"complex64"}}),$aComplexVals=cpuBackend.data.get($aComplex.dataId),aReal=$aComplexVals.complexTensorInfos.real,aImag=$aComplexVals.complexTensorInfos.imag,aRealVals=cpuBackend.data.get(aReal.dataId).values,aImagVals=cpuBackend.data.get(aImag.dataId).values,$bComplex=cast49({inputs:{x:b},backend:cpuBackend,attrs:{dtype:"complex64"}}),$bComplexVals=cpuBackend.data.get($bComplex.dataId),bReal=$bComplexVals.complexTensorInfos.real,bImag=$bComplexVals.complexTensorInfos.imag,bRealVals=cpuBackend.data.get(bReal.dataId).values,bImagVals=cpuBackend.data.get(bImag.dataId).values,[resultRealData,resultImagData,resultShape]=complexImpl(a.shape,b.shape,aRealVals,aImagVals,bRealVals,bImagVals),resultReal=cpuBackend.makeTensorInfo(resultShape,"float32",resultRealData),resultImag=cpuBackend.makeTensorInfo(resultShape,"float32",resultImagData),result=complex9({inputs:{real:resultReal,imag:resultImag},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($aComplex),cpuBackend.disposeIntermediateTensorInfo($bComplex),cpuBackend.disposeIntermediateTensorInfo(resultReal),cpuBackend.disposeIntermediateTensorInfo(resultImag),result}else{const aVals=cpuBackend.data.get(a.dataId).values,bVals=cpuBackend.data.get(b.dataId).values,$dtype=dtype||a.dtype,[resultData,resultShape]=simpleImpl(a.shape,b.shape,aVals,bVals,$dtype);return cpuBackend.makeTensorInfo(resultShape,$dtype,resultData)}}}function createComplexBinaryKernelImpl(op2){return(aShape,bShape,aRealVals,aImagVals,bRealVals,bImagVals)=>{const resultShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),resultSize=util_exports.sizeFromShape(resultShape),resultRank=resultShape.length,resultStrides=util_exports.computeStrides(resultShape),resultRealVals=util_exports.getTypedArrayFromDType("float32",resultSize),resultImagVals=util_exports.getTypedArrayFromDType("float32",resultSize),aBroadcastDims=backend_util_exports.getBroadcastDims(aShape,resultShape),bBroadcastDims=backend_util_exports.getBroadcastDims(bShape,resultShape),aVals=backend_util_exports.mergeRealAndImagArrays(aRealVals,aImagVals),bVals=backend_util_exports.mergeRealAndImagArrays(bRealVals,bImagVals),aRank=aShape.length,aStrides=util_exports.computeStrides(aShape),bRank=bShape.length,bStrides=util_exports.computeStrides(bShape);if(aBroadcastDims.length+bBroadcastDims.length===0)for(let i=0;i<resultRealVals.length;i++){const aIdx=i%aVals.length,bIdx=i%bVals.length,result=op2(aVals[aIdx*2],aVals[aIdx*2+1],bVals[bIdx*2],bVals[bIdx*2+1]);resultRealVals[i]=result.real,resultImagVals[i]=result.imag}else for(let i=0;i<resultRealVals.length;i++){const loc=util_exports.indexToLoc(i,resultRank,resultStrides),aLoc=loc.slice(-aRank);aBroadcastDims.forEach(d=>aLoc[d]=0);const aIndex=util_exports.locToIndex(aLoc,aRank,aStrides),bLoc=loc.slice(-bRank);bBroadcastDims.forEach(d=>bLoc[d]=0);const bIndex=util_exports.locToIndex(bLoc,bRank,bStrides),opResult=op2(aVals[aIndex*2],aVals[aIndex*2+1],bVals[bIndex*2],bVals[bIndex*2+1]);resultRealVals[i]=opResult.real,resultImagVals[i]=opResult.imag}return[resultRealVals,resultImagVals,resultShape]}}const addImpl=createSimpleBinaryKernelImpl((a,b)=>a+b),addComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal+bReal,imag:aImag+bImag})),add32=binaryKernelFunc(Add,addImpl,addComplexImpl),addConfig={kernelName:Add,backendName:"cpu",kernelFunc:add32};function createSimpleUnaryImpl(op2){return(values,dtype,attrs)=>{const newValues=util_exports.getTypedArrayFromDType(dtype,values.length);for(let i=0;i<values.length;++i)newValues[i]=op2(values[i],attrs);return newValues}}function unaryKernelFunc(name,op2,dtype){return({inputs,attrs,backend:backend3})=>{const{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");const cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),$dtype=dtype||x.dtype,newValues=util_exports.getArrayFromDType($dtype,xSize);for(let i=0;i<xSize;++i)newValues[i]=op2(values[i],attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}function unaryKernelFuncFromImpl(name,unaryImpl,dtype){return({inputs,attrs,backend:backend3})=>{const{x}=inputs;if(assertNotComplex(x,name),x.dtype==="string"||dtype==="string")throw new Error("unaryKernelFunc does not support string input/output");const cpuBackend=backend3,values=cpuBackend.data.get(x.dataId).values,$dtype=dtype||x.dtype,newValues=unaryImpl(values,$dtype,attrs);return cpuBackend.makeTensorInfo(x.shape,$dtype,newValues)}}const ceilImpl=createSimpleUnaryImpl(xi=>Math.ceil(xi)),ceil4=unaryKernelFuncFromImpl(Ceil,ceilImpl),ceilConfig={kernelName:Ceil,backendName:"cpu",kernelFunc:ceil4},expImpl=createSimpleUnaryImpl(xi=>Math.exp(xi)),exp12=unaryKernelFuncFromImpl(Exp,expImpl),expConfig={kernelName:Exp,backendName:"cpu",kernelFunc:exp12},expm1Impl=createSimpleUnaryImpl(xi=>Math.expm1(xi)),expm14=unaryKernelFuncFromImpl(Expm1,expm1Impl),expm1Config={kernelName:Expm1,backendName:"cpu",kernelFunc:expm14},floorImpl=createSimpleUnaryImpl(xi=>Math.floor(xi)),floor6=unaryKernelFuncFromImpl(Floor,floorImpl),floorConfig={kernelName:Floor,backendName:"cpu",kernelFunc:floor6},logImpl=createSimpleUnaryImpl(xi=>Math.log(xi)),log9=unaryKernelFuncFromImpl(Log,logImpl),logConfig={kernelName:Log,backendName:"cpu",kernelFunc:log9};function maxImpl(aVals,reduceSize,outShape,dtype){const vals=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(outShape));for(let i=0;i<vals.length;++i){const offset=i*reduceSize;let max10=aVals[offset];for(let j=0;j<reduceSize;++j){const value=aVals[offset+j];value>max10&&(max10=value)}vals[i]=max10}return vals}const multiplyImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue*bValue),multiplyComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal*bReal-aImag*bImag,imag:aReal*bImag+aImag*bReal})),multiply2=binaryKernelFunc(Multiply,multiplyImpl,multiplyComplexImpl),multiplyConfig={kernelName:Multiply,backendName:"cpu",kernelFunc:multiply2},notEqualImpl=createSimpleBinaryKernelImpl((a,b)=>a!==b?1:0),notEqual2=binaryKernelFunc(NotEqual,notEqualImpl,null,"bool"),notEqualConfig={kernelName:NotEqual,backendName:"cpu",kernelFunc:notEqual2},rsqrtImpl=createSimpleUnaryImpl(xi=>1/Math.sqrt(xi)),rsqrt5=unaryKernelFuncFromImpl(Rsqrt,rsqrtImpl),rsqrtConfig={kernelName:Rsqrt,backendName:"cpu",kernelFunc:rsqrt5};function sliceImpl(vals,begin,size,shape,dtype){const isContinous=slice_util_exports.isSliceContinous(shape,begin,size),length=util_exports.sizeFromShape(size),xStrides=util_exports.computeStrides(shape);if(isContinous){const flatOffset=slice_util_exports.computeFlatOffset(begin,xStrides);return vals.subarray(flatOffset,flatOffset+length)}const outVals=util_exports.getTypedArrayFromDType(dtype,length);for(let i=0;i<length;++i){const rank=size.length,strides=util_exports.computeStrides(size),loc=util_exports.indexToLoc(i,rank,strides),xLoc=loc.map((idx,j)=>idx+begin[j]),xIndex=util_exports.locToIndex(xLoc,shape.length,xStrides);outVals[i]=vals[xIndex]}return outVals}function slice19(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{begin,size}=attrs;assertNotComplex(x,"slice");const[$begin,$size]=slice_util_exports.parseSliceParams(x,begin,size);slice_util_exports.assertParamsValid(x,$begin,$size);const vals=backend3.data.get(x.dataId).values,outVals=sliceImpl(vals,$begin,$size,x.shape,x.dtype);return backend3.makeTensorInfo($size,x.dtype,outVals)}const sliceConfig={kernelName:Slice,backendName:"cpu",kernelFunc:slice19},squaredDifferenceImpl=createSimpleBinaryKernelImpl((a,b)=>{const diff=a-b;return diff*diff}),squaredDifference2=binaryKernelFunc(SquaredDifference,squaredDifferenceImpl),squaredDifferenceConfig={kernelName:SquaredDifference,backendName:"cpu",kernelFunc:squaredDifference2},subImpl=createSimpleBinaryKernelImpl((aValue,bValue)=>aValue-bValue),subComplexImpl=createComplexBinaryKernelImpl((aReal,aImag,bReal,bImag)=>({real:aReal-bReal,imag:aImag-bImag})),sub34=binaryKernelFunc(Sub,subImpl,subComplexImpl),subConfig={kernelName:Sub,backendName:"cpu",kernelFunc:sub34};function transposeImpl(xVals,xShape,dtype,perm,newShape){const xRank=xShape.length,xSize=util_exports.sizeFromShape(xShape),xStrides=util_exports.computeStrides(xShape),newStrides=util_exports.computeStrides(newShape),result=util_exports.getTypedArrayFromDType(dtype,util_exports.sizeFromShape(newShape));for(let i=0;i<xSize;++i){const loc=util_exports.indexToLoc(i,xRank,xStrides),newLoc=new Array(loc.length);for(let i2=0;i2<newLoc.length;i2++)newLoc[i2]=loc[perm[i2]];const newIndex=util_exports.locToIndex(newLoc,xRank,newStrides);result[newIndex]=xVals[i]}return result}function uniqueImpl(values,axis,shape,dtype){const $axis=util_exports.parseAxisParam(axis,shape)[0],newShape=[1,shape[0],1];for(let i=0;i<$axis;i++)newShape[0]*=shape[i];newShape[1]=shape[$axis];for(let i=$axis+1;i<shape.length;i++)newShape[2]*=shape[i];const uniqueElements={},indices=new Int32Array(shape[$axis]),inputBuffer=new TensorBuffer(newShape,dtype,values),uniqueIndices=[],is1DTensor=newShape[0]===1&&newShape[2]===1;for(let i=0;i<shape[$axis];i++){let element;if(is1DTensor)element=values[i].toString();else{const axisValues=[];for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)axisValues.push(inputBuffer.get(m,i,n));element=axisValues.join(",")}if(uniqueElements[element]!==void 0)indices[i]=uniqueElements[element];else{const uniqueIndex=Object.keys(uniqueElements).length;uniqueElements[element]=uniqueIndex,indices[i]=uniqueIndex,uniqueIndices.push(i)}}const outputTmpShape=newShape.slice();outputTmpShape[1]=Object.keys(uniqueElements).length;const outputBuffer=new TensorBuffer(outputTmpShape,dtype);uniqueIndices.forEach((uniqueElementIndex,i)=>{for(let m=0;m<newShape[0];m++)for(let n=0;n<newShape[2];n++)outputBuffer.set(inputBuffer.get(m,uniqueElementIndex,n),m,i,n)});const outputShape=shape.slice();return outputShape[$axis]=outputTmpShape[1],{outputValues:outputBuffer.values,outputShape,indices}}const version10="2.7.0";registerBackend("cpu",()=>new MathBackendCPU,1);const elu8=unaryKernelFunc(Elu,xi=>xi>=0?xi:Math.exp(xi)-1),eluConfig={kernelName:Elu,backendName:"cpu",kernelFunc:elu8},preluImpl=createSimpleBinaryKernelImpl((xValue,aValue)=>xValue<0?aValue*xValue:xValue);function prelu7(args){const{inputs,backend:backend3}=args,{x,alpha}=inputs;assertNotComplex([x,alpha],"prelu");const aVals=backend3.data.get(x.dataId).values,bVals=backend3.data.get(alpha.dataId).values,[resultData,resultShape]=preluImpl(x.shape,alpha.shape,aVals,bVals,x.dtype);return backend3.makeTensorInfo(resultShape,x.dtype,resultData)}const preluConfig={kernelName:Prelu,backendName:"cpu",kernelFunc:prelu7},relu9=unaryKernelFunc(Relu,xi=>Math.max(0,xi)),reluConfig={kernelName:Relu,backendName:"cpu",kernelFunc:relu9},relu66=unaryKernelFunc(Relu6,xi=>Math.min(Math.max(0,xi),6)),relu6Config={kernelName:Relu6,backendName:"cpu",kernelFunc:relu66};function applyActivation2(backend3,x,activation2,preluActivationWeights){if(activation2==="linear")return identity2({inputs:{x},backend:backend3});if(activation2==="relu")return relu9({inputs:{x},backend:backend3});if(activation2==="elu")return elu8({inputs:{x},backend:backend3});if(activation2==="relu6")return relu66({inputs:{x},backend:backend3});if(activation2==="prelu")return prelu7({inputs:{x,alpha:preluActivationWeights},backend:backend3});throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`)}function reshape88(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`),backend3.incRef(x.dataId);const xData=backend3.data.get(x.dataId);if(xData.complexTensorInfos!=null){const real8=xData.complexTensorInfos.real,imag8=xData.complexTensorInfos.imag;real8.shape=$shape,imag8.shape=$shape}return{dataId:x.dataId,shape:$shape,dtype:x.dtype}}const reshapeConfig={kernelName:Reshape,backendName:"cpu",kernelFunc:reshape88};function batchMatMul(args){const{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;assertNotComplex([a,b],"matMul");const aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);const outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);const a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape88({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape88({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),sharedDim=transposeA?a3d.shape[1]:a3d.shape[2],leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),a3dValues=backend3.data.get(a3d.dataId).values,b3dValues=backend3.data.get(b3d.dataId).values,a3dStrides=util_exports.computeStrides(a3d.shape),b3dStrides=util_exports.computeStrides(b3d.shape),[aBatch,aOuterStep,aInnerStep]=transposeA?[a3dStrides[0],1,a3dStrides[1]]:[a3dStrides[0],a3dStrides[1],1],[bInnerStep,bOuterStep,bBatch]=transposeB?[1,b3dStrides[1],b3dStrides[0]]:[b3dStrides[1],1,b3dStrides[0]],size=leftDim*rightDim,result=buffer([batchDim,leftDim,rightDim],a3d.dtype),resVals=result.values,blockSize=backend3.blockSize;for(let bi=0;bi<batchDim;bi++)for(let i0=0;i0<leftDim;i0+=blockSize)for(let j0=0;j0<rightDim;j0+=blockSize)for(let k0=0;k0<sharedDim;k0+=blockSize){const iBlock=Math.min(i0+blockSize,leftDim),jBlock=Math.min(j0+blockSize,rightDim),kBlock=Math.min(k0+blockSize,sharedDim);for(let i=i0;i<iBlock;i++)for(let j=j0;j<jBlock;j++){let sum29=0;for(let k=k0;k<kBlock;k++){const batchOffsetA=Math.min(bi,batchDimA-1)*aBatch,batchOffsetB=Math.min(bi,batchDimB-1)*bBatch,aVal=a3dValues[batchOffsetA+i*aOuterStep+k*aInnerStep],bVal=b3dValues[k*bInnerStep+j*bOuterStep+batchOffsetB];sum29+=aVal*bVal}resVals[bi*size+(i*rightDim+j)]+=sum29}}return backend3.disposeIntermediateTensorInfo(a3d),backend3.disposeIntermediateTensorInfo(b3d),backend3.makeTensorInfo(outShape,result.dtype,result.values)}const batchMatMulConfig={kernelName:BatchMatMul,backendName:"cpu",kernelFunc:batchMatMul};function _fusedMatMul(args){const{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs,{transposeA,transposeB,activation:activation2}=attrs;let current,addRes,activationRes;const intermediates=[],matMulRes=batchMatMul({inputs:{a,b},attrs:{transposeA,transposeB},backend:backend3});current=matMulRes,bias&&(addRes=add32({inputs:{a:current,b:bias},backend:backend3}),intermediates.push(current),current=addRes),activation2&&(activationRes=applyActivation2(backend3,current,activation2,preluActivationWeights),intermediates.push(current),current=activationRes);for(const i of intermediates)backend3.disposeIntermediateTensorInfo(i);return current}const _fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"cpu",kernelFunc:_fusedMatMul},acos4=unaryKernelFunc(Acos,xi=>Math.acos(xi)),acosConfig={kernelName:Acos,backendName:"cpu",kernelFunc:acos4},acosh4=unaryKernelFunc(Acosh,xi=>Math.acosh(xi)),acoshConfig={kernelName:Acosh,backendName:"cpu",kernelFunc:acosh4},asin4=unaryKernelFunc(Asin,xi=>Math.asin(xi)),asinConfig={kernelName:Asin,backendName:"cpu",kernelFunc:asin4},asinh4=unaryKernelFunc(Asinh,xi=>Math.asinh(xi)),asinhConfig={kernelName:Asinh,backendName:"cpu",kernelFunc:asinh4},atan5=unaryKernelFunc(Atan,xi=>Math.atan(xi)),atanConfig={kernelName:Atan,backendName:"cpu",kernelFunc:atan5},atanh4=unaryKernelFunc(Atanh,xi=>Math.atanh(xi)),atanhConfig={kernelName:Atanh,backendName:"cpu",kernelFunc:atanh4};function pool5(xValues,xShape,dtype,strides,convInfo,poolType){const strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,initialValue=poolType==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,output=buffer(convInfo.outShape,dtype),outputVals=output.values,outputBatchStrides=convInfo.outShape[1]*convInfo.outShape[2]*convInfo.outShape[3],outputRowStrides=convInfo.outShape[2]*convInfo.outShape[3],outputColStrides=convInfo.outShape[3];for(let b=0;b<convInfo.batchSize;++b){const outputBatchOffset=b*outputBatchStrides,inputBatchOffset=b*strides[0];for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){const xRCorner=yR*strideHeight-padTop,xRMin=Math.max(0,xRCorner),xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner),outputRowOffset=outputBatchOffset+yR*outputRowStrides;for(let yC=0;yC<convInfo.outWidth;++yC){const xCCorner=yC*strideWidth-padLeft,xCMin=Math.max(0,xCCorner),xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner);let minMaxValue=initialValue,avgValue=0,count2=0;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){const xROffset=inputBatchOffset+xR*strides[1];for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){const xCOffset=xROffset+xC*strides[2],pixel=xValues[xCOffset+d];poolType==="max"&&pixel>minMaxValue?minMaxValue=pixel:poolType==="avg"&&(avgValue+=pixel,count2++)}if(isNaN(minMaxValue))break}const outputOffset=outputRowOffset+yC*outputColStrides+d;outputVals[outputOffset]=poolType==="avg"?avgValue/count2:minMaxValue}}}return output}function maxPoolPositions(xValues,xShape,dtype,convInfo,flattenPositions=!1,includeBatchInIndex=!1){const maxPositions=buffer(convInfo.outShape,"int32"),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,xBuf=buffer(xShape,dtype,xValues);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let yR=0;yR<convInfo.outHeight;++yR){const xRCorner=yR*strideHeight-padTop;let xRMin=xRCorner;for(;xRMin<0;)xRMin+=dilationHeight;const xRMax=Math.min(convInfo.inHeight,effectiveFilterHeight+xRCorner);for(let yC=0;yC<convInfo.outWidth;++yC){const xCCorner=yC*strideWidth-padLeft;let xCMin=xCCorner;for(;xCMin<0;)xCMin+=dilationWidth;const xCMax=Math.min(convInfo.inWidth,effectiveFilterWidth+xCCorner);let maxValue=Number.NEGATIVE_INFINITY,maxPosition=-1;for(let xR=xRMin;xR<xRMax;xR+=dilationHeight){const wR=xR-xRCorner;for(let xC=xCMin;xC<xCMax;xC+=dilationWidth){const wC=xC-xCCorner,pixel=xBuf.get(b,xR,xC,d);pixel>maxValue&&(maxValue=pixel,flattenPositions?maxPosition=includeBatchInIndex?((b*convInfo.inHeight+xR)*convInfo.inWidth+xC)*convInfo.inChannels+d:(xR*convInfo.inWidth+xC)*convInfo.inChannels+d:maxPosition=wR*effectiveFilterWidth+wC)}}maxPositions.set(maxPosition,b,yR,yC,d)}}return maxPositions}function avgPool2(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"avgPool");const{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);let res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{const xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"avg");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}const avgPoolConfig={kernelName:AvgPool,backendName:"cpu",kernelFunc:avgPool2};function avgPoolBackprop2(args){const{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex([dy,input2],"avgPoolBackprop");const{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),avgMultiplier=1/(filterHeight*filterWidth),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){const dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft;let dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){const dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){const dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;const pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel}}dx.set(dotProd*avgMultiplier,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}const avgPoolBackpropConfig={kernelName:AvgPoolBackprop,backendName:"cpu",kernelFunc:avgPoolBackprop2};function batchNorm2(args){const{inputs,backend:backend3,attrs}=args,{x,scale:scale2,offset,mean:mean7,variance}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale2==null||mean7.shape.length===scale2.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),assertNotComplex([x,mean7,variance,scale2,offset],"batchNorm");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);const xVals=backend3.data.get(x.dataId).values,mVals=backend3.data.get(mean7.dataId).values,varVals=backend3.data.get(variance.dataId).values,sVals=scale2?backend3.data.get(scale2.dataId).values:new Float32Array([1]),offVals=offset?backend3.data.get(offset.dataId).values:new Float32Array([0]),outVals=new Float32Array(xVals.length),offValsLength=offVals.length,sValsLength=sVals.length,varValsLength=varVals.length,mValsLength=mVals.length;let offi=0,mi=0,si=0,vi=0;for(let i=0;i<xVals.length;++i)outVals[i]=offVals[offi++]+(xVals[i]-mVals[mi++])*sVals[si++]/Math.sqrt(varVals[vi++]+varianceEpsilon),offi>=offValsLength&&(offi=0),mi>=mValsLength&&(mi=0),si>=sValsLength&&(si=0),vi>=varValsLength&&(vi=0);return backend3.makeTensorInfo(x.shape,x.dtype,outVals)}const batchNormConfig={kernelName:FusedBatchNorm,backendName:"cpu",kernelFunc:batchNorm2},clip=unaryKernelFunc(ClipByValue,(xi,attrs)=>{const clipAttrs=attrs;return xi>clipAttrs.clipValueMax?clipAttrs.clipValueMax:xi<clipAttrs.clipValueMin?clipAttrs.clipValueMin:xi}),clipConfig={kernelName:ClipByValue,backendName:"cpu",kernelFunc:clip};function imag6(args){const{inputs,backend:backend3}=args,{input:input2}=inputs,imag8=backend3.data.get(input2.dataId).complexTensorInfos.imag,imagVal=backend3.data.get(imag8.dataId).values;return backend3.makeTensorInfo(imag8.shape,imag8.dtype,imagVal)}const imagConfig={kernelName:Imag,backendName:"cpu",kernelFunc:imag6};function concat17(args){const{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0];let outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);const $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];const shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,$axis),$inputs[0].dtype==="complex64"){const reals=$inputs.map(t=>real6({inputs:{input:t},backend:backend3})),imags=$inputs.map(t=>imag6({inputs:{input:t},backend:backend3})),realConcated=concat17({inputs:reals,backend:backend3,attrs:{axis:$axis}}),imagConcated=concat17({inputs:imags,backend:backend3,attrs:{axis:$axis}}),result=complex9({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result}const inputs2D=$inputs.map(t=>{const innerSize=util_exports.sizeFromShape(t.shape.slice($axis)),shape=[-1,innerSize];return reshape88({inputs:{x:t},backend:backend3,attrs:{shape}})});outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);const outVals=util_exports.getTypedArrayFromDType($inputs[0].dtype,util_exports.sizeFromShape(outShape));if(inputs2D[0].shape[0]===1){let offset=0;inputs2D.forEach(t=>{const val=backend3.data.get(t.dataId).values,size=util_exports.sizeFromShape(t.shape);outVals.set(val,offset),offset+=size})}else{let colOffset=0;inputs2D.forEach(t=>{const tVals=backend3.data.get(t.dataId).values;let tIdx=0;for(let row=0;row<t.shape[0];++row){const resIdx=row*outShape[1]+colOffset;for(let col=0;col<t.shape[1];++col)outVals[resIdx+col]=tVals[tIdx++]}colOffset+=t.shape[1]})}const finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),$axis),outInfo=backend3.makeTensorInfo(finalOutShape,inputs[0].dtype,outVals);return inputs2D.forEach(t=>backend3.disposeIntermediateTensorInfo(t)),outInfo}const concatConfig={kernelName:Concat,backendName:"cpu",kernelFunc:concat17};function conv2D(args){const{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"conv2d");const $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,padLeft=convInfo.padInfo.left,padTop=convInfo.padInfo.top,isChannelsLast=convInfo.dataFormat==="channelsLast",y=new TensorBuffer(convInfo.outShape,x.dtype),xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape),xBatchStride=xStrides[0],xRowStride=isChannelsLast?xStrides[1]:xStrides[2],xColStride=isChannelsLast?xStrides[2]:1,xChannelStride=isChannelsLast?1:xStrides[1],yBatchStride=y.strides[0],yRowStride=isChannelsLast?y.strides[1]:y.strides[2],yColStride=isChannelsLast?y.strides[2]:1,yChannelStride=isChannelsLast?1:y.strides[1],xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){const xOffset1=b*xBatchStride,yOffset1=b*yBatchStride;for(let yR=0;yR<convInfo.outHeight;++yR){const yOffset2=yOffset1+yR*yRowStride,xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){const xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;const wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xRowStride;for(let yC=0;yC<convInfo.outWidth;++yC){const yOffset3=yOffset2+yC*yColStride,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){const xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;const wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*xColStride;let wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){const xVal=xVals[xOffset3+d1*xChannelStride];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset3+d2*yChannelStride]+=xVal*wVals[wOffset3+d2];wOffset3+=convInfo.outChannels}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,yVals)}const conv2DConfig={kernelName:Conv2D,backendName:"cpu",kernelFunc:conv2D};function conv2DBackpropFilter2(args){const{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"conv2dBackpropFilter");const $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,isChannelsLast=convInfo.dataFormat==="channelsLast",dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,xVals=backend3.data.get(x.dataId).values,dyVals=backend3.data.get(dy.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){const yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){const yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d1=0;d1<convInfo.inChannels;++d1)for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){const xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){const xC=wC+yC*strideWidth-leftPad;isChannelsLast?dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2):dotProd+=xBuf.get(b,d1,xR,xC)*dyBuf.get(b,d2,yR,yC)}}dW.set(dotProd,wR,wC,d1,d2)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}const conv2DBackpropFilterConfig={kernelName:Conv2DBackpropFilter,backendName:"cpu",kernelFunc:conv2DBackpropFilter2};function conv2DBackpropInput2(args){const{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{inputShape,strides,pad:pad11,dataFormat,dimRoundingMode}=attrs;assertNotComplex([dy,filter],"conv2dBackpropInput");const filterStrides=util_exports.computeStrides(filter.shape),dyStrides=util_exports.computeStrides(dy.shape);let $dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat);const convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,1,pad11,dimRoundingMode,!1,$dataFormat),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,dyValues=backend3.data.get(dy.dataId).values,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo;$dataFormat=convInfo.dataFormat;const topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=$dataFormat==="channelsLast",xBatchStride=dx.strides[0],xRowStride=isChannelsLast?dx.strides[1]:dx.strides[2],xColStride=isChannelsLast?dx.strides[2]:1,xChannelStride=isChannelsLast?1:dx.strides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1];for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){const xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){const xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth);let dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){const wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){const wC=yC*strideWidth-xCCorner,dyOffset=yBatchStride*b+yRowStride*yR+yColStride*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let d2=0;d2<outChannels;++d2){const pixel=dyValues[dyOffset+yChannelStride*d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}const dxOffset=xBatchStride*b+xRowStride*xR+xColStride*xC+xChannelStride*d1;dxValues[dxOffset]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}const conv2DBackpropInputConfig={kernelName:Conv2DBackpropInput,backendName:"cpu",kernelFunc:conv2DBackpropInput2};function conv3D(args){const{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs;assertNotComplex([x,filter],"conv3d");const convInfo=backend_util_exports.computeConv3DInfo(x.shape,filter.shape,strides,dilations,pad11),{filterDepth,filterHeight,filterWidth,dilationDepth,dilationHeight,dilationWidth,padInfo}=convInfo,padFront=padInfo.front,padLeft=padInfo.left,padTop=padInfo.top,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values,xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);for(let b=0;b<convInfo.batchSize;++b){const xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yF=0;yF<convInfo.outDepth;++yF){const yOffset2=yOffset1+yF*y.strides[1],xFCorner=yF*convInfo.strideDepth-padFront;for(let wF=0;wF<filterDepth;++wF){const xF=xFCorner+wF*dilationDepth;if(xF<0||xF>=convInfo.inDepth)continue;const wOffset1=wF*filterStrides[0],xOffset2=xOffset1+xF*xStrides[1];for(let yR=0;yR<convInfo.outHeight;++yR){const yOffset3=yOffset2+yR*y.strides[2],xRCorner=yR*convInfo.strideHeight-padTop;for(let wR=0;wR<filterHeight;++wR){const xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;const wOffset2=wOffset1+wR*filterStrides[1],xOffset3=xOffset2+xR*xStrides[2];for(let yC=0;yC<convInfo.outWidth;++yC){const yOffset4=yOffset3+yC*convInfo.outChannels,xCCorner=yC*convInfo.strideWidth-padLeft;for(let wC=0;wC<filterWidth;++wC){const xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;const wOffset3=wOffset2+wC*filterStrides[2],xOffset4=xOffset3+xC*convInfo.inChannels;let wOffset4=wOffset3;for(let d1=0;d1<convInfo.inChannels;++d1){const xVal=xVals[xOffset4+d1];for(let d2=0;d2<convInfo.outChannels;++d2)yVals[yOffset4+d2]+=xVal*wVals[wOffset4+d2];wOffset4+=convInfo.outChannels}}}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}const conv3DConfig={kernelName:Conv3D,backendName:"cpu",kernelFunc:conv3D};function conv3DBackpropFilterV2(args){const{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,pad:pad11,filterShape}=attrs;assertNotComplex([x,dy],"conv3dBackpropFilterV2");const xStrides=util_exports.computeStrides(x.shape),dyStrides=util_exports.computeStrides(dy.shape),convInfo=backend_util_exports.computeConv3DInfo(x.shape,filterShape,strides,1,pad11),strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,dw=new TensorBuffer(convInfo.filterShape,"float32"),dwValues=dw.values,[dwS0,dwS1,dwS2,dwS3]=dw.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,xValues=backend3.data.get(x.dataId).values,[xS0,xS1,xS2,xS3]=xStrides,frontPad=convInfo.padInfo.front,leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top;for(let wF=0;wF<filterDepth;++wF){const yFMin=Math.max(0,Math.ceil((frontPad-wF)/strideDepth)),yFMax=Math.min(convInfo.outDepth,(convInfo.inDepth+frontPad-wF)/strideDepth),wOffset1=wF*dwS0;for(let wR=0;wR<filterHeight;++wR){const yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight),wOffset2=wR*dwS1+wOffset1;for(let wC=0;wC<filterWidth;++wC){const yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth),wOffset3=wC*dwS2+wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){const wOffset4=d1*dwS3+wOffset3;for(let d2=0;d2<convInfo.outChannels;++d2){let dotProd=0;for(let b=0;b<convInfo.batchSize;++b){const xOffset1=b*xS0,yOffset1=b*dyS0;for(let yF=yFMin;yF<yFMax;++yF){const xF=wF+yF*strideDepth-frontPad,xOffset2=xF*xS1+xOffset1,yOffset2=yF*dyS1+yOffset1;for(let yR=yRMin;yR<yRMax;++yR){const xR=wR+yR*strideHeight-topPad,xOffset3=xR*xS2+xOffset2,yOffset3=yR*dyS2+yOffset2;for(let yC=yCMin;yC<yCMax;++yC){const xC=wC+yC*strideWidth-leftPad,xOffset4=xC*xS3+xOffset3,yOffset4=yC*dyS3+yOffset3;dotProd+=xValues[xOffset4+d1]*dyValues[yOffset4+d2]}}}}dwValues[wOffset4+d2]=dotProd}}}}}return backend3.makeTensorInfo(dw.shape,dw.dtype,dw.values)}const conv3DBackpropFilterV2Config={kernelName:Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:conv3DBackpropFilterV2};function conv3DBackpropInputV2(args){const{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{pad:pad11,strides,inputShape}=attrs;assertNotComplex([dy],"conv3dBackpropInputV2");const dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv3DInfo(inputShape,filter.shape,strides,1,pad11),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2,dxS3]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2,dyS3]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2,fltS3]=filterStrides,{batchSize,filterDepth,filterHeight,filterWidth,inChannels,inDepth,inHeight,inWidth,outChannels,outDepth,outHeight,outWidth,strideDepth,strideHeight,strideWidth}=convInfo,frontPad=filterDepth-1-convInfo.padInfo.front,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xF=0;xF<inDepth;++xF){const xFCorner=xF-frontPad,xFMin=Math.max(0,Math.ceil(xFCorner/strideDepth)),yFMax=Math.min(outDepth,(filterDepth+xFCorner)/strideDepth);for(let xR=0;xR<inHeight;++xR){const xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){const xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth);let dotProd=0;for(let yF=xFMin;yF<yFMax;++yF){const wF=yF*strideDepth-xFCorner;for(let yR=xRMin;yR<yRMax;++yR){const wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){const wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yF+dyS2*yR+dyS3*yC,fltOffset=fltS0*(filterDepth-1-wF)+fltS1*(filterHeight-1-wR)+fltS2*(filterWidth-1-wC)+fltS3*d1;for(let d2=0;d2<outChannels;++d2){const pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+d2];dotProd+=pixel*weight}}}}dxValues[dxS0*b+dxS1*xF+dxS2*xR+dxS3*xC+d1]=dotProd}}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}const conv3DBackpropInputV2Config={kernelName:Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:conv3DBackpropInputV2},cos6=unaryKernelFunc(Cos,xi=>Math.cos(xi)),cosConfig={kernelName:Cos,backendName:"cpu",kernelFunc:cos6},cosh5=unaryKernelFunc(Cosh,xi=>Math.cosh(xi)),coshConfig={kernelName:Cosh,backendName:"cpu",kernelFunc:cosh5};function depthwiseConv2dNative(args){const{inputs,backend:backend3,attrs}=args,{x,filter}=inputs,{strides,pad:pad11,dilations,dimRoundingMode}=attrs;assertNotComplex([x,filter],"depthwiseConv2DNative");const xStrides=util_exports.computeStrides(x.shape),filterStrides=util_exports.computeStrides(filter.shape);let $dilations=dilations;$dilations==null&&($dilations=[1,1]),util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);const convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),{filterHeight,filterWidth,dilationHeight,dilationWidth,padInfo}=convInfo,padLeft=padInfo.left,padTop=padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,y=new TensorBuffer(convInfo.outShape,x.dtype),xVals=backend3.data.get(x.dataId).values,wVals=backend3.data.get(filter.dataId).values,yVals=y.values;for(let b=0;b<convInfo.batchSize;++b){const xOffset1=b*xStrides[0],yOffset1=b*y.strides[0];for(let yR=0;yR<convInfo.outHeight;++yR){const yOffset2=yOffset1+yR*y.strides[1],xRCorner=yR*convInfo.strideHeight-padLeft;for(let wR=0;wR<filterHeight;++wR){const xR=xRCorner+wR*dilationHeight;if(xR<0||xR>=convInfo.inHeight)continue;const wOffset1=wR*filterStrides[0],xOffset2=xOffset1+xR*xStrides[1];for(let yC=0;yC<convInfo.outWidth;++yC){const yOffset3=yOffset2+yC*y.strides[2],xCCorner=yC*convInfo.strideWidth-padTop;for(let wC=0;wC<filterWidth;++wC){const xC=xCCorner+wC*dilationWidth;if(xC<0||xC>=convInfo.inWidth)continue;const wOffset2=wOffset1+wC*filterStrides[1],xOffset3=xOffset2+xC*convInfo.inChannels;let yOffset4=yOffset3,wOffset3=wOffset2;for(let d1=0;d1<convInfo.inChannels;++d1){const xVal=xVals[xOffset3+d1];for(let q=0;q<chMul;++q)yVals[yOffset4+q]+=xVal*wVals[wOffset3+q];yOffset4+=chMul,wOffset3+=chMul}}}}}}return backend3.makeTensorInfo(y.shape,y.dtype,y.values)}const depthwiseConv2dNativeConfig={kernelName:DepthwiseConv2dNative,backendName:"cpu",kernelFunc:depthwiseConv2dNative};function depthwiseConv2dNativeBackpropFilter2(args){const{inputs,backend:backend3,attrs}=args,{x,dy}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,filterShape}=attrs;assertNotComplex([x,dy],"depthwiseConv2dNativeBackpropFilter");const convInfo=backend_util_exports.computeConv2DInfo(x.shape,filterShape,strides,dilations,pad11,dimRoundingMode,!0),{strideHeight,strideWidth,filterHeight,filterWidth}=convInfo,dW=new TensorBuffer(convInfo.filterShape,"float32"),leftPad=convInfo.padInfo.left,topPad=convInfo.padInfo.top,chMul=convInfo.outChannels/convInfo.inChannels,xVals=backend3.data.get(x.dataId).values,xBuf=new TensorBuffer(x.shape,x.dtype,xVals),dyVals=backend3.data.get(dy.dataId).values,dyBuf=new TensorBuffer(dy.shape,dy.dtype,dyVals);for(let wR=0;wR<filterHeight;++wR){const yRMin=Math.max(0,Math.ceil((topPad-wR)/strideHeight)),yRMax=Math.min(convInfo.outHeight,(convInfo.inHeight+topPad-wR)/strideHeight);for(let wC=0;wC<filterWidth;++wC){const yCMin=Math.max(0,Math.ceil((leftPad-wC)/strideWidth)),yCMax=Math.min(convInfo.outWidth,(convInfo.inWidth+leftPad-wC)/strideWidth);for(let d2=0;d2<convInfo.outChannels;++d2){const d1=Math.trunc(d2/chMul),dm=d2%chMul;let dotProd=0;for(let b=0;b<convInfo.batchSize;++b)for(let yR=yRMin;yR<yRMax;++yR){const xR=wR+yR*strideHeight-topPad;for(let yC=yCMin;yC<yCMax;++yC){const xC=wC+yC*strideWidth-leftPad;dotProd+=xBuf.get(b,xR,xC,d1)*dyBuf.get(b,yR,yC,d2)}}dW.set(dotProd,wR,wC,d1,dm)}}}return backend3.makeTensorInfo(dW.shape,dW.dtype,dW.values)}const depthwiseConv2dNativeBackpropFilterConfig={kernelName:DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropFilter2};function depthwiseConv2dNativeBackpropInput2(args){const{inputs,backend:backend3,attrs}=args,{dy,filter}=inputs,{strides,dilations,pad:pad11,dimRoundingMode,inputShape}=attrs;assertNotComplex([dy,filter],"depthwiseConv2DNativeBackpropInput");const dyStrides=util_exports.computeStrides(dy.shape),filterStrides=util_exports.computeStrides(filter.shape),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),dx=new TensorBuffer(convInfo.inShape,"float32"),dxValues=dx.values,[dxS0,dxS1,dxS2]=dx.strides,dyValues=backend3.data.get(dy.dataId).values,[dyS0,dyS1,dyS2]=dyStrides,fltValues=backend3.data.get(filter.dataId).values,[fltS0,fltS1,fltS2]=filterStrides,{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,chMul=outChannels/inChannels;for(let b=0;b<batchSize;++b)for(let d1=0;d1<inChannels;++d1)for(let xR=0;xR<inHeight;++xR){const xRCorner=xR-topPad,xRMin=Math.max(0,Math.ceil(xRCorner/strideHeight)),yRMax=Math.min(outHeight,(filterHeight+xRCorner)/strideHeight);for(let xC=0;xC<inWidth;++xC){const xCCorner=xC-leftPad,xCMin=Math.max(0,Math.ceil(xCCorner/strideWidth)),yCMax=Math.min(outWidth,(filterWidth+xCCorner)/strideWidth);let dotProd=0;for(let yR=xRMin;yR<yRMax;++yR){const wR=yR*strideHeight-xRCorner;for(let yC=xCMin;yC<yCMax;++yC){const wC=yC*strideWidth-xCCorner,dyOffset=dyS0*b+dyS1*yR+dyS2*yC,fltOffset=fltS0*(filterHeight-1-wR)+fltS1*(filterWidth-1-wC)+fltS2*d1;for(let dm=0;dm<chMul;++dm){const d2=d1*chMul+dm,pixel=dyValues[dyOffset+d2],weight=fltValues[fltOffset+dm];dotProd+=pixel*weight}}}dxValues[dxS0*b+dxS1*xR+dxS2*xC+d1]=dotProd}}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}const depthwiseConv2dNativeBackpropInputConfig={kernelName:DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:depthwiseConv2dNativeBackpropInput2},dilation2dConfig={kernelName:Dilation2D,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{const{x,filter}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,xVals=cpuBackend.data.get(x.dataId).values,xRank=x.shape.length,filterVals=cpuBackend.data.get(filter.dataId).values,filterRank=filter.shape.length,{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations),outSize=util_exports.sizeFromShape(outShape),outRank=outShape.length,outputVals=util_exports.getArrayFromDType(x.dtype,outSize);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){const hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){const wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER;for(let h=0;h<filterHeight;++h){const hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){const wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){const xIndex=util_exports.locToIndex([b,hIn,wIn,d],xRank,util_exports.computeStrides(x.shape)),filterIndex=util_exports.locToIndex([h,w,d],filterRank,util_exports.computeStrides(filter.shape)),val=xVals[xIndex]+filterVals[filterIndex];val>curVal&&(curVal=val)}}}const outputIndex=util_exports.locToIndex([b,hOut,wOut,d],outRank,util_exports.computeStrides(outShape));outputVals[outputIndex]=curVal}}}const dataId=cpuBackend.write(util_exports.toTypedArray(outputVals,x.dtype),outShape,x.dtype);return{dataId,shape:outShape,dtype:x.dtype}}},dilation2dBackpropFilterConfig={kernelName:Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{const{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);const $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(filter.shape,filter.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){const hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){const wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hMax=0,wMax=0;for(let h=0;h<filterHeight;++h){const hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){const wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){const val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hMax=h,wMax=w)}}}gradients8[hMax][wMax][d]+=$dy[b][hOut][wOut][d]}}}const dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),filter.shape,filter.dtype);return{dataId,shape:filter.shape,dtype:filter.dtype}}},dilation2dBackpropInputConfig={kernelName:Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{const{x,filter,dy}=inputs,{strides,pad:pad11,dilations}=attrs,cpuBackend=backend3,$x=util_exports.toNestedArray(x.shape,cpuBackend.data.get(x.dataId).values),$filter=util_exports.toNestedArray(filter.shape,cpuBackend.data.get(filter.dataId).values),{batchSize,inHeight,inWidth,inChannels,outHeight,outWidth,padInfo,strideHeight,strideWidth,filterHeight,filterWidth,dilationHeight,dilationWidth,outShape}=backend_util_exports.computeDilation2DInfo(x.shape,filter.shape,strides,pad11,"NHWC",dilations);util_exports.assert(dy.rank===outShape.length,()=>`Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);const $dy=util_exports.toNestedArray(outShape,cpuBackend.data.get(dy.dataId).values),gradients8=util_exports.makeZerosNestedTypedArray(x.shape,x.dtype);for(let b=0;b<batchSize;++b)for(let hOut=0;hOut<outHeight;++hOut){const hBeg=hOut*strideHeight-padInfo.top;for(let wOut=0;wOut<outWidth;++wOut){const wBeg=wOut*strideWidth-padInfo.left;for(let d=0;d<inChannels;++d){let curVal=Number.MIN_SAFE_INTEGER,hInMax=hBeg<0?0:hBeg,wInMax=wBeg<0?0:wBeg;for(let h=0;h<filterHeight;++h){const hIn=hBeg+h*dilationHeight;if(hIn>=0&&hIn<inHeight)for(let w=0;w<filterWidth;++w){const wIn=wBeg+w*dilationWidth;if(wIn>=0&&wIn<inWidth){const val=$x[b][hIn][wIn][d]+$filter[h][w][d];val>curVal&&(curVal=val,hInMax=hIn,wInMax=wIn)}}}gradients8[b][hInMax][wInMax][d]+=$dy[b][hOut][wOut][d]}}}const dataId=cpuBackend.write(util_exports.toTypedArray(gradients8,x.dtype),x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}},divImpl=createSimpleBinaryKernelImpl((a,b)=>a/b),div35=binaryKernelFunc(Div,divImpl),divConfig={kernelName:Div,backendName:"cpu",kernelFunc:div35},p=backend_util_exports.ERF_P,a1=backend_util_exports.ERF_A1,a2=backend_util_exports.ERF_A2,a3=backend_util_exports.ERF_A3,a4=backend_util_exports.ERF_A4,a5=backend_util_exports.ERF_A5,erf4=unaryKernelFunc(Erf,xi=>{const sign5=Math.sign(xi),v=Math.abs(xi),t=1/(1+p*v);return sign5*(1-((((a5*t+a4)*t+a3)*t+a2)*t+a1)*t*Math.exp(-v*v))}),erfConfig={kernelName:Erf,backendName:"cpu",kernelFunc:erf4};function fftBatch(input2,inverse,cpuBackend){const inputShape=input2.shape,batch=inputShape[0],innerDim=inputShape[1],inputVals=cpuBackend.data.get(input2.dataId),real2D=inputVals.complexTensorInfos.real,imag2D=inputVals.complexTensorInfos.imag,resultShape=[batch,innerDim],resultSize=util_exports.sizeFromShape(resultShape),resultReal=util_exports.getTypedArrayFromDType("float32",resultSize),resultImag=util_exports.getTypedArrayFromDType("float32",resultSize);for(let b=0;b<batch;b++){const r=slice19({inputs:{x:real2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),i=slice19({inputs:{x:imag2D},backend:cpuBackend,attrs:{begin:[b,0],size:[1,innerDim]}}),input3=complex9({inputs:{real:r,imag:i},backend:cpuBackend}),{real:real8,imag:imag8}=fftImpl(input3,inverse,cpuBackend),res=backend_util_exports.mergeRealAndImagArrays(real8,imag8);for(let d=0;d<innerDim;d++){const c=backend_util_exports.getComplexWithIndex(res,d);resultReal[b*innerDim+d]=c.real,resultImag[b*innerDim+d]=c.imag}cpuBackend.disposeIntermediateTensorInfo(r),cpuBackend.disposeIntermediateTensorInfo(i),cpuBackend.disposeIntermediateTensorInfo(input3)}const $realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultReal),$imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",resultImag),result=complex9({inputs:{real:$realInfo,imag:$imagInfo},backend:cpuBackend});return cpuBackend.disposeIntermediateTensorInfo($realInfo),cpuBackend.disposeIntermediateTensorInfo($imagInfo),result}function fftImpl(input2,inverse,cpuBackend){const inputSize=util_exports.sizeFromShape(input2.shape),inputVals=cpuBackend.data.get(input2.dataId),realVals=cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values,imagVals=cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;if(isExponentOf2(inputSize)){const result=fftRadix2(realVals,imagVals,inputSize,inverse,cpuBackend),resultShape=[input2.shape[0],input2.shape[1]];if(inverse){const realInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.real),imagInfo=cpuBackend.makeTensorInfo(resultShape,"float32",result.imag),sizeInfo=cpuBackend.makeTensorInfo([],"float32",util_exports.createScalarValue(inputSize,"float32")),sizeInfoCopy=identity2({inputs:{x:sizeInfo},backend:cpuBackend}),divRealInfo=divConfig.kernelFunc({inputs:{a:realInfo,b:sizeInfo},backend:cpuBackend}),divImagInfo=divConfig.kernelFunc({inputs:{a:imagInfo,b:sizeInfoCopy},backend:cpuBackend}),divRealVals=cpuBackend.data.get(divRealInfo.dataId).values,divImagVals=cpuBackend.data.get(divImagInfo.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(realInfo),cpuBackend.disposeIntermediateTensorInfo(imagInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfo),cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy),cpuBackend.disposeIntermediateTensorInfo(divRealInfo),cpuBackend.disposeIntermediateTensorInfo(divImagInfo),{real:divRealVals,imag:divImagVals}}return result}else{const data2=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),rawOutput=fourierTransformByMatmul(data2,inputSize,inverse);return backend_util_exports.splitRealAndImagArrays(rawOutput)}}function isExponentOf2(size){return(size&size-1)===0}function fftRadix2(realVals,imagVals,size,inverse,cpuBackend){if(size===1)return{real:realVals,imag:imagVals};const data2=backend_util_exports.mergeRealAndImagArrays(realVals,imagVals),half=size/2,evenComplex=backend_util_exports.complexWithEvenIndex(data2),evenRealVals=evenComplex.real,evenImagVals=evenComplex.imag,evenShape=[evenRealVals.length],evenRealInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenRealVals),evenImagInfo=cpuBackend.makeTensorInfo(evenShape,"float32",evenImagVals),evenTensorInfo=complex9({inputs:{real:evenRealInfo,imag:evenImagInfo},backend:cpuBackend}),oddComplex=backend_util_exports.complexWithOddIndex(data2),oddRealVals=oddComplex.real,oddImagVals=oddComplex.imag,oddShape=[oddRealVals.length],oddRealInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddRealVals),oddImagInfo=cpuBackend.makeTensorInfo(oddShape,"float32",oddImagVals),oddTensorInfo=complex9({inputs:{real:oddRealInfo,imag:oddImagInfo},backend:cpuBackend}),$evenComplex=fftRadix2(evenRealVals,evenImagVals,half,inverse,cpuBackend),$evenRealVals=$evenComplex.real,$evenImagVals=$evenComplex.imag,$evenShape=[$evenRealVals.length],$evenRealInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenRealVals),$evenImagInfo=cpuBackend.makeTensorInfo($evenShape,"float32",$evenImagVals),$evenTensorInfo=complex9({inputs:{real:$evenRealInfo,imag:$evenImagInfo},backend:cpuBackend}),$oddComplex=fftRadix2(oddRealVals,oddImagVals,half,inverse,cpuBackend),$oddRealVals=$oddComplex.real,$oddImagVals=$oddComplex.imag,$oddShape=[$oddRealVals.length],$oddRealInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddRealVals),$oddImagInfo=cpuBackend.makeTensorInfo($oddShape,"float32",$oddImagVals),$oddTensorInfo=complex9({inputs:{real:$oddRealInfo,imag:$oddImagInfo},backend:cpuBackend}),e=backend_util_exports.exponents(size,inverse),eShape=[e.real.length],eRealInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.real),eImagInfo=cpuBackend.makeTensorInfo(eShape,"float32",e.imag),complexInfo=complex9({inputs:{real:eRealInfo,imag:eImagInfo},backend:cpuBackend}),exponentInfo=multiply2({inputs:{a:complexInfo,b:$oddTensorInfo},backend:cpuBackend}),addPart=add32({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),subPart=sub34({inputs:{a:$evenTensorInfo,b:exponentInfo},backend:cpuBackend}),addPartReal=real6({inputs:{input:addPart},backend:cpuBackend}),subPartReal=real6({inputs:{input:subPart},backend:cpuBackend}),addPartImag=imag6({inputs:{input:addPart},backend:cpuBackend}),subPartImag=imag6({inputs:{input:subPart},backend:cpuBackend}),$real=concat17({inputs:[addPartReal,subPartReal],backend:cpuBackend,attrs:{axis:0}}),$imag=concat17({inputs:[addPartImag,subPartImag],backend:cpuBackend,attrs:{axis:0}}),$realVals=cpuBackend.data.get($real.dataId).values,$imagVals=cpuBackend.data.get($imag.dataId).values;return cpuBackend.disposeIntermediateTensorInfo(evenRealInfo),cpuBackend.disposeIntermediateTensorInfo(evenImagInfo),cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo(oddRealInfo),cpuBackend.disposeIntermediateTensorInfo(oddImagInfo),cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo($evenRealInfo),cpuBackend.disposeIntermediateTensorInfo($evenImagInfo),cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo),cpuBackend.disposeIntermediateTensorInfo($oddRealInfo),cpuBackend.disposeIntermediateTensorInfo($oddImagInfo),cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo),cpuBackend.disposeIntermediateTensorInfo(eRealInfo),cpuBackend.disposeIntermediateTensorInfo(eImagInfo),cpuBackend.disposeIntermediateTensorInfo(complexInfo),cpuBackend.disposeIntermediateTensorInfo(exponentInfo),cpuBackend.disposeIntermediateTensorInfo(addPart),cpuBackend.disposeIntermediateTensorInfo(subPart),cpuBackend.disposeIntermediateTensorInfo(addPartReal),cpuBackend.disposeIntermediateTensorInfo(addPartImag),cpuBackend.disposeIntermediateTensorInfo(subPartReal),cpuBackend.disposeIntermediateTensorInfo(subPartImag),cpuBackend.disposeIntermediateTensorInfo($real),cpuBackend.disposeIntermediateTensorInfo($imag),{real:$realVals,imag:$imagVals}}function fourierTransformByMatmul(data2,size,inverse){const ret=new Float32Array(size*2);for(let r=0;r<size;r++){let real8=0,imag8=0;for(let c=0;c<size;c++){const e=backend_util_exports.exponent(r*c,size,inverse),term=backend_util_exports.getComplexWithIndex(data2,c);real8+=term.real*e.real-term.imag*e.imag,imag8+=term.real*e.imag+term.imag*e.real}inverse&&(real8/=size,imag8/=size),backend_util_exports.assignToTypedArray(ret,real8,imag8,r)}return ret}function fft6(args){const{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!1,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}const fftConfig={kernelName:FFT,backendName:"cpu",kernelFunc:fft6};function fill5(args){const{backend:backend3,attrs}=args,{shape,value,dtype}=attrs,$dtype=dtype||util_exports.inferDtype(value),values=util_exports.getArrayFromDType($dtype,util_exports.sizeFromShape(shape));return fillValues(values,value,$dtype),backend3.makeTensorInfo(shape,$dtype,values)}const fillConfig={kernelName:Fill,backendName:"cpu",kernelFunc:fill5};function fillValues(values,value,dtype){dtype==="string",values.fill(value)}const flipLeftRightConfig={kernelName:FlipLeftRight,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{const{image:image3}=inputs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){const batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){const rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){const colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){const coords2=[batch,row,col,channel],x=coords2[2],coordX=Math.round(imageWidth-x),outIdx=batchOffset+rowOffset+colOffset+channel;let outputValue=imageVals[outIdx];if(coordX>=0&&coordX<imageWidth){const rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}output[outIdx]=outputValue}}}}const dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}};function fusedConv2D(args){const{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs;let result=conv2D({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){const resultOld=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(resultOld)}if(activation2){const resultOld=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(resultOld)}return result}const fusedConv2DConfig={kernelName:FusedConv2D,backendName:"cpu",kernelFunc:fusedConv2D};function fusedDepthwiseConv2D(args){const{inputs,backend:backend3,attrs}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dataFormat,dilations,dimRoundingMode,activation:activation2}=attrs;let result=depthwiseConv2dNative({inputs:{x,filter},backend:backend3,attrs:{strides,pad:pad11,dataFormat,dilations,dimRoundingMode}});if(bias){const oldResult=result;result=add32({inputs:{a:result,b:bias},backend:backend3}),backend3.disposeIntermediateTensorInfo(oldResult)}if(activation2){const oldResult=result;result=applyActivation2(backend3,result,activation2,preluActivationWeights),backend3.disposeIntermediateTensorInfo(oldResult)}return result}const fusedDepthwiseConv2DConfig={kernelName:FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:fusedDepthwiseConv2D};function ifft6(args){const{inputs,backend:backend3}=args,{input:input2}=inputs,inputSize=util_exports.sizeFromShape(input2.shape),innerDimensionSize=input2.shape[input2.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape88({inputs:{x:input2},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),result=fftBatch(input2D,!0,backend3),resultReshaped=reshape88({inputs:{x:result},backend:backend3,attrs:{shape:input2.shape}});return backend3.disposeIntermediateTensorInfo(input2D),backend3.disposeIntermediateTensorInfo(result),resultReshaped}const ifftConfig={kernelName:IFFT,backendName:"cpu",kernelFunc:ifft6},isFinite3=unaryKernelFunc(IsFinite,xi=>Number.isFinite(xi)?1:0,"bool"),isFiniteConfig={kernelName:IsFinite,backendName:"cpu",kernelFunc:isFinite3},isInf2=unaryKernelFunc(IsInf,xi=>Math.abs(xi)===Infinity?1:0,"bool"),isInfConfig={kernelName:IsInf,backendName:"cpu",kernelFunc:isInf2},isNaN3=unaryKernelFunc(IsNan,xi=>Number.isNaN(xi)?1:0,"bool"),isNaNConfig={kernelName:IsNan,backendName:"cpu",kernelFunc:isNaN3},log1p5=unaryKernelFunc(Log1p,xi=>Math.log1p(xi)),log1pConfig={kernelName:Log1p,backendName:"cpu",kernelFunc:log1p5},logicalNot2=unaryKernelFunc(LogicalNot,xi=>xi?0:1,"bool"),logicalNotConfig={kernelName:LogicalNot,backendName:"cpu",kernelFunc:logicalNot2},maxConfig={kernelName:Max,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{const{x}=inputs,{reductionIndices,keepDims}=attrs,cpuBackend=backend3;let xShape=x.shape;const xRank=xShape.length,origAxes=util_exports.parseAxisParam(reductionIndices,xShape);let axes=origAxes;const permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank);let xVals=cpuBackend.data.get(x.dataId).values;if(permutedAxes!=null){const newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];xVals=transposeImpl(xVals,xShape,x.dtype,permutedAxes,newShape),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xShape=newShape}assertNotComplex(x,"max"),backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);const[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(xShape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),result=maxImpl(xVals,reduceSize,maxOutShape,x.dtype),dataId=cpuBackend.write(result,maxOutShape,x.dtype);let outShape=maxOutShape;if(keepDims){const newShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes);outShape=newShape}return{dataId,shape:outShape,dtype:x.dtype}}};function maxPool2(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex(x,"maxPool");const{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);let res;if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))res=identity2({inputs:{x},backend:backend3});else{const xValues=backend3.data.get(x.dataId).values,strides2=util_exports.computeStrides(x.shape),buffer11=pool5(xValues,x.shape,x.dtype,strides2,convInfo,"max");res=backend3.makeTensorInfo(convInfo.outShape,x.dtype,buffer11.values)}return res}const maxPoolConfig={kernelName:MaxPool,backendName:"cpu",kernelFunc:maxPool2};function maxPoolBackprop2(args){const{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex([input2,output],"maxPoolBackprop");const{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),xValues=backend3.data.get(x.dataId).values,maxPosBuf=buffer(convInfo.outShape,x.dtype,maxPoolPositions(xValues,x.shape,x.dtype,convInfo).values),strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,dx=buffer(x.shape,"float32"),dyData=backend3.data.get(dy.dataId).values,dyBuf=buffer(dy.shape,"float32",dyData);for(let b=0;b<convInfo.batchSize;++b)for(let d=0;d<convInfo.inChannels;++d)for(let dxR=0;dxR<convInfo.inHeight;++dxR)for(let dxC=0;dxC<convInfo.inWidth;++dxC){const dyRCorner=dxR-padTop,dyCCorner=dxC-padLeft;let dotProd=0;for(let wR=0;wR<effectiveFilterHeight;wR+=dilationHeight){const dyR=(dyRCorner+wR)/strideHeight;if(dyR<0||dyR>=convInfo.outHeight||Math.floor(dyR)!==dyR)continue;for(let wC=0;wC<effectiveFilterWidth;wC+=dilationWidth){const dyC=(dyCCorner+wC)/strideWidth;if(dyC<0||dyC>=convInfo.outWidth||Math.floor(dyC)!==dyC)continue;const maxPos=effectiveFilterHeight*effectiveFilterWidth-1-maxPosBuf.get(b,dyR,dyC,d),curPos=wR*effectiveFilterWidth+wC,mask=maxPos===curPos?1:0;if(mask===0)continue;const pixel=dyBuf.get(b,dyR,dyC,d);dotProd+=pixel*mask}}dx.set(dotProd,b,dxR,dxC,d)}return backend3.makeTensorInfo(dx.shape,dx.dtype,dx.values)}const maxPoolBackpropConfig={kernelName:MaxPoolBackprop,backendName:"cpu",kernelFunc:maxPoolBackprop2};function maxPoolWithArgmaxImpl(xValues,xShape,dtype,includeBatchInIndex,convInfo){const strides=util_exports.computeStrides(xShape),maxPools=pool5(xValues,xShape,dtype,strides,convInfo,"max"),maxPositions=maxPoolPositions(xValues,xShape,dtype,convInfo,!0,includeBatchInIndex);return[maxPools.values,maxPositions.values]}const maxPoolWithArgmaxConfig={kernelName:MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{const{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,cpuBackend=backend3;assertNotComplex(x,"MaxPoolWithArgmax");const values=cpuBackend.data.get(x.dataId).values,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,[1,1],pad11),[pooled,indexes]=maxPoolWithArgmaxImpl(values,x.shape,x.dtype,includeBatchInIndex,convInfo),pooledDataId=cpuBackend.write(pooled,convInfo.outShape,x.dtype),indexesDataId=cpuBackend.write(indexes,convInfo.outShape,x.dtype);return[{dataId:pooledDataId,shape:convInfo.outShape,dtype:x.dtype},{dataId:indexesDataId,shape:convInfo.outShape,dtype:"int32"}]}};function mirrorPad2(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,mode}=attrs;assertNotComplex(x,"mirrorPad");const outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),end=paddings.map((p2,i)=>p2[0]+x.shape[i]),offset=mode==="reflect"?0:1,xVals=backend3.data.get(x.dataId).values,xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);for(let i=0;i<resultSize;i++){let coords2=util_exports.indexToLoc(i,resultRank,resultStrides);for(let i2=0;i2<resultRank;i2++)coords2[i2]<start[i2]?coords2[i2]=start[i2]*2-coords2[i2]-offset:coords2[i2]>=end[i2]&&(coords2[i2]=(end[i2]-1)*2-coords2[i2]+offset);coords2=coords2.map((c,i2)=>c-start[i2]);const inIndex=util_exports.locToIndex(coords2,xRank,xStrides);resVals[i]=xVals[inIndex]}const outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}const mirrorPadConfig={kernelName:MirrorPad,backendName:"cpu",kernelFunc:mirrorPad2},nonMaxSuppressionV4Impl2=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config={kernelName:NonMaxSuppressionV4,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{const{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionPadded");const boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl2(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}},nonMaxSuppressionV5Impl2=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config={kernelName:NonMaxSuppressionV5,backendName:"cpu",kernelFunc:({inputs,backend:backend3,attrs})=>{const{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,cpuBackend=backend3;assertNotComplex(boxes,"NonMaxSuppressionWithScore");const boxesVals=cpuBackend.data.get(boxes.dataId).values,scoresVals=cpuBackend.data.get(scores.dataId).values,maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl2(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}};function padV2(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{paddings,constantValue}=attrs;assertNotComplex(x,"pad");const outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),start=paddings.map(p2=>p2[0]),xVals=backend3.data.get(x.dataId).values,xSize=util_exports.sizeFromShape(x.shape),xRank=x.shape.length,xStrides=util_exports.computeStrides(x.shape),resultSize=util_exports.sizeFromShape(outShape),resultRank=outShape.length,resultStrides=util_exports.computeStrides(outShape),resVals=util_exports.getTypedArrayFromDType(x.dtype,resultSize);constantValue!==0&&resVals.fill(constantValue);for(let i=0;i<xSize;i++){const coords2=util_exports.indexToLoc(i,xRank,xStrides),outCoords=coords2.map((c,i2)=>c+start[i2]),outIndex=util_exports.locToIndex(outCoords,resultRank,resultStrides);resVals[outIndex]=xVals[i]}const outId=backend3.write(resVals,outShape,x.dtype);return{dataId:outId,shape:outShape,dtype:x.dtype}}const padV2Config={kernelName:PadV2,backendName:"cpu",kernelFunc:padV2},reciprocal4=unaryKernelFunc(Reciprocal,xi=>1/xi),reciprocalConfig={kernelName:Reciprocal,backendName:"cpu",kernelFunc:reciprocal4},rotateWithOffsetConfig={kernelName:RotateWithOffset,backendName:"cpu",kernelFunc:({inputs,attrs,backend:backend3})=>{const{image:image3}=inputs,{radians,fillValue,center}=attrs,cpuBackend=backend3,output=util_exports.getTypedArrayFromDType(image3.dtype,util_exports.sizeFromShape(image3.shape)),[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fullOpacityValue=255,sinFactor=Math.sin(radians),cosFactor=Math.cos(radians),imageVals=cpuBackend.data.get(image3.dataId).values;for(let batchIdx=0;batchIdx<batch;batchIdx++){const batchOffset=batchIdx*imageWidth*imageHeight*numChannels;for(let row=0;row<imageHeight;row++){const rowOffset=row*(imageWidth*numChannels);for(let col=0;col<imageWidth;col++){const colOffset=col*numChannels;for(let channel=0;channel<numChannels;channel++){const coords2=[batch,row,col,channel],x=coords2[2],y=coords2[1];let coordX=(x-centerX)*cosFactor-(y-centerY)*sinFactor,coordY=(x-centerX)*sinFactor+(y-centerY)*cosFactor;coordX=Math.round(coordX+centerX),coordY=Math.round(coordY+centerY);let outputValue=fillValue;if(typeof fillValue!="number"&&(channel===3?outputValue=fullOpacityValue:outputValue=fillValue[channel]),coordX>=0&&coordX<imageWidth&&coordY>=0&&coordY<imageHeight){const rotatedRowOffset=coordY*(imageWidth*numChannels),rotatedColOffset=coordX*numChannels,imageIdx=batchOffset+rotatedRowOffset+rotatedColOffset+channel;outputValue=imageVals[imageIdx]}const outIdx=batchOffset+rowOffset+colOffset+channel;output[outIdx]=outputValue}}}}const dataId=cpuBackend.write(output,image3.shape,image3.dtype);return{dataId,shape:image3.shape,dtype:image3.dtype}}},round4=unaryKernelFunc(Round,xi=>{const base2=Math.floor(xi);return xi-base2<.5?Math.floor(xi):xi-base2>.5?Math.ceil(xi):base2%2===0?base2:base2+1}),roundConfig={kernelName:Round,backendName:"cpu",kernelFunc:round4},scaleAlpha=backend_util_exports.SELU_SCALEALPHA,scale=backend_util_exports.SELU_SCALE,selu5=unaryKernelFunc(Selu,xi=>xi>=0?scale*xi:scaleAlpha*(Math.exp(xi)-1)),seluConfig={kernelName:Selu,backendName:"cpu",kernelFunc:selu5},sigmoid7=unaryKernelFunc(Sigmoid,xi=>1/(1+Math.exp(-xi))),sigmoidConfig={kernelName:Sigmoid,backendName:"cpu",kernelFunc:sigmoid7},sign4=unaryKernelFunc(Sign,xi=>xi<0?-1:xi>0?1:0),signConfig={kernelName:Sign,backendName:"cpu",kernelFunc:sign4},sin5=unaryKernelFunc(Sin,xi=>Math.sin(xi)),sinConfig={kernelName:Sin,backendName:"cpu",kernelFunc:sin5},sinh5=unaryKernelFunc(Sinh,xi=>Math.sinh(xi)),sinhConfig={kernelName:Sinh,backendName:"cpu",kernelFunc:sinh5},epsilon2=11920928955078125e-23,threshold=Math.log(epsilon2)+2,softplus5=unaryKernelFunc(Softplus,xi=>{const tooLarge=xi>-threshold,tooSmall=xi<threshold,expX=Math.exp(xi);let result;return tooSmall?result=expX:tooLarge?result=xi:result=Math.log(1+expX),result}),softplusConfig={kernelName:Softplus,backendName:"cpu",kernelFunc:softplus5};function transpose18(args){const{inputs,attrs,backend:backend3}=args,{x}=inputs,{perm}=attrs;assertNotComplex(x,"transpose");const xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];const values=backend3.data.get(x.dataId).values,result=transposeImpl(values,x.shape,x.dtype,perm,newShape),dataId=backend3.write(result,newShape,x.dtype);return{dataId,shape:newShape,dtype:x.dtype}}const transposeConfig={kernelName:Transpose,backendName:"cpu",kernelFunc:transpose18};function spaceToBatchND2(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{blockShape,paddings}=attrs;assertNotComplex([x],"spaceToBatchND");const prod5=util_exports.sizeFromShape(blockShape),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);const paddedX=padV2Config.kernelFunc({inputs:{x},backend:backend3,attrs:{paddings:completePaddings,constantValue:0}}),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),reshapeInputs={x:paddedX},reshapeAttrs={shape:reshapedPaddedShape},paddedXReshaped=reshape88({inputs:reshapeInputs,backend:backend3,attrs:reshapeAttrs}),transposeInputs={x:paddedXReshaped},transposeAttrs={perm:permutedReshapedPaddedPermutation},paddedXT=transpose18({inputs:transposeInputs,backend:backend3,attrs:transposeAttrs}),resultReshapeInputs={x:paddedXT},resultReshapeAttrs={shape:flattenShape},result=reshape88({inputs:resultReshapeInputs,backend:backend3,attrs:resultReshapeAttrs});return backend3.disposeIntermediateTensorInfo(paddedX),backend3.disposeIntermediateTensorInfo(paddedXReshaped),backend3.disposeIntermediateTensorInfo(paddedXT),result}const spaceToBatchNDConfig={kernelName:SpaceToBatchND,backendName:"cpu",kernelFunc:spaceToBatchND2},sqrt13=unaryKernelFunc(Sqrt,xi=>Math.sqrt(xi)),sqrtConfig={kernelName:Sqrt,backendName:"cpu",kernelFunc:sqrt13},squareConfig={kernelName:Square,backendName:"cpu",kernelFunc:({inputs,backend:backend3})=>{const{x}=inputs,cpuBackend=backend3;assertNotComplex(x,"square");const values=cpuBackend.data.get(x.dataId).values,newValues=new Float32Array(values.length);for(let i=0;i<values.length;++i){const value=values[i];newValues[i]=value*value}const dataId=cpuBackend.write(newValues,x.shape,x.dtype);return{dataId,shape:x.shape,dtype:x.dtype}}},step8=unaryKernelFunc(Step,(xi,attrs)=>{const stepAttrs=attrs;return isNaN(xi)?NaN:xi>0?1:stepAttrs.alpha}),stepConfig={kernelName:Step,backendName:"cpu",kernelFunc:step8},tan4=unaryKernelFunc(Tan,xi=>Math.tan(xi)),tanConfig={kernelName:Tan,backendName:"cpu",kernelFunc:tan4},tanh6=unaryKernelFunc(Tanh,xi=>Math.tanh(xi)),tanhConfig={kernelName:Tanh,backendName:"cpu",kernelFunc:tanh6};function unique6(args){const{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex(x,"unique");const values=backend3.data.get(x.dataId).values,{outputValues,outputShape,indices}=uniqueImpl(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}const uniqueConfig={kernelName:Unique,backendName:"cpu",kernelFunc:unique6},kernelConfigs=[_fusedMatMulConfig,absConfig,acosConfig,acoshConfig,addConfig,asinConfig,asinhConfig,atanConfig,atanhConfig,avgPoolConfig,avgPoolBackpropConfig,batchMatMulConfig,batchNormConfig,castConfig,ceilConfig,clipConfig,complexConfig,concatConfig,conv2DBackpropFilterConfig,conv2DBackpropInputConfig,conv2DConfig,conv3DBackpropFilterV2Config,conv3DBackpropInputV2Config,conv3DConfig,cosConfig,coshConfig,depthwiseConv2dNativeConfig,depthwiseConv2dNativeBackpropFilterConfig,depthwiseConv2dNativeBackpropInputConfig,dilation2dConfig,dilation2dBackpropInputConfig,dilation2dBackpropFilterConfig,divConfig,eluConfig,erfConfig,expConfig,expm1Config,fftConfig,fillConfig,flipLeftRightConfig,floorConfig,fusedConv2DConfig,fusedDepthwiseConv2DConfig,identityConfig,ifftConfig,imagConfig,isFiniteConfig,isInfConfig,isNaNConfig,logConfig,log1pConfig,logicalNotConfig,maxPoolConfig,maxPoolBackpropConfig,maxPoolWithArgmaxConfig,maxConfig,mirrorPadConfig,multiplyConfig,nonMaxSuppressionV4Config,nonMaxSuppressionV5Config,notEqualConfig,padV2Config,preluConfig,realConfig,reciprocalConfig,reluConfig,relu6Config,reshapeConfig,rotateWithOffsetConfig,roundConfig,rsqrtConfig,seluConfig,sigmoidConfig,signConfig,sinConfig,sinhConfig,sliceConfig,softplusConfig,spaceToBatchNDConfig,sqrtConfig,squareConfig,squaredDifferenceConfig,stepConfig,subConfig,tanConfig,tanhConfig,transposeConfig,uniqueConfig];for(const kernelConfig of kernelConfigs)registerKernel(kernelConfig);const contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function setWebGLContext(webGLVersion,gl){contexts[webGLVersion]=gl}function getWebGLContext(webGLVersion){if(!(webGLVersion in contexts)){const newCtx=getWebGLRenderingContext(webGLVersion);if(newCtx!==null)contexts[webGLVersion]=newCtx;else return console.log("Could not get context for WebGL version",webGLVersion),null}const gl=contexts[webGLVersion];return gl.isContextLost()?(delete contexts[webGLVersion],getWebGLContext(webGLVersion)):(gl.disable(gl.DEPTH_TEST),gl.disable(gl.STENCIL_TEST),gl.disable(gl.BLEND),gl.disable(gl.DITHER),gl.disable(gl.POLYGON_OFFSET_FILL),gl.disable(gl.SAMPLE_COVERAGE),gl.enable(gl.SCISSOR_TEST),gl.enable(gl.CULL_FACE),gl.cullFace(gl.BACK),contexts[webGLVersion])}function createCanvas(webGLVersion){if(typeof OffscreenCanvas!="undefined"&&webGLVersion===2)return new OffscreenCanvas(300,150);if(typeof document!="undefined")return document.createElement("canvas");throw new Error("Cannot create a canvas in this context")}function getWebGLRenderingContext(webGLVersion){if(webGLVersion!==1&&webGLVersion!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");const canvas=createCanvas(webGLVersion);return canvas.addEventListener("webglcontextlost",ev=>{ev.preventDefault(),delete contexts[webGLVersion]},!1),webGLVersion===1?canvas.getContext("webgl",WEBGL_ATTRIBUTES)||canvas.getContext("experimental-webgl",WEBGL_ATTRIBUTES):canvas.getContext("webgl2",WEBGL_ATTRIBUTES)}var PackingScheme;(function(PackingScheme2){PackingScheme2[PackingScheme2.DENSE=0]="DENSE",PackingScheme2[PackingScheme2.SHARED_BATCH=1]="SHARED_BATCH"})(PackingScheme||(PackingScheme={}));var TextureUsage;(function(TextureUsage2){TextureUsage2[TextureUsage2.RENDER=0]="RENDER",TextureUsage2[TextureUsage2.UPLOAD=1]="UPLOAD",TextureUsage2[TextureUsage2.PIXELS=2]="PIXELS",TextureUsage2[TextureUsage2.DOWNLOAD=3]="DOWNLOAD"})(TextureUsage||(TextureUsage={}));var PhysicalTextureType;(function(PhysicalTextureType2){PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",PhysicalTextureType2[PhysicalTextureType2.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",PhysicalTextureType2[PhysicalTextureType2.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(PhysicalTextureType||(PhysicalTextureType={}));function getUnpackedMatrixTextureShapeWidthHeight(rows,columns){return[columns,rows]}function getUnpackedArraySizeFromMatrixSize(matrixSize,channelsPerTexture){return matrixSize*channelsPerTexture}function getDenseTexShape(shape){const size=util_exports.sizeFromShape(shape),texelsNeeded=Math.ceil(size/4);return util_exports.sizeToSquarishShape(texelsNeeded)}function getPackedMatrixTextureShapeWidthHeight(rows,columns){return[Math.max(1,Math.ceil(columns/2)),Math.max(1,Math.ceil(rows/2))]}function getPackedRGBAArraySizeFromMatrixShape(rows,columns){const[w,h]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return w*h*4}function getTextureConfig(gl,textureHalfFloatExtension){const glany=gl;let internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat;return env().getNumber("WEBGL_VERSION")===2?(internalFormatFloat=glany.R32F,internalFormatHalfFloat=glany.R16F,internalFormatPackedHalfFloat=glany.RGBA16F,internalFormatPackedFloat=glany.RGBA32F,textureFormatFloat=glany.RED,downloadUnpackNumChannels=4,defaultNumChannels=1,textureTypeHalfFloat=glany.HALF_FLOAT,textureTypeFloat=glany.FLOAT):(internalFormatFloat=gl.RGBA,internalFormatHalfFloat=gl.RGBA,internalFormatPackedHalfFloat=gl.RGBA,internalFormatPackedFloat=glany.RGBA,textureFormatFloat=gl.RGBA,downloadUnpackNumChannels=4,defaultNumChannels=4,textureTypeHalfFloat=textureHalfFloatExtension!=null?textureHalfFloatExtension.HALF_FLOAT_OES:null,textureTypeFloat=gl.FLOAT),downloadTextureFormat=gl.RGBA,{internalFormatFloat,internalFormatHalfFloat,internalFormatPackedHalfFloat,internalFormatPackedFloat,textureFormatFloat,downloadTextureFormat,downloadUnpackNumChannels,defaultNumChannels,textureTypeHalfFloat,textureTypeFloat}}function callAndCheck(gl,func2){const returnValue=func2();return env().getBool("DEBUG")&&checkWebGLError(gl),returnValue}function checkWebGLError(gl){const error=gl.getError();if(error!==gl.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(gl,error))}const MIN_FLOAT16=596e-10,MAX_FLOAT16=65504;function canBeRepresented(num){return!!(env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||num===0||MIN_FLOAT16<Math.abs(num)&&Math.abs(num)<MAX_FLOAT16)}function getWebGLErrorMessage(gl,status){switch(status){case gl.NO_ERROR:return"NO_ERROR";case gl.INVALID_ENUM:return"INVALID_ENUM";case gl.INVALID_VALUE:return"INVALID_VALUE";case gl.INVALID_OPERATION:return"INVALID_OPERATION";case gl.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case gl.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case gl.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${status}`}}function getExtensionOrThrow(gl,extensionName){return throwIfNull(gl,()=>gl.getExtension(extensionName),'Extension "'+extensionName+'" not supported on this browser.')}function createVertexShader(gl,vertexShaderSource){const vertexShader=throwIfNull(gl,()=>gl.createShader(gl.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(vertexShader,vertexShaderSource)),callAndCheck(gl,()=>gl.compileShader(vertexShader)),gl.getShaderParameter(vertexShader,gl.COMPILE_STATUS)===!1)throw console.log(gl.getShaderInfoLog(vertexShader)),new Error("Failed to compile vertex shader.");return vertexShader}function createFragmentShader(gl,fragmentShaderSource){const fragmentShader=throwIfNull(gl,()=>gl.createShader(gl.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(callAndCheck(gl,()=>gl.shaderSource(fragmentShader,fragmentShaderSource)),callAndCheck(gl,()=>gl.compileShader(fragmentShader)),gl.getShaderParameter(fragmentShader,gl.COMPILE_STATUS)===!1)throw logShaderSourceAndInfoLog(fragmentShaderSource,gl.getShaderInfoLog(fragmentShader)),new Error("Failed to compile fragment shader.");return fragmentShader}const lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(shaderSource,shaderInfoLog){const lineNumberRegexResult=lineNumberRegex.exec(shaderInfoLog);if(lineNumberRegexResult==null){console.log(`Couldn't parse line number in error: ${shaderInfoLog}`),console.log(shaderSource);return}const lineNumber=+lineNumberRegexResult[1],shaderLines=shaderSource.split(`
`),pad11=shaderLines.length.toString().length+2,linesWithLineNumbers=shaderLines.map((line,lineNumber2)=>util_exports.rightPad((lineNumber2+1).toString(),pad11)+line);let maxLineLength=0;for(let i=0;i<linesWithLineNumbers.length;i++)maxLineLength=Math.max(linesWithLineNumbers[i].length,maxLineLength);const beforeErrorLines=linesWithLineNumbers.slice(0,lineNumber-1),errorLine=linesWithLineNumbers.slice(lineNumber-1,lineNumber),afterErrorLines=linesWithLineNumbers.slice(lineNumber);console.log(beforeErrorLines.join(`
`)),console.log(shaderInfoLog.split(`
`)[0]),console.log(`%c ${util_exports.rightPad(errorLine[0],maxLineLength)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(afterErrorLines.join(`
`))}function createProgram(gl){return throwIfNull(gl,()=>gl.createProgram(),"Unable to create WebGLProgram.")}function linkProgram(gl,program){if(callAndCheck(gl,()=>gl.linkProgram(program)),gl.getProgramParameter(program,gl.LINK_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(gl,program){if(callAndCheck(gl,()=>gl.validateProgram(program)),gl.getProgramParameter(program,gl.VALIDATE_STATUS)===!1)throw console.log(gl.getProgramInfoLog(program)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(gl,data2){const buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ARRAY_BUFFER,data2,gl.STATIC_DRAW)),buffer11}function createStaticIndexBuffer(gl,data2){const buffer11=throwIfNull(gl,()=>gl.createBuffer(),"Unable to create WebGLBuffer");return callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER,data2,gl.STATIC_DRAW)),buffer11}function createTexture(gl){return throwIfNull(gl,()=>gl.createTexture(),"Unable to create WebGLTexture.")}function validateTextureSize(width,height){const maxTextureSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(width<=0||height<=0){const requested=`[${width}x${height}]`;throw new Error("Requested texture size "+requested+" is invalid.")}if(width>maxTextureSize||height>maxTextureSize){const requested=`[${width}x${height}]`,max10=`[${maxTextureSize}x${maxTextureSize}]`;throw new Error("Requested texture size "+requested+" greater than WebGL maximum on this browser / GPU "+max10+".")}}function createFramebuffer(gl){return throwIfNull(gl,()=>gl.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(gl,program,attribute,buffer11,arrayEntriesPerItem,itemStrideInBytes,itemOffsetInBytes){const loc=gl.getAttribLocation(program,attribute);return loc===-1?!1:(callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,buffer11)),callAndCheck(gl,()=>gl.vertexAttribPointer(loc,arrayEntriesPerItem,gl.FLOAT,!1,itemStrideInBytes,itemOffsetInBytes)),callAndCheck(gl,()=>gl.enableVertexAttribArray(loc)),!0)}function bindTextureUnit(gl,texture,textureUnit){validateTextureUnit(gl,textureUnit),callAndCheck(gl,()=>gl.activeTexture(gl.TEXTURE0+textureUnit)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture))}function getProgramUniformLocationOrThrow(gl,program,uniformName){return throwIfNull(gl,()=>gl.getUniformLocation(program,uniformName),'uniform "'+uniformName+'" not present in program.')}function getProgramUniformLocation(gl,program,uniformName){return gl.getUniformLocation(program,uniformName)}function bindTextureToProgramUniformSampler(gl,texture,uniformSamplerLocation,textureUnit){callAndCheck(gl,()=>bindTextureUnit(gl,texture,textureUnit)),callAndCheck(gl,()=>gl.uniform1i(uniformSamplerLocation,textureUnit))}function bindColorTextureToFramebuffer(gl,texture,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0))}function unbindColorTextureFromFramebuffer(gl,framebuffer){callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,framebuffer)),callAndCheck(gl,()=>gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,null,0))}function validateFramebuffer(gl){const status=gl.checkFramebufferStatus(gl.FRAMEBUFFER);if(status!==gl.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(gl,status))}function getFramebufferErrorMessage(gl,status){switch(status){case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case gl.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${status}`}}function throwIfNull(gl,returnTOrNull,failureMessage){const tOrNull=callAndCheck(gl,()=>returnTOrNull());if(tOrNull==null)throw new Error(failureMessage);return tOrNull}function validateTextureUnit(gl,textureUnit){const maxTextureUnit=gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,glTextureUnit=textureUnit+gl.TEXTURE0;if(glTextureUnit<gl.TEXTURE0||glTextureUnit>maxTextureUnit){const textureUnitRange=`[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;throw new Error(`textureUnit must be in ${textureUnitRange}.`)}}function getBatchDim(shape,dimsToSkip=2){return util_exports.sizeFromShape(shape.slice(0,shape.length-dimsToSkip))}function getRowsCols(shape){if(shape.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[shape.length>1?shape[shape.length-2]:1,shape[shape.length-1]]}function getShapeAs3D(shape){let shapeAs3D=[1,1,1];const isScalar=shape.length===0||shape.length===1&&shape[0]===1;return isScalar||(shapeAs3D=[getBatchDim(shape),...getRowsCols(shape)]),shapeAs3D}function getTextureShapeFromLogicalShape(logShape,isPacked=!1){let maxTexSize=env().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(isPacked&&(maxTexSize=maxTexSize*2,logShape=logShape.map((d,i)=>i>=logShape.length-2?util_exports.nearestLargerEven(logShape[i]):logShape[i]),logShape.length===1&&(logShape=[2,logShape[0]])),logShape.length!==2){const squeezeResult=util_exports.squeezeShape(logShape);logShape=squeezeResult.newShape}let size=util_exports.sizeFromShape(logShape);if(logShape.length<=1&&size<=maxTexSize)return[1,size];if(logShape.length===2&&logShape[0]<=maxTexSize&&logShape[1]<=maxTexSize)return logShape;if(logShape.length===3&&logShape[0]*logShape[1]<=maxTexSize&&logShape[2]<=maxTexSize)return[logShape[0]*logShape[1],logShape[2]];if(logShape.length===3&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]];if(logShape.length===4&&logShape[0]*logShape[1]*logShape[2]<=maxTexSize&&logShape[3]<=maxTexSize)return[logShape[0]*logShape[1]*logShape[2],logShape[3]];if(logShape.length===4&&logShape[0]<=maxTexSize&&logShape[1]*logShape[2]*logShape[3]<=maxTexSize)return[logShape[0],logShape[1]*logShape[2]*logShape[3]];if(isPacked){const batchDim=getBatchDim(logShape);let rows=2,cols=2;return logShape.length&&([rows,cols]=getRowsCols(logShape)),size=batchDim*(rows/2)*(cols/2),util_exports.sizeToSquarishShape(size).map(d=>d*2)}return util_exports.sizeToSquarishShape(size)}function isEven(n){return n%2===0}function isReshapeFree(shape1,shape2){if(shape1=shape1.slice(-2),shape2=shape2.slice(-2),util_exports.arraysEqual(shape1,shape2))return!0;if(!shape1.length||!shape2.length)return!0;if(shape1[0]===0||shape1[1]===0||shape2[0]===0||shape2[1]===0)return!0;if(shape1.length!==shape2.length){const shape1Cols=shape1.slice(-1)[0],shape2Cols=shape2.slice(-1)[0];if(shape1Cols===shape2Cols)return!0;if(isEven(shape1Cols)&&isEven(shape2Cols)&&(shape1[0]===1||shape2[0]===1))return!0}return shape1[1]===shape2[1]&&isEven(shape1[0])&&isEven(shape2[0])}let MAX_TEXTURE_SIZE,MAX_TEXTURES_IN_SHADER;function getWebGLMaxTextureSize(webGLVersion){if(MAX_TEXTURE_SIZE==null){const gl=getWebGLContext(webGLVersion);MAX_TEXTURE_SIZE=gl.getParameter(gl.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getMaxTexturesInShader(webGLVersion){if(MAX_TEXTURES_IN_SHADER==null){const gl=getWebGLContext(webGLVersion);MAX_TEXTURES_IN_SHADER=gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,MAX_TEXTURES_IN_SHADER)}function getWebGLDisjointQueryTimerVersion(webGLVersion){if(webGLVersion===0)return 0;let queryTimerVersion;const gl=getWebGLContext(webGLVersion);return hasExtension(gl,"EXT_disjoint_timer_query_webgl2")&&webGLVersion===2?queryTimerVersion=2:hasExtension(gl,"EXT_disjoint_timer_query")?queryTimerVersion=1:queryTimerVersion=0,queryTimerVersion}function hasExtension(gl,extensionName){const ext=gl.getExtension(extensionName);return ext!=null}function isWebGLVersionEnabled(webGLVersion){try{const gl=getWebGLContext(webGLVersion);if(gl!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function isCapableOfRenderingToFloatTexture(webGLVersion){if(webGLVersion===0)return!1;const gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1}else if(!hasExtension(gl,"EXT_color_buffer_float"))return!1;const isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function isDownloadFloatTextureEnabled(webGLVersion){if(webGLVersion===0)return!1;const gl=getWebGLContext(webGLVersion);if(webGLVersion===1){if(!hasExtension(gl,"OES_texture_float"))return!1;if(!hasExtension(gl,"WEBGL_color_buffer_float"))return!1}else{if(hasExtension(gl,"EXT_color_buffer_float"))return createFloatTextureAndBindToFramebuffer(gl);const COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(hasExtension(gl,COLOR_BUFFER_HALF_FLOAT)){const textureHalfFloatExtension=gl.getExtension(COLOR_BUFFER_HALF_FLOAT);return createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension)}return!1}const isFrameBufferComplete=createFloatTextureAndBindToFramebuffer(gl);return isFrameBufferComplete}function createFloatTextureAndBindToFramebuffer(gl){const texConfig=getTextureConfig(gl),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);const width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeFloat,null);const frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);const isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function createHalfFloatTextureAndBindToFramebuffer(gl,textureHalfFloatExtension){const texConfig=getTextureConfig(gl,textureHalfFloatExtension),texture=gl.createTexture();gl.bindTexture(gl.TEXTURE_2D,texture);const width=1,height=1;gl.texImage2D(gl.TEXTURE_2D,0,texConfig.internalFormatHalfFloat,width,height,0,texConfig.textureFormatFloat,texConfig.textureTypeHalfFloat,null);const frameBuffer=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,frameBuffer),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0);const isFrameBufferComplete=gl.checkFramebufferStatus(gl.FRAMEBUFFER)===gl.FRAMEBUFFER_COMPLETE;return gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),gl.deleteTexture(texture),gl.deleteFramebuffer(frameBuffer),isFrameBufferComplete}function isWebGLFenceEnabled(webGLVersion){if(webGLVersion!==2)return!1;const gl=getWebGLContext(webGLVersion),isEnabled=gl.fenceSync!=null;return isEnabled}function assertNotComplex2(tensor168,opName){Array.isArray(tensor168)||(tensor168=[tensor168]),tensor168.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the WebGL backend.`)})}const ENV3=env();ENV3.registerFlag("HAS_WEBGL",()=>ENV3.getNumber("WEBGL_VERSION")>0);ENV3.registerFlag("WEBGL_VERSION",()=>isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0);ENV3.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);ENV3.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>ENV3.get("WEBGL_VERSION")===2);ENV3.registerFlag("WEBGL_CPU_FORWARD",()=>!0);ENV3.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);ENV3.registerFlag("WEBGL_PACK",()=>ENV3.getBool("HAS_WEBGL"));ENV3.registerFlag("WEBGL_PACK_NORMALIZATION",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_CLIP",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);ENV3.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_PACK_REDUCE",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_LAZILY_UNPACK",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_CONV_IM2COL",()=>ENV3.getBool("WEBGL_PACK"));ENV3.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>getWebGLMaxTextureSize(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>getMaxTexturesInShader(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{const webGLVersion=ENV3.getNumber("WEBGL_VERSION");return webGLVersion===0?0:getWebGLDisjointQueryTimerVersion(webGLVersion)});ENV3.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>ENV3.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!device_util_exports.isMobile());ENV3.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>isCapableOfRenderingToFloatTexture(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>ENV3.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:ENV3.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));ENV3.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>isDownloadFloatTextureEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_FENCE_API_ENABLED",()=>isWebGLFenceEnabled(ENV3.getNumber("WEBGL_VERSION")));ENV3.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>{const useUniforms=ENV3.getBool("WEBGL_RENDER_FLOAT32_ENABLED");return useUniforms?4:0});ENV3.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,threshold2=>{if(threshold2<0&&threshold2!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold2}.`)});const{simpleAbsImpl:simpleAbsImplCPU,addImpl:addImplCPU,ceilImpl:ceilImplCPU,expImpl:expImplCPU,expm1Impl:expm1ImplCPU,floorImpl:floorImplCPU,logImpl:logImplCPU,maxImpl:maxImplCPU,multiplyImpl:multiplyImplCPU,rsqrtImpl:rsqrtImplCPU,sliceImpl:sliceImplCPU,subImpl:subImplCPU,transposeImpl:transposeImplCPU,uniqueImpl:uniqueImplCPU}=shared_exports;class AddNProgram{constructor(outputShape,shapes){this.outputShape=[],this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);const snippets=[];this.variableNames.forEach(variable3=>{snippets.push(`float v${variable3} = get${variable3}AtOutCoords();`)});const operation211=this.variableNames.map(variable3=>`v${variable3}`).join(" + ");this.userCode=`
      void main() {
        ${snippets.join(`
        `)}

        float result = ${operation211};
        setOutput(result);
      }
    `}}class AddNPackedProgram{constructor(outputShape,shapes){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape,this.variableNames=shapes.map((_,i)=>`T${i}`);const snippets=[];this.variableNames.forEach(variable3=>{snippets.push(`vec4 v${variable3} = get${variable3}AtOutCoords();`)});const operation211=this.variableNames.map(variable3=>`v${variable3}`).join(" + ");this.userCode=`
      void main() {
        ${snippets.join(`
        `)}

        vec4 result = ${operation211};
        setOutput(result);
      }
    `}}class ArgMinMaxProgram{constructor(reduceInfo,op2,firstPass){this.variableNames=["A"];const{windowSize,batchSize,outSize}=reduceInfo;firstPass||this.variableNames.push("bestIndicesA"),this.outputShape=[batchSize,outSize];const compOp=op2==="max"?">":"<",indexSnippet=firstPass?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${windowSize}; i++) {
          int inIdx = ${indexSnippet};
          float candidate = getA(batch, inIdx);
          if (candidate ${compOp} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}}function getVecChannels(name,rank){return["x","y","z","w","u","v"].slice(0,rank).map(d=>`${name}.${d}`)}function getChannels(name,rank){return rank===1?[name]:getVecChannels(name,rank)}function getSourceCoords(rank,dims){if(rank===1)return"rc";let coords2="";for(let i=0;i<rank;i++)coords2+=dims[i],i<rank-1&&(coords2+=",");return coords2}function getGlslDifferences(){let version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound;return env().getNumber("WEBGL_VERSION")===2?(version19="#version 300 es",attribute="in",varyingVs="out",varyingFs="in",texture2D="texture",output="outputColor",defineOutput="out vec4 outputColor;",defineSpecialNaN=`
      bool isnan_custom(float val) {
        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
      }

      #define isnan(value) isnan_custom(value)
    `,defineSpecialInf="",defineRound=`
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `):(version19="",attribute="attribute",varyingVs="varying",varyingFs="varying",texture2D="texture2D",output="gl_FragColor",defineOutput="",defineSpecialNaN=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,defineSpecialInf=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,defineRound=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:version19,attribute,varyingVs,varyingFs,texture2D,output,defineOutput,defineSpecialNaN,defineSpecialInf,defineRound}}function getLogicalCoordinatesFromFlatIndex(coords2,shape,index="index"){const strides=util_exports.computeStrides(shape);return strides.map((stride,i)=>{const line1=`int ${coords2[i]} = ${index} / ${stride}`,line2=i===strides.length-1?`int ${coords2[i+1]} = ${index} - ${coords2[i]} * ${stride}`:`index -= ${coords2[i]} * ${stride}`;return`${line1}; ${line2};`}).join("")}function getFlatIndexFrom3D(shape){const strides=util_exports.computeStrides(shape).map(d=>d.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
  }
`}const ENCODE_FLOAT_SNIPPET=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`,{getBroadcastDims:getBroadcastDims2}=backend_util_exports;function makeShader(inputsInfo,outputShape,userCode,usesPackedTextures){const prefixSnippets=[];inputsInfo.forEach(x=>{const size=util_exports.sizeFromShape(x.shapeInfo.logicalShape);x.shapeInfo.isUniform?prefixSnippets.push(`uniform float ${x.name}${size>1?`[${size}]`:""};`):(prefixSnippets.push(`uniform sampler2D ${x.name};`),prefixSnippets.push(`uniform int offset${x.name};`))});const inputPrefixSnippet=prefixSnippets.join(`
`),inputSamplingSnippet=inputsInfo.map(x=>getInputSamplingSnippet(x,outputShape,usesPackedTextures)).join(`
`),outTexShape=outputShape.texShape,glsl=getGlslDifferences(),floatTextureSampleSnippet=getFloatTextureSampleSnippet(glsl);let outputSamplingSnippet,floatTextureSetOutputSnippet,shaderPrefix=getShaderPrefix(glsl);outputShape.isPacked?(outputSamplingSnippet=getPackedOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRGBASnippet(glsl)):(outputSamplingSnippet=getOutputSamplingSnippet(outputShape.logicalShape,outTexShape),floatTextureSetOutputSnippet=getFloatTextureSetRSnippet(glsl)),usesPackedTextures&&(shaderPrefix+=SHADER_PACKED_PREFIX);const source=[shaderPrefix,floatTextureSampleSnippet,floatTextureSetOutputSnippet,inputPrefixSnippet,outputSamplingSnippet,inputSamplingSnippet,userCode].join(`
`);return source}function getSamplerFromInInfo(inInfo){const shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getSamplerScalar(inInfo);case 1:return getSampler1D(inInfo);case 2:return getSampler2D(inInfo);case 3:return getSampler3D(inInfo);case 4:return getSampler4D(inInfo);case 5:return getSampler5D(inInfo);case 6:return getSampler6D(inInfo);default:throw new Error(`${shape.length}-D input sampling is not yet supported`)}}function getPackedSamplerFromInInfo(inInfo){const shape=inInfo.shapeInfo.logicalShape;switch(shape.length){case 0:return getPackedSamplerScalar(inInfo);case 1:return getPackedSampler1D(inInfo);case 2:return getPackedSampler2D(inInfo);case 3:return getPackedSampler3D(inInfo);default:return getPackedSamplerND(inInfo)}}function getInputSamplingSnippet(inInfo,outShapeInfo,usesPackedTextures=!1){let res="";usesPackedTextures?res+=getPackedSamplerFromInInfo(inInfo):res+=getSamplerFromInInfo(inInfo);const inShape=inInfo.shapeInfo.logicalShape,outShape=outShapeInfo.logicalShape;return inShape.length<=outShape.length&&(usesPackedTextures?res+=getPackedSamplerAtOutputCoords(inInfo,outShapeInfo):res+=getSamplerAtOutputCoords(inInfo,outShapeInfo)),res}function getPackedOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(outShape,outTexShape);case 2:return getOutputPacked2DCoords(outShape,outTexShape);case 3:return getOutputPacked3DCoords(outShape,outTexShape);default:return getOutputPackedNDCoords(outShape,outTexShape)}}function getOutputSamplingSnippet(outShape,outTexShape){switch(outShape.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(outShape,outTexShape);case 2:return getOutput2DCoords(outShape,outTexShape);case 3:return getOutput3DCoords(outShape,outTexShape);case 4:return getOutput4DCoords(outShape,outTexShape);case 5:return getOutput5DCoords(outShape,outTexShape);case 6:return getOutput6DCoords(outShape,outTexShape);default:throw new Error(`${outShape.length}-D output sampling is not yet supported`)}}function getFloatTextureSampleSnippet(glsl){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${glsl.texture2D}(textureSampler, uv).r;
    }
  `}function getFloatTextureSetRSnippet(glsl){return`
    void setOutput(float val) {
      ${glsl.output} = vec4(val, 0, 0, 0);
    }
  `}function getFloatTextureSetRGBASnippet(glsl){return`
    void setOutput(vec4 val) {
      ${glsl.output} = val;
    }
  `}function getShaderPrefix(glsl){const SHADER_PREFIX=`${glsl.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${glsl.varyingFs} vec2 resultUV;
    ${glsl.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${glsl.defineSpecialNaN}
    ${glsl.defineSpecialInf}
    ${glsl.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${SAMPLE_1D_SNIPPET}
    ${SAMPLE_2D_SNIPPET}
    ${SAMPLE_3D_SNIPPET}
  `;return SHADER_PREFIX}const SAMPLE_1D_SNIPPET=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,SAMPLE_2D_SNIPPET=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,SAMPLE_3D_SNIPPET=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,SHADER_PACKED_PREFIX=`
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;function getOutputScalarCoords(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function getOutputPacked1DCoords(shape,texShape){const packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];return packedTexShape[0]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
      }
    `:packedTexShape[1]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
    }
  `}function getOutput1DCoords(shape,texShape){return texShape[0]===1?`
      int getOutputCoords() {
        return int(resultUV.x * ${texShape[1]}.0);
      }
    `:texShape[1]===1?`
      int getOutputCoords() {
        return int(resultUV.y * ${texShape[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      return resTexRC.x * ${texShape[1]} + resTexRC.y;
    }
  `}function getOutputPacked3DCoords(shape,texShape){const packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[2]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;

      int b = index / ${texelsInBatch};
      index -= b * ${texelsInBatch};

      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec3(b, r, c);
    }
  `}function getOutput3DCoords(shape,texShape){const coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec3(r, c, d);
    }
  `}function getOutputPackedNDCoords(shape,texShape){const packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texelsInLogicalRow=Math.ceil(shape[shape.length-1]/2),texelsInBatch=texelsInLogicalRow*Math.ceil(shape[shape.length-2]/2);let texelsInBatchN=texelsInBatch,batches="",coords2="b, r, c";for(let b=2;b<shape.length-1;b++)texelsInBatchN*=shape[shape.length-b-1],batches=`
      int b${b} = index / ${texelsInBatchN};
      index -= b${b} * ${texelsInBatchN};
    `+batches,coords2=`b${b}, `+coords2;return`
    ivec${shape.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;

      ${batches}

      int b = index / ${texelsInBatch};
      index -= b * ${texelsInBatch};

      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec${shape.length}(${coords2});
    }
  `}function getOutput4DCoords(shape,texShape){const coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],shape);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec4(r, c, d, d2);
    }
  `}function getOutput5DCoords(shape,texShape){const coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],shape);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
                             ${texShape[1]}));

      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;

      ${coordsFromIndexSnippet}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function getOutput6DCoords(shape,texShape){const coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],shape);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;

      ${coordsFromIndexSnippet}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function getOutputPacked2DCoords(shape,texShape){const packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(util_exports.arraysEqual(shape,texShape))return`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      }
    `;const texelsInLogicalRow=Math.ceil(shape[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));

      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec2(r, c);
    }
  `}function getOutput2DCoords(shape,texShape){return util_exports.arraysEqual(shape,texShape)?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
      }
    `:shape[1]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${texShape[0]}, ${texShape[1]}));
        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:shape[0]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${texShape[0]}, ${texShape[1]}));
        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      int r = index / ${shape[1]};
      int c = index - r * ${shape[1]};
      return ivec2(r, c);
    }
  `}function getFlatOffsetUniformName(texName){return`offset${texName}`}function getPackedSamplerScalar(inputInfo){const texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),glsl=getGlslDifferences();return`
    vec4 ${funcName}() {
      return ${glsl.texture2D}(${texName}, halfCR);
    }
  `}function getSamplerScalar(inputInfo){const texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`float ${funcName}() {return ${texName};}`;const[texNumR,texNumC]=inputInfo.shapeInfo.texShape;if(texNumR===1&&texNumC===1)return`
      float ${funcName}() {
        return sampleTexture(${texName}, halfCR);
      }
    `;const[tNumR,tNumC]=inputInfo.shapeInfo.texShape,offset=getFlatOffsetUniformName(texName);return`
    float ${funcName}() {
      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
      return sampleTexture(${texName}, uv);
    }
  `}function getPackedSampler1D(inputInfo){const texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],glsl=getGlslDifferences();return`
    vec4 ${funcName}(int index) {
      vec2 uv = packedUVfrom1D(
        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `}function getSampler1D(inputInfo){const texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1);if(inputInfo.shapeInfo.isUniform)return`
      float ${funcName}(int index) {
        ${getUniformSampler(inputInfo)}
      }
    `;const texShape=inputInfo.shapeInfo.texShape,tNumR=texShape[0],tNumC=texShape[1];if(tNumC===1&&tNumR===1)return`
      float ${funcName}(int index) {
        return sampleTexture(${texName}, halfCR);
      }
    `;const offset=getFlatOffsetUniformName(texName);return tNumC===1?`
      float ${funcName}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `:tNumR===1?`
      float ${funcName}(int index) {
        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
        return sampleTexture(${texName}, uv);
      }
    `:`
    float ${funcName}(int index) {
      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `}function getPackedSampler2D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],glsl=getGlslDifferences();if(texShape!=null&&util_exports.arraysEqual(shape,texShape))return`
      vec4 ${funcName}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);

        return ${glsl.texture2D}(${texName}, uv);
      }
    `;const packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],valuesPerRow=Math.ceil(shape[1]/2);return`
    vec4 ${funcName}(int row, int col) {
      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `}function getSampler2D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape;if(texShape!=null&&util_exports.arraysEqual(shape,texShape)){const texNumR2=texShape[0],texNumC2=texShape[1];return`
    float ${funcName}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
      return sampleTexture(${texName}, uv);
    }
  `}const{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){const newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col"];return`
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col) {
        return ${funcName}(${getSqueezedParams(params,keptDims)});
      }
    `}if(inputInfo.shapeInfo.isUniform)return`
      float ${funcName}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;const texNumR=texShape[0],texNumC=texShape[1],offset=getFlatOffsetUniformName(texName);return texNumC===1?`
    float ${funcName}(int row, int col) {
      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `:texNumR===1?`
    float ${funcName}(int row, int col) {
      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
      return sampleTexture(${texName}, uv);
    }
  `:`
  float ${funcName}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${shape[1]} + col + ${offset};
    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
    return sampleTexture(${texName}, uv);
  }
`}function getPackedSampler3D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)];if(shape[0]===1){const squeezedShape=shape.slice(1),keptDims=[1,2],newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["b","row","col"];return`
        ${getPackedSamplerFromInInfo(newInputInfo)}
        vec4 ${funcName}(int b, int row, int col) {
          return ${funcName}(${getSqueezedParams(params,keptDims)});
        }
      `}const texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[2]/2),texelsInBatch=valuesPerRow*Math.ceil(shape[1]/2),glsl=getGlslDifferences();return`
    vec4 ${funcName}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `}function getSampler3D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride0=shape[1]*shape[2],stride1=shape[2],{newShape,keptDims}=util_exports.squeezeShape(shape),squeezedShape=newShape;if(squeezedShape.length<shape.length){const newInputInfo=squeezeInputInfo(inputInfo,squeezedShape),params=["row","col","depth"];return`
        ${getSamplerFromInInfo(newInputInfo)}
        float ${funcName}(int row, int col, int depth) {
          return ${funcName}(${getSqueezedParams(params,keptDims)});
        }
      `}if(inputInfo.shapeInfo.isUniform)return`
      float ${funcName}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${stride0}, ${stride1}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;const texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1],flatOffset=inputInfo.shapeInfo.flatOffset;if(texNumC===stride0&&flatOffset==null)return`
        float ${funcName}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${texNumC}.0, ${texNumR}.0);
          return sampleTexture(${texName}, uv);
        }
      `;if(texNumC===stride1&&flatOffset==null)return`
    float ${funcName}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `;const offset=getFlatOffsetUniformName(texName);return`
      float ${funcName}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
        return sampleTexture(${texName}, uv);
      }
  `}function getPackedSamplerND(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,rank=shape.length,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),texShape=inputInfo.shapeInfo.texShape,packedTexShape=[Math.ceil(texShape[0]/2),Math.ceil(texShape[1]/2)],texNumR=packedTexShape[0],texNumC=packedTexShape[1],valuesPerRow=Math.ceil(shape[rank-1]/2);let texelsInBatch=valuesPerRow*Math.ceil(shape[rank-2]/2),params="int b, int row, int col",index=`b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;for(let b=2;b<rank-1;b++)params=`int b${b}, `+params,texelsInBatch*=shape[rank-b-1],index=`b${b} * ${texelsInBatch} + `+index;const glsl=getGlslDifferences();return`
    vec4 ${funcName}(${params}) {
      int index = ${index};
      int texR = index / ${texNumC};
      int texC = index - texR * ${texNumC};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
      return ${glsl.texture2D}(${texName}, uv);
    }
  `}function getSampler4D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride2=shape[3],stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){const newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2"];return`
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth, int depth2) {
        return ${funcName}(${getSqueezedParams(params,keptDims)});
      }
    `}if(inputInfo.shapeInfo.isUniform)return`
      float ${funcName}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;const flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${stride1}, ${stride2}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;if(texNumC===stride2&&flatOffset==null)return`
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${shape[1]*shape[2]}, ${shape[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;const offset=getFlatOffsetUniformName(texName);return`
    float ${funcName}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} +
          depth * ${stride2} + depth2;
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `}function getSampler5D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),stride3=shape[4],stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1,{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){const newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3"];return`
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        return ${funcName}(${getSqueezedParams(params,keptDims)});
      }
    `}if(inputInfo.shapeInfo.isUniform)return`
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
          depth3;
        ${getUniformSampler(inputInfo)}
      }
    `;const flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;if(texNumC===stride3&&flatOffset==null)return`
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${shape[1]*shape[2]*shape[3]},
               ${shape[2]*shape[3]}, ${shape[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;const offset=getFlatOffsetUniformName(texName);return`
    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
          depth2 * ${stride3} + depth3 + ${offset};
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
      return sampleTexture(${texName}, uv);
    }
  `}function getSampler6D(inputInfo){const shape=inputInfo.shapeInfo.logicalShape,texName=inputInfo.name,funcName="get"+texName.charAt(0).toUpperCase()+texName.slice(1),{newShape,keptDims}=util_exports.squeezeShape(shape);if(newShape.length<shape.length){const newInputInfo=squeezeInputInfo(inputInfo,newShape),params=["row","col","depth","depth2","depth3","depth4"];return`
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${funcName}(${getSqueezedParams(params,keptDims)});
      }
    `}const stride4=shape[5],stride3=shape[4]*stride4,stride2=shape[3]*stride3,stride1=shape[2]*stride2,stride0=shape[1]*stride1;if(inputInfo.shapeInfo.isUniform)return`
      float ${funcName}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
          dot(
            vec2(depth3, depth4),
            vec2(${stride4}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;const flatOffset=inputInfo.shapeInfo.flatOffset,texShape=inputInfo.shapeInfo.texShape,texNumR=texShape[0],texNumC=texShape[1];if(texNumC===stride0&&flatOffset==null)return`
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;if(texNumC===stride4&&flatOffset==null)return`
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${shape[1]*shape[2]*shape[3]*shape[4]},
               ${shape[2]*shape[3]*shape[4]},
               ${shape[3]*shape[4]},
               ${shape[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;const offset=getFlatOffsetUniformName(texName);return`
    float ${funcName}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
      return sampleTexture(${texName}, uv);
    }
  `}function getUniformSampler(inputInfo){const texName=inputInfo.name,inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);return inSize<2?`return ${texName};`:`
    for (int i = 0; i < ${inSize}; i++) {
      if (i == index) {
        return ${texName}[i];
      }
    }
  `}function getPackedSamplerAtOutputCoords(inputInfo,outShapeInfo){const texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length,broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),type=getCoordsDataType(outRank),rankDiff=outRank-inRank;let coordsSnippet;const fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
`);let unpackedCoordsSnippet="";outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", ");let output="return outputValue;";const inSize=util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape),isInputScalar=inSize===1,outSize=util_exports.sizeFromShape(outShapeInfo.logicalShape),isOutputScalar=outSize===1;if(inRank===1&&!isInputScalar&&!isOutputScalar)output=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(isInputScalar&&!isOutputScalar)outRank===1?output=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:output=`
        return vec4(outputValue.x);
      `;else if(broadcastDims.length){const rows=inRank-2,cols=inRank-1;broadcastDims.indexOf(rows)>-1&&broadcastDims.indexOf(cols)>-1?output="return vec4(outputValue.x);":broadcastDims.indexOf(rows)>-1?output="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":broadcastDims.indexOf(cols)>-1&&(output="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${funcName}() {
      ${type} coords = getOutputCoords();
      ${coordsSnippet}
      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
      ${output}
    }
  `}function getSamplerAtOutputCoords(inputInfo,outShapeInfo){const texName=inputInfo.name,texFuncSnippet=texName.charAt(0).toUpperCase()+texName.slice(1),funcName="get"+texFuncSnippet+"AtOutCoords",outTexShape=outShapeInfo.texShape,inTexShape=inputInfo.shapeInfo.texShape,inRank=inputInfo.shapeInfo.logicalShape.length,outRank=outShapeInfo.logicalShape.length;if(!inputInfo.shapeInfo.isUniform&&inRank===outRank&&inputInfo.shapeInfo.flatOffset==null&&util_exports.arraysEqual(inTexShape,outTexShape))return`
      float ${funcName}() {
        return sampleTexture(${texName}, resultUV);
      }
    `;const type=getCoordsDataType(outRank),broadcastDims=getBroadcastDims2(inputInfo.shapeInfo.logicalShape,outShapeInfo.logicalShape),rankDiff=outRank-inRank;let coordsSnippet;const fields=["x","y","z","w","u","v"];inRank===0?coordsSnippet="":outRank<2&&broadcastDims.length>=1?coordsSnippet="coords = 0;":coordsSnippet=broadcastDims.map(d=>`coords.${fields[d+rankDiff]} = 0;`).join(`
`);let unpackedCoordsSnippet="";return outRank<2&&inRank>0?unpackedCoordsSnippet="coords":unpackedCoordsSnippet=inputInfo.shapeInfo.logicalShape.map((s,i)=>`coords.${fields[i+rankDiff]}`).join(", "),`
    float ${funcName}() {
      ${type} coords = getOutputCoords();
      ${coordsSnippet}
      return get${texFuncSnippet}(${unpackedCoordsSnippet});
    }
  `}function getCoordsDataType(rank){if(rank<=1)return"int";if(rank===2)return"ivec2";if(rank===3)return"ivec3";if(rank===4)return"ivec4";if(rank===5)return"ivec5";if(rank===6)return"ivec6";throw Error(`GPU for rank ${rank} is not yet supported`)}function squeezeInputInfo(inInfo,squeezedShape){const newInputInfo=JSON.parse(JSON.stringify(inInfo));return newInputInfo.shapeInfo.logicalShape=squeezedShape,newInputInfo}function getSqueezedParams(params,keptDims){return keptDims.map(d=>params[d]).join(", ")}class ArgMinMaxPackedProgram{constructor(shape,windowSize,op2,firstPass){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,util_exports.assert(shape.length>2,()=>`Packed arg${op2.charAt(0).toUpperCase()+op2.slice(1)} supports only inputs with rank above 2.`);const inSize=shape[shape.length-1],outSize=Math.ceil(inSize/windowSize);this.outputShape=shape.slice(0,-1),outSize>1&&this.outputShape.push(outSize),firstPass||this.variableNames.push("bestIndicesA");const outShape=this.outputShape,rank=outShape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank);let sourceLocSetup,sourceRank;if(outSize===1){sourceRank=rank+1;const sourceLocDType=getCoordsDataType(sourceRank);sourceLocSetup=`
        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);
        ++${coords2[rank-1]};
        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);
        ++${coords2[rank-2]};
        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);
        --${coords2[rank-1]};
        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);
        --${coords2[rank-2]};`}else sourceRank=rank,sourceLocSetup=`
        ${dtype} sourceLocR = coords;
        ++${coords2[rank-1]};
        ${dtype} sourceLocG = coords;
        ++${coords2[rank-2]};
        ${dtype} sourceLocA = coords;
        --${coords2[rank-1]};
        ${dtype} sourceLocB = coords;
        --${coords2[rank-2]};`;const channels=["x","y","z","w","u","v"].slice(0,sourceRank),inChannel="."+channels[sourceRank-1],intChannels=channels.map(x=>"int "+x),srcRCoords=getChannels("sourceLocR",sourceRank-1).concat("inIdx.r"),srcGCoords=getChannels("sourceLocG",sourceRank-1).concat("inIdx.g"),srcBCoords=getChannels("sourceLocB",sourceRank-1).concat("inIdx.b"),srcACoords=getChannels("sourceLocA",sourceRank-1).concat("inIdx.a"),compOp=op2==="max"?"greaterThan":"lessThan",fetchCandidateIdx=firstPass?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
                             getBestIndicesAChannel(${srcGCoords.join()}),
                             getBestIndicesAChannel(${srcBCoords.join()}),
                             getBestIndicesAChannel(${srcACoords.join()})));`,fetchValue=`vec4(
            getAChannel(${srcRCoords.join()}),
            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`,getBestIndicesAChannelSnippet=firstPass?"":`
      float getBestIndicesAChannel(${intChannels.join()}) {
        return getChannel(getBestIndicesA(${channels.join()}),
                                          vec2(${channels.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${intChannels.join()}) {
        return getChannel(getA(${channels.join()}),
                               vec2(${channels.slice(-2).join()}));
      }
      ${getBestIndicesAChannelSnippet}
      void main() {
        ${dtype} coords = getOutputCoords();
        bool hasNextCol = ${coords2[rank-1]} < ${outShape[rank-1]-1};
        bool hasNextRow = ${coords2[rank-2]} < ${outShape[rank-2]-1};
        ${sourceLocSetup}
        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${fetchValue};

        for (int i = 0; i < ${windowSize}; i++) {
          inIdx = srcIdx;
          ${fetchCandidateIdx}
          vec4 candidate = ${fetchValue};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}}class AvgPool2DBackpropProgram{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;const filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterHeight*filterWidth);this.userCode=`
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float avgMultiplier = float(${avgMultiplier});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${effectiveFilterWidth};
            wC+= ${dilationWidth}) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}}class AvgPool3DBackpropProgram{constructor(convInfo){this.variableNames=["dy"],this.outputShape=convInfo.inShape;const filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,avgMultiplier=1/(filterDepth*filterHeight*filterWidth);this.userCode=`
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
      const float avgMultiplier = float(${avgMultiplier});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
            wD += ${dilationDepth}) {
          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;

          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}const CHECK_NAN_SNIPPET=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,INT_DIV=`
  float s = sign(a) * sign(b);
  int ia = round(a);
  int ib = round(b);
  if (ib != 0) {
    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
    return float(idiv(ia, ib, s));
  } else {
    return NAN;
  }
`,POW=`
if(a < 0.0 && floor(b) < b){
  return NAN;
}
if (b == 0.0) {
  return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
    pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,EQUAL="return float(a == b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+`
  return max(a, b);
`,MIN=CHECK_NAN_SNIPPET+`
  return min(a, b);
`,MOD=`if (b == 0.0) return NAN;
  return mod(a, b);`,ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",PRELU="return (a < 0.) ? b * a : a;";class BinaryOpProgram{constructor(op2,aShape,bShape){this.variableNames=["A","B"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
      float binaryOperation(float a, float b) {
        ${op2}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}}const CHECK_NAN_SNIPPET2=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`,INT_DIV2=`
  ivec4 ia = round(a);
  ivec4 ib = round(b);
  bvec4 cond = notEqual(ib, ivec4(0));
  ivec4 result = ivec4(0);
  vec4 s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    result[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    result[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    result[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    result[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4(result);
`,POW2=`
  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
  vec4 result = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  bvec4 isExpZero = equal(b, vec4(0.0));
  result.r = isExpZero.r ? 1.0 : result.r;
  result.g = isExpZero.g ? 1.0 : result.g;
  result.b = isExpZero.b ? 1.0 : result.b;
  result.a = isExpZero.a ? 1.0 : result.a;

  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
  `+CHECK_NAN_SNIPPET2+`
  return result;
`,PRELU2=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,ELU_DER2=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,EQUAL2=`
  return vec4(equal(a, b));
`,LESS2=`
  return vec4(lessThan(a, b));
`,LESS_EQUAL2=`
  return vec4(lessThanEqual(a, b));
`,GREATER2=`
  return vec4(greaterThan(a, b));
`,GREATER_EQUAL2=`
  return vec4(greaterThanEqual(a, b));
`,LOGICAL_AND2=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,LOGICAL_OR2=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,MAX2=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+CHECK_NAN_SNIPPET2+`
  return result;
`,MIN2=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+CHECK_NAN_SNIPPET2+`
  return result;
`,MOD2=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+CHECK_NAN_SNIPPET2+`
  return result;
`;class BinaryOpPackedProgram{constructor(op2,aShape,bShape,checkOutOfBounds=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape);const rank=this.outputShape.length;let checkOutOfBoundsString="";if(checkOutOfBounds)if(rank===0||util_exports.sizeFromShape(this.outputShape)===1)checkOutOfBoundsString=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else{const dtype=getCoordsDataType(rank);if(checkOutOfBoundsString=`
          ${dtype} coords = getOutputCoords();
        `,rank===1)checkOutOfBoundsString+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{const channels=getChannels("coords",rank);checkOutOfBoundsString+=`
            bool nextRowOutOfBounds =
              (${channels[rank-2]} + 1) >= ${this.outputShape[rank-2]};
            bool nextColOutOfBounds =
              (${channels[rank-1]} + 1) >= ${this.outputShape[rank-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${op2}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${checkOutOfBoundsString}

        setOutput(result);
      }
    `}}class ClipProgram{constructor(aShape){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}getCustomSetupFunc(min8,max10){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min8),gpgpu.gl.uniform1f(this.maxLoc,max10)}}}class ClipPackedProgram{constructor(aShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}getCustomSetupFunc(min8,max10){return(gpgpu,webGLProgram)=>{this.minLoc==null&&(this.minLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"minVal"),this.maxLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"maxVal")),gpgpu.gl.uniform1f(this.minLoc,min8),gpgpu.gl.uniform1f(this.maxLoc,max10)}}}class ComplexAbsProgram{constructor(shape){this.variableNames=["real","imag"],this.outputShape=shape,this.userCode=`
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `}}class Conv2DDerFilterProgram{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;const strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
            int xR = wR + yR * ${strideHeight} - ${padTop};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
              int xC = wC + yC * ${strideWidth} - ${padLeft};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              if (${isChannelsLast}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}}class Conv2DDerInputProgram{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;const filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,isChannelsLast=convInfo.dataFormat==="channelsLast",padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1;this.userCode=`
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${channelDim}];

        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${filterWidth} - 1 - wC;

            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {

              if (${isChannelsLast}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `}}class Conv3DDerFilterProgram{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;const strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
            int xF = wF + yF * ${strideDepth} - ${padFront};

            if (xF < 0 || xF >= ${convInfo.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
              int xR = wR + yR * ${strideHeight} - ${padTop};

              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
                int xC = wC + yC * ${strideWidth} - ${padLeft};

                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class Conv3DDerInputProgram{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;const filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padFront=filterDepth-1-convInfo.padInfo.front,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${filterDepth}; wF++) {
          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;

          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${filterDepth} - 1 - wF;

          for (int wR = 0; wR < ${filterHeight}; wR++) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${filterHeight} - 1 - wR;

            for (int wC = 0; wC < ${filterWidth}; wC++) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${filterWidth} - 1 - wC;

              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class DepthwiseConv2DDerFilterProgram{constructor(convInfo){this.variableNames=["x","dy"],this.outputShape=convInfo.filterShape;const strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${channelMul} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
            int xR = wR + yR * ${strideHeight} - ${padTop};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
              int xC = wC + yC * ${strideWidth} - ${padLeft};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class DepthwiseConv2DDerInputProgram{constructor(convInfo){this.variableNames=["dy","W"],this.outputShape=convInfo.inShape;const filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,padTop=filterHeight-1-convInfo.padInfo.top,padLeft=filterWidth-1-convInfo.padInfo.left,channelMul=convInfo.outChannels/convInfo.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${filterWidth} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${channelMul}; dm++) {
              int d2 = d1 * ${channelMul} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class Conv2DProgram{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivationWeights=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;const padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4,isChannelsLast=convInfo.dataFormat==="channelsLast",rowDim=isChannelsLast?1:2,colDim=isChannelsLast?2:3,channelDim=isChannelsLast?3:1;let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivationWeights?activationSnippet=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`:activationSnippet=`
          float activation(float x) {
            ${activation2}
          }
        `,applyActivationSnippet="result = activation(result);");const addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${channelDim}];

        ivec2 xRCCorner =
            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * ${dilationHeight};

          if (xR < 0 || xR >= ${convInfo.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * ${dilationWidth};

            if (xC < 0 || xC >= ${convInfo.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${isChannelsLast}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${inputDepthVec4Remainder===1}) {

              if (${isChannelsLast}) {
                dotProd +=
                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
              } else {
                dotProd +=
                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
              }

            } else if (${inputDepthVec4Remainder===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${inputDepthNearestVec4}, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
              );

              if (${isChannelsLast}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${inputDepthVec4Remainder===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${inputDepthNearestVec4}, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
              );

              if (${isChannelsLast}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `}}class Conv3DProgram{constructor(convInfo){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;const padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterDepth=convInfo.filterDepth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,inputDepthNearestVec4=Math.floor(convInfo.inChannels/4)*4,inputDepthVec4Remainder=convInfo.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${filterDepth}; wF++) {
          int xF = xFCorner + wF * ${dilationDepth};

          if (xF < 0 || xF >= ${convInfo.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${filterHeight}; wR++) {
            int xR = xRCorner + wR * ${dilationHeight};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${filterWidth}; wC++) {
              int xC = xCCorner + wC * ${dilationWidth};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${inputDepthVec4Remainder===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
              } else if (${inputDepthVec4Remainder===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${inputDepthVec4Remainder===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class DepthwiseConv2DProgram{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.outputShape=convInfo.outShape;const xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,channelMul=convInfo.outChannels/convInfo.inChannels;let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`:activationSnippet=`
          float activation(float x) {
            ${activation2}
          }
        `,applyActivationSnippet="result = activation(result);");const addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${channelMul};
        int q = d2 - d1 * ${channelMul};

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * ${dilationHeight};

          if (xR < 0 || xR >= ${xNumRows}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * ${dilationWidth};

            if (xC < 0 || xC >= ${xNumCols}) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `}}class DepthwiseConvPacked2DProgram{constructor(convInfo,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=convInfo.outShape;const xNumRows=convInfo.inHeight,xNumCols=convInfo.inWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,texelsAcross=filterWidth;let mainLoop="int xR; int xC; int xCOffset;";for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`
          vec4 xTexelR${r}C${c*2} = vec4(0.);
          vec4 wR${r}C${c} = vec4(0.);
          vec4 xR${r}C${c} = vec4(0.);`;for(let r=0;r<filterHeight;r++)for(let texelC=0;texelC<texelsAcross;texelC++){const c=texelC*2;if(mainLoop+=`
          xR = xRCorner + ${r*dilationHeight};
          xC = xCCorner + ${c*dilationWidth};
        `,strideWidth===1){if(c<filterWidth&&(padLeft%2===1?mainLoop+=`
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${xNumCols}) {
                    xTexelR${r}C${c}.zw = vec2(0.);
                  }
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                xCOffset = xC + 1 - 2;
                if(xR >= 0 && xR < ${xNumRows} && xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  vec4 previous = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${xNumCols}) {
                    previous.zw = vec2(0.);
                  }

                  xR${r}C${c} = vec4(previous.zw, xTexelR${r}C${c}.xy);
                } else {
                  xR${r}C${c} = vec4(0, 0, xTexelR${r}C${c}.xy);
                }
              `:mainLoop+=`
                if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                xR${r}C${c} = xTexelR${r}C${c};
              `,c+1<filterWidth)){const nextTexelOffset=padLeft%2===0?util_exports.nearestLargerEven(dilationWidth):dilationWidth;dilationWidth%2===0&&padLeft%2===1||dilationWidth%2!==0&&padLeft%2!==1?(mainLoop+=`
                  xCOffset = xC + ${padLeft%2} + ${nextTexelOffset};

                  if(xR >= 0 && xR < ${xNumRows} &&
                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                  }
                `,dilationWidth>1&&(mainLoop+=`
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${xNumRows} &&
                      xCOffset >= 0 && xCOffset < ${xNumCols}) {
                      xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${r}C${c} = vec4(0.);
                    }
                  `),mainLoop+=`
                  xR${r}C${c+1} = vec4(
                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.xy);
                `):mainLoop+=`
                  xCOffset = xC + ${nextTexelOffset};

                  if(xR >= 0 && xR < ${xNumRows} &&
                    xCOffset >= 0 && xCOffset < ${xNumCols}) {
                    xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${r}C${c+1} = xTexelR${r}C${c+2};
                `}}else c<filterWidth&&(mainLoop+=`
              if(xR >= 0 && xR < ${xNumRows}) {
            `,padLeft%2===1?(mainLoop+=`
                xCOffset = xC + 1 - ${strideWidth};
                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                if(xC + 1 >= 0 && xC + 1 < ${xNumCols}) {
                  xTexelR${r}C${c+2} = getX(batch, xR, xC + 1, d1);
                } else {
                  xTexelR${r}C${c+2} = vec4(0.);
                }

                xR${r}C${c} = vec4(
                  xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
              `,c+1<filterWidth&&(mainLoop+=`
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${strideWidth};
                  if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${r}C${c+1} = vec4(xTexelR${r}C${c+2}.xy, final.xy);
                `)):(mainLoop+=`
                if(xC >= 0 && xC < ${xNumCols}) {
                  xTexelR${r}C${c} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${r}C${c} = vec4(0.);
                }

                xCOffset = xC + ${strideWidth};
                if(xCOffset >= 0 && xCOffset < ${xNumCols}) {
                  xTexelR${r}C${c+2} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${r}C${c+2} = vec4(0.);
                }

                xR${r}C${c} = vec4(
                  xTexelR${r}C${c}.xy, xTexelR${r}C${c+2}.xy);
              `,c+1<filterWidth&&(mainLoop+=`
                  xR${r}C${c+1} = vec4(
                    xTexelR${r}C${c}.zw, xTexelR${r}C${c+2}.zw);
                `)),mainLoop+="}");c<filterWidth&&(mainLoop+=`
            vec4 wTexelR${r}C${c} = getW(${r}, ${c}, d1, q);
            wR${r}C${c} = vec4(wTexelR${r}C${c}.xz, wTexelR${r}C${c}.xz);
          `,c+1<filterWidth&&(mainLoop+=`
              vec4 wTexelR${r}C${c+1} = getW(${r}, ${c+1}, d1, q);
              wR${r}C${c+1} =
                vec4(wTexelR${r}C${c+1}.xz, wTexelR${r}C${c+1}.xz);`))}for(let r=0;r<filterHeight;r++)for(let c=0;c<filterWidth;c++)mainLoop+=`dotProd += xR${r}C${c} * wR${r}C${c};`;let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`:activationSnippet=`vec4 activation(vec4 x) {
          ${activation2}
        }`,applyActivationSnippet="result = activation(result);");const addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {

        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2;
        int q = 0;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        vec4 dotProd = vec4(0.);

        ${mainLoop}

        vec4 result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `}}class CropAndResizeProgram{constructor(imageShape,boxShape,cropSize,method,extrapolationValue){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[batch,imageHeight,imageWidth,depth]=imageShape,[numBoxes]=boxShape,[cropHeight,cropWidth]=cropSize;this.outputShape=[numBoxes,cropHeight,cropWidth,depth];const methodId=method==="bilinear"?1:0,[inputHeightFloat,inputWidthFloat]=[`${imageHeight-1}.0`,`${imageWidth-1}.0`],[heightRatio,heightScale,inY]=cropHeight>1?[`${(imageHeight-1)/(cropHeight-1)}`,"(y2-y1) * height_ratio",`y1*${inputHeightFloat} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${inputHeightFloat}`],[widthRatio,widthScale,inX]=cropWidth>1?[`${(imageWidth-1)/(cropWidth-1)}`,"(x2-x1) * width_ratio",`x1*${inputWidthFloat} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${inputWidthFloat}`];this.userCode=`
      const float height_ratio = float(${heightRatio});
      const float width_ratio = float(${widthRatio});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${batch}) {
          return;
        }

        float height_scale = ${heightScale};
        float width_scale = ${widthScale};

        float in_y = ${inY};
        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
          setOutput(float(${extrapolationValue}));
          return;
        }
        float in_x = ${inX};
        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
          setOutput(float(${extrapolationValue}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${methodId} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}}class CumSumProgram{constructor(shape,exclusive,reverse12){this.variableNames=["x"],this.outputShape=shape;const rank=shape.length,val=exclusive?"0.0":`getX(${getCoords(rank,"coords")})`,length=shape[shape.length-1];let condition="",idxString="";exclusive?(condition=reverse12?`end != ${length-1}`:"end != 0",idxString=reverse12?"end + 1":"end - 1"):(condition=reverse12?`end + pow2 < ${length}`:"end >= pow2",idxString=reverse12?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${getCoordsDataType(rank)} coords = getOutputCoords();
        int end = ${getFinalCoord(rank,"coords")};
        float val = ${val};
        int pow2 = int(pow(2.0, index));
        if (${condition}) {
          int idx = ${idxString};
          ${getFinalCoord(rank,"coords")} = idx;
          val += getX(${getCoords(rank,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(index){return(gpgpu,webGLProgram)=>{this.index==null&&(this.index=gpgpu.getUniformLocation(webGLProgram,"index")),gpgpu.gl.uniform1f(this.index,index)}}}function getCoords(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.x, ${name}.y`;if(rank===3)return`${name}.x, ${name}.y, ${name}.z`;if(rank===4)return`${name}.x, ${name}.y, ${name}.z, ${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}function getFinalCoord(rank,name){if(rank===1)return`${name}`;if(rank===2)return`${name}.y`;if(rank===3)return`${name}.z`;if(rank===4)return`${name}.w`;throw Error(`Cumulative sum for rank ${rank} is not yet supported`)}class DecodeMatrixProgram{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;const texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${texShape[0]}, ${texShape[1]}));
        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${glsl.output} = result;
      }
    `}}class DecodeMatrixPackedProgram{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=PackingScheme.DENSE;const texShape=getDenseTexShape(outputShape),glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${getLogicalCoordinatesFromFlatIndex(["r","c","d"],outputShape)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx *
          vec2(${texShape[0]}, ${texShape[1]}));
        int index = 4 * (resTexRC.x * ${texShape[1]} + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${glsl.output} = result;
      }
    `}}class DepthToSpaceProgram{constructor(outputShape,blockSize,dataFormat){this.variableNames=["x"],this.outputShape=[],this.outputShape=outputShape,this.blockSize=blockSize,this.dataFormat=dataFormat,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${blockSize};
      int offset_h = imod(h, ${blockSize});
      int in_w = w / ${blockSize};
      int offset_w = imod(w, ${blockSize});
      int offset_d = (offset_h * ${blockSize} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}class DiagProgram{constructor(size){this.variableNames=["X"],this.outputShape=[size,size],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}}class EncodeFloatProgram{constructor(outputShape){this.variableNames=["A"],this.outTexUsage=TextureUsage.DOWNLOAD;const glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
      ${ENCODE_FLOAT_SNIPPET}

      void main() {
        float x = getAAtOutCoords();
        ${glsl.output} = encode_float(x);
      }
    `}}class EncodeFloatPackedProgram{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=TextureUsage.DOWNLOAD;const glsl=getGlslDifferences();this.outputShape=outputShape,this.userCode=`
      ${ENCODE_FLOAT_SNIPPET}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${glsl.output} = encode_float(x);
      }
    `}}class EncodeMatrixProgram{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"];const glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)"),this.userCode=`
      ${getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 coords = getOutputCoords();

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / ${width};
        int c = imod(flatIndex, ${width});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
        vec4 values = ${glsl.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${glsl.output} = vec4(${output}, 0., 0., 0.);
      }
    `}}class EncodeMatrixPackedProgram{constructor(outputShape,texShape,inputIsUnsignedByte=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const glsl=getGlslDifferences(),[height,width]=texShape;this.outputShape=outputShape;let mainLoop="",output="result";inputIsUnsignedByte&&(output="floor(result * 255. + 0.5)");for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){const channel=row*2+col;mainLoop+=`
          localCoords = coords;
          if(localCoords[2] + ${col} < ${outputShape[2]}) {
            localCoords[2] += ${col};
            if(localCoords[1] + ${row} < ${outputShape[1]}) {
              localCoords[1] += ${row};

              flatIndex = getFlatIndex(localCoords);
              offset = imod(flatIndex, 4);

              flatIndex = idiv(flatIndex, 4, 1.);

              r = flatIndex / ${width};
              c = imod(flatIndex, ${width});
              uv = (vec2(c, r) + halfCR) / vec2(${width}.0, ${height}.0);
              values = ${glsl.texture2D}(A, uv);

              if(offset == 0) {
                result[${channel}] = values[0];
              } else if(offset == 1) {
                result[${channel}] = values[1];
              } else if(offset == 2) {
                result[${channel}] = values[2];
              } else {
                result[${channel}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 coords = getOutputCoords();

        vec4 result = vec4(0.);
        int flatIndex, r, c, offset;
        ivec3 localCoords;
        vec2 uv;
        vec4 values;

        ${mainLoop}

        ${glsl.output} = ${output};
      }
    `}}class FillProgram{constructor(shape,value){this.outputShape=[],this.variableNames=["x"],this.outputShape=shape,this.userCode=`
      uniform float value;
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}getCustomSetupFunc(value){return(gpgpu,webGLProgram)=>{this.valueLoc==null&&(this.valueLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"value")),gpgpu.gl.uniform1f(this.valueLoc,value)}}}class GatherProgram{constructor(aShape,indicesLength,axis){this.variableNames=["A","indices"];const outputShape=aShape.slice();outputShape[axis]=indicesLength,this.outputShape=outputShape,this.rank=outputShape.length;const dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords2(aShape,axis);this.userCode=`
      void main() {
        ${dtype} resRC = getOutputCoords();
        setOutput(getA(${sourceCoords}));
      }
    `}}function getSourceCoords2(aShape,axis){const rank=aShape.length;if(rank>4)throw Error(`Gather for rank ${rank} is not yet supported`);if(rank===1)return"int(getIndices(resRC))";const currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],sourceCoords=[];for(let i=0;i<aShape.length;i++)i===axis?sourceCoords.push(`int(getIndices(${currentCoords[i]}))`):sourceCoords.push(`${currentCoords[i]}`);return sourceCoords.join()}class GatherNDProgram{constructor(sliceDim,strides,shape){this.sliceDim=sliceDim,this.strides=strides,this.variableNames=["x","indices"],this.outputShape=shape;const stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length),strideString=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${stridesType} strides = ${stridesType}(${this.strides});
         void main() {
          ${dtype} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${strideString};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}}function createVertexShader2(gl){const glsl=getGlslDifferences(),vertexShaderSource=`${glsl.version}
    precision highp float;
    ${glsl.attribute} vec3 clipSpacePos;
    ${glsl.attribute} vec2 uv;
    ${glsl.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return createVertexShader(gl,vertexShaderSource)}function createVertexBuffer(gl){const vertexArray=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return createStaticVertexBuffer(gl,vertexArray)}function createIndexBuffer(gl){const triangleVertexIndices=new Uint16Array([0,1,2,2,1,3]);return createStaticIndexBuffer(gl,triangleVertexIndices)}function createAndConfigureTexture(gl,width,height,internalFormat,textureFormat,textureType){validateTextureSize(width,height);const texture=createTexture(gl),tex2d=gl.TEXTURE_2D;return callAndCheck(gl,()=>gl.bindTexture(tex2d,texture)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MIN_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texParameteri(tex2d,gl.TEXTURE_MAG_FILTER,gl.NEAREST)),callAndCheck(gl,()=>gl.texImage2D(tex2d,0,internalFormat,width,height,0,textureFormat,textureType,null)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null)),texture}function getInternalFormatForFloat32MatrixTexture(textureConfig){return textureConfig.internalFormatFloat}function createFloat32MatrixTexture(gl,rows,columns,textureConfig){const[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat32MatrixTexture(textureConfig),textureConfig.textureFormatFloat,gl.FLOAT)}function getInternalFormatForFloat16MatrixTexture(textureConfig){return textureConfig.internalFormatHalfFloat}function createFloat16MatrixTexture(gl,rows,columns,textureConfig){const[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16MatrixTexture(textureConfig),textureConfig.textureFormatFloat,textureConfig.textureTypeHalfFloat)}function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig){return textureConfig.downloadTextureFormat}function createUnsignedBytesMatrixTexture(gl,rows,columns,textureConfig){const[width,height]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForUnsignedBytesMatrixTexture(textureConfig),gl.RGBA,gl.UNSIGNED_BYTE)}function getInternalFormatForPackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedFloat}function createPackedMatrixTexture(gl,rows,columns,textureConfig){const[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForPackedMatrixTexture(textureConfig),gl.RGBA,gl.FLOAT)}function getInternalFormatForFloat16PackedMatrixTexture(textureConfig){return textureConfig.internalFormatPackedHalfFloat}function createFloat16PackedMatrixTexture(gl,rows,columns,textureConfig){const[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);return createAndConfigureTexture(gl,width,height,getInternalFormatForFloat16PackedMatrixTexture(textureConfig),gl.RGBA,textureConfig.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(gl,program,vertexBuffer){const posOffset=0,uvOffset=3*4,stride=3*4+2*4;callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,vertexBuffer));const success=bindVertexBufferToProgramAttribute(gl,program,"clipSpacePos",vertexBuffer,3,stride,posOffset);return success&&bindVertexBufferToProgramAttribute(gl,program,"uv",vertexBuffer,2,stride,uvOffset)}function uploadDenseMatrixToTexture(gl,texture,width,height,data2,textureConfig){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture));let dataForUpload,texelDataType,internalFormat;data2 instanceof Uint8Array?(dataForUpload=new Uint8Array(width*height*4),texelDataType=gl.UNSIGNED_BYTE,internalFormat=gl.RGBA):(dataForUpload=new Float32Array(width*height*4),texelDataType=gl.FLOAT,internalFormat=textureConfig.internalFormatPackedFloat),dataForUpload.set(data2),callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,internalFormat,width,height,0,gl.RGBA,texelDataType,dataForUpload)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function uploadPixelDataToTexture(gl,texture,pixels){callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,texture)),pixels.data instanceof Uint8Array?callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,pixels.width,pixels.height,0,gl.RGBA,gl.UNSIGNED_BYTE,pixels.data)):callAndCheck(gl,()=>gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,pixels)),callAndCheck(gl,()=>gl.bindTexture(gl.TEXTURE_2D,null))}function createBufferFromOutputTexture(gl2,rows,columns,textureConfig){const buffer11=gl2.createBuffer();callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11));const bytesPerFloat=4,valuesPerTexel=4,bufferSizeBytes=bytesPerFloat*valuesPerTexel*rows*columns;return callAndCheck(gl2,()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER,bufferSizeBytes,gl2.STREAM_READ)),callAndCheck(gl2,()=>gl2.readPixels(0,0,columns,rows,gl2.RGBA,gl2.FLOAT,0)),callAndCheck(gl2,()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null)),buffer11}function downloadFloat32MatrixFromBuffer(gl,buffer11,size){const gl2=gl,downloadTarget=new Float32Array(size);return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadByteEncodedFloatMatrixFromOutputTexture(gl,rows,columns,textureConfig){const[w,h]=getUnpackedMatrixTextureShapeWidthHeight(rows,columns),numChannels=4,downloadTarget=new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows*columns,numChannels));return callAndCheck(gl,()=>gl.readPixels(0,0,w,h,textureConfig.downloadTextureFormat,gl.UNSIGNED_BYTE,downloadTarget)),new Float32Array(downloadTarget.buffer)}function downloadPackedMatrixFromBuffer(gl,buffer11,batch,rows,cols,physicalRows,physicalCols,textureConfig){const gl2=gl,downloadTarget=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows,physicalCols));return gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,buffer11),gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER,0,downloadTarget),gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER,null),downloadTarget}function downloadMatrixFromPackedOutputTexture(gl,physicalRows,physicalCols){const packedRGBA=new Float32Array(physicalRows*physicalCols*4);return callAndCheck(gl,()=>gl.readPixels(0,0,physicalCols,physicalRows,gl.RGBA,gl.FLOAT,packedRGBA)),packedRGBA}class GPGPUContext{constructor(gl){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const glVersion=env().getNumber("WEBGL_VERSION");gl!=null?(this.gl=gl,setWebGLContext(glVersion,gl)):this.gl=getWebGLContext(glVersion);let COLOR_BUFFER_FLOAT="WEBGL_color_buffer_float";const COLOR_BUFFER_HALF_FLOAT="EXT_color_buffer_half_float";if(env().getNumber("WEBGL_VERSION")===1){const TEXTURE_FLOAT="OES_texture_float",TEXTURE_HALF_FLOAT="OES_texture_half_float";if(this.textureFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_FLOAT),hasExtension(this.gl,TEXTURE_HALF_FLOAT))this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,TEXTURE_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT),hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=getExtensionOrThrow(this.gl,COLOR_BUFFER_HALF_FLOAT);else if(env().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(COLOR_BUFFER_FLOAT="EXT_color_buffer_float",hasExtension(this.gl,COLOR_BUFFER_FLOAT))this.colorBufferFloatExtension=this.gl.getExtension(COLOR_BUFFER_FLOAT);else if(hasExtension(this.gl,COLOR_BUFFER_HALF_FLOAT))this.colorBufferHalfFloatExtension=this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}get debug(){return env().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const gl=this.gl;callAndCheck(gl,()=>gl.finish()),callAndCheck(gl,()=>gl.bindFramebuffer(gl.FRAMEBUFFER,null)),callAndCheck(gl,()=>gl.deleteFramebuffer(this.framebuffer)),callAndCheck(gl,()=>gl.bindBuffer(gl.ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER,null)),callAndCheck(gl,()=>gl.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,rows,columns,this.textureConfig)}createFloat16MatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,rows,columns,this.textureConfig)}createUnsignedBytesMatrixTexture(rows,columns){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,rows,columns,this.textureConfig)}uploadPixelDataToTexture(texture,pixels){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,texture,pixels)}uploadDenseMatrixToTexture(texture,width,height,data2){this.throwIfDisposed(),uploadDenseMatrixToTexture(this.gl,texture,width,height,data2,this.textureConfig)}createFloat16PackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}createPackedMatrixTexture(rows,columns){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,rows,columns,this.textureConfig)}deleteMatrixTexture(texture){this.throwIfDisposed(),this.outputTexture===texture&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,()=>this.gl.deleteTexture(texture))}downloadByteEncodedFloatMatrixFromOutputTexture(texture,rows,columns){return this.downloadMatrixDriver(texture,()=>downloadByteEncodedFloatMatrixFromOutputTexture(this.gl,rows,columns,this.textureConfig))}downloadPackedMatrixFromBuffer(buffer11,batch,rows,columns,physicalRows,physicalCols){return downloadPackedMatrixFromBuffer(this.gl,buffer11,batch,rows,columns,physicalRows,physicalCols,this.textureConfig)}downloadFloat32MatrixFromBuffer(buffer11,size){return downloadFloat32MatrixFromBuffer(this.gl,buffer11,size)}createBufferFromTexture(texture,rows,columns){this.bindTextureToFrameBuffer(texture);const result=createBufferFromOutputTexture(this.gl,rows,columns,this.textureConfig);return this.unbindTextureToFrameBuffer(),result}createAndWaitForFence(){const fenceContext=this.createFence(this.gl);return this.pollFence(fenceContext)}createFence(gl){let query,isFencePassed;if(env().getBool("WEBGL_FENCE_API_ENABLED")){const gl2=gl,sync=gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE,0);gl.flush(),isFencePassed=()=>{const status=gl2.clientWaitSync(sync,0,0);return status===gl2.ALREADY_SIGNALED||status===gl2.CONDITION_SATISFIED},query=sync}else env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(query=this.beginQuery(),this.endQuery(),isFencePassed=()=>this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):isFencePassed=()=>!0;return{query,isFencePassed}}downloadMatrixFromPackedTexture(texture,physicalRows,physicalCols){return this.downloadMatrixDriver(texture,()=>downloadMatrixFromPackedOutputTexture(this.gl,physicalRows,physicalCols))}createProgram(fragmentShaderSource){this.throwIfDisposed();const gl=this.gl,fragmentShader=createFragmentShader(gl,fragmentShaderSource),vertexShader=createVertexShader2(gl),program=createProgram(gl);return callAndCheck(gl,()=>gl.attachShader(program,vertexShader)),callAndCheck(gl,()=>gl.attachShader(program,fragmentShader)),linkProgram(gl,program),this.debug&&validateProgram(gl,program),this.vertexAttrsAreBound||(this.setProgram(program),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(gl,this.program,this.vertexBuffer)),program}deleteProgram(program){this.throwIfDisposed(),program===this.program&&(this.program=null),program!=null&&callAndCheck(this.gl,()=>this.gl.deleteProgram(program))}setProgram(program){this.throwIfDisposed(),this.program=program,this.program!=null&&this.debug&&validateProgram(this.gl,this.program),callAndCheck(this.gl,()=>this.gl.useProgram(program))}getUniformLocation(program,uniformName,shouldThrow=!0){return this.throwIfDisposed(),shouldThrow?getProgramUniformLocationOrThrow(this.gl,program,uniformName):getProgramUniformLocation(this.gl,program,uniformName)}getAttributeLocation(program,attribute){return this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.getAttribLocation(program,attribute))}getUniformLocationNoThrow(program,uniformName){return this.throwIfDisposed(),this.gl.getUniformLocation(program,uniformName)}setInputMatrixTexture(inputMatrixTexture,uniformLocation,textureUnit){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,inputMatrixTexture,uniformLocation,textureUnit)}setOutputMatrixTexture(outputMatrixTexture,rows,columns){this.setOutputMatrixTextureDriver(outputMatrixTexture,columns,rows)}setOutputPackedMatrixTexture(outputPackedMatrixTexture,rows,columns){this.throwIfDisposed();const[width,height]=getPackedMatrixTextureShapeWidthHeight(rows,columns);this.setOutputMatrixTextureDriver(outputPackedMatrixTexture,width,height)}setOutputMatrixWriteRegion(startRow,numRows,startColumn,numColumns){this.setOutputMatrixWriteRegionDriver(startColumn,startRow,numColumns,numRows)}setOutputPackedMatrixWriteRegion(startRow,numRows,startColumn,numColumns){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const gl=this.gl;this.debug&&this.debugValidate(),callAndCheck(gl,()=>gl.drawElements(gl.TRIANGLES,6,gl.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2(),query2=gl2.createQuery();return gl2.beginQuery(ext2.TIME_ELAPSED_EXT,query2),query2}const ext=this.getQueryTimerExtensionWebGL1(),query=ext.createQueryEXT();return ext.beginQueryEXT(ext.TIME_ELAPSED_EXT,query),query}endQuery(){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){const gl2=this.gl,ext2=this.getQueryTimerExtensionWebGL2();gl2.endQuery(ext2.TIME_ELAPSED_EXT);return}const ext=this.getQueryTimerExtensionWebGL1();ext.endQueryEXT(ext.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(query){return await util_exports.repeatedTry(()=>this.disposed||this.isQueryAvailable(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(query,env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(query,queryTimerVersion){if(queryTimerVersion===0)return null;if(queryTimerVersion===2){const gl2=this.gl,timeElapsedNanos=gl2.getQueryParameter(query,gl2.QUERY_RESULT);return timeElapsedNanos/1e6}else{const ext=this.getQueryTimerExtensionWebGL1(),timeElapsedNanos=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_EXT);return timeElapsedNanos/1e6}}isQueryAvailable(query,queryTimerVersion){if(queryTimerVersion===0)return!0;if(queryTimerVersion===2){const gl2=this.gl,ext=this.getQueryTimerExtensionWebGL2(),available=gl2.getQueryParameter(query,gl2.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}else{const ext=this.getQueryTimerExtensionWebGL1(),available=ext.getQueryObjectEXT(query,ext.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(ext.GPU_DISJOINT_EXT)),available&&!this.disjoint}}pollFence(fenceContext){return new Promise(resolve=>{this.addItemToPoll(()=>fenceContext.isFencePassed(),()=>resolve())})}pollItems(){const index=linearSearchLastTrue(this.itemsToPoll.map(x=>x.isDoneFn));for(let i=0;i<=index;++i){const{resolveFn}=this.itemsToPoll[i];resolveFn()}this.itemsToPoll=this.itemsToPoll.slice(index+1)}addItemToPoll(isDoneFn,resolveFn){if(this.itemsToPoll.push({isDoneFn,resolveFn}),this.itemsToPoll.length>1)return;util_exports.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(texture){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,texture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.debug&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)}downloadMatrixDriver(texture,downloadAndDecode){this.bindTextureToFrameBuffer(texture);const result=downloadAndDecode();return this.unbindTextureToFrameBuffer(),result}setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked,width,height){this.throwIfDisposed();const gl=this.gl;bindColorTextureToFramebuffer(gl,outputMatrixTextureMaybePacked,this.framebuffer),this.debug&&validateFramebuffer(gl),this.outputTexture=outputMatrixTextureMaybePacked,callAndCheck(gl,()=>gl.viewport(0,0,width,height)),callAndCheck(gl,()=>gl.scissor(0,0,width,height))}setOutputMatrixWriteRegionDriver(x,y,width,height){this.throwIfDisposed(),callAndCheck(this.gl,()=>this.gl.scissor(x,y,width,height))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}}function linearSearchLastTrue(arr){let i=0;for(;i<arr.length;++i){const isDone=arr[i]();if(!isDone)break}return i-1}function compileProgram(gpgpu,program,inputs,output){const userCode=program.userCode,inputInfos=inputs.map((input2,i)=>{const shapeInfo={logicalShape:input2.shape,texShape:input2.isUniform?null:input2.texData.texShape,isUniform:input2.isUniform,isPacked:input2.isUniform?!1:input2.texData.isPacked,flatOffset:null};return input2.texData!=null&&input2.texData.slice!=null&&input2.texData.slice.flatOffset>0&&(shapeInfo.flatOffset=input2.texData.slice.flatOffset),{name:program.variableNames[i],shapeInfo}}),inShapeInfos=inputInfos.map(x=>x.shapeInfo),outShapeInfo={logicalShape:output.shape,texShape:output.texData.texShape,isUniform:!1,isPacked:output.texData.isPacked,flatOffset:null},source=makeShader(inputInfos,outShapeInfo,userCode,program.packedInputs),webGLProgram=gpgpu.createProgram(source);let infLoc=null;const nanLoc=gpgpu.getUniformLocation(webGLProgram,"NAN",!1);env().getNumber("WEBGL_VERSION")===1&&(infLoc=gpgpu.getUniformLocation(webGLProgram,"INFINITY",!1));const uniformLocations={};for(let i=0;i<program.variableNames.length;i++){const varName=program.variableNames[i],shouldThrow=!1;uniformLocations[varName]=gpgpu.getUniformLocation(webGLProgram,varName,shouldThrow),uniformLocations[`offset${varName}`]=gpgpu.getUniformLocation(webGLProgram,`offset${varName}`,shouldThrow)}return{program,source,webGLProgram,uniformLocations,inShapeInfos,outShapeInfo,infLoc,nanLoc}}function validateBinaryAndProgram(shapeInfos,inputs){if(shapeInfos.length!==inputs.length)throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);shapeInfos.forEach((s,i)=>{const shapeA=s.logicalShape,input2=inputs[i],shapeB=input2.shape;if(!util_exports.arraysEqual(shapeA,shapeB))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);if(s.isUniform&&input2.isUniform)return;const texShapeA=s.texShape,texShapeB=input2.isUniform?null:input2.texData.texShape;if(!util_exports.arraysEqual(texShapeA,texShapeB))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`)})}function runProgram(gpgpu,binary,inputs,output,customSetup){validateBinaryAndProgram(binary.inShapeInfos,inputs),validateBinaryAndProgram([binary.outShapeInfo],[output]);const outTex=output.texData.texture,outTexShape=output.texData.texShape;output.texData.isPacked?gpgpu.setOutputPackedMatrixTexture(outTex,outTexShape[0],outTexShape[1]):gpgpu.setOutputMatrixTexture(outTex,outTexShape[0],outTexShape[1]),gpgpu.setProgram(binary.webGLProgram),env().getNumber("WEBGL_VERSION")===1&&binary.infLoc!==null&&gpgpu.gl.uniform1f(binary.infLoc,Infinity),binary.nanLoc!==null&&gpgpu.gl.uniform1f(binary.nanLoc,NaN),inputs.forEach((input2,i)=>{const varName=binary.program.variableNames[i],varLoc=binary.uniformLocations[varName],varOffsetLoc=binary.uniformLocations[`offset${varName}`];if(varLoc==null)return;if(input2.isUniform){if(util_exports.sizeFromShape(input2.shape)<2)gpgpu.gl.uniform1f(varLoc,input2.uniformValues[0]);else{let vals=input2.uniformValues;vals instanceof Float32Array||(vals=new Float32Array(vals)),gpgpu.gl.uniform1fv(varLoc,vals)}return}input2.texData.slice!=null&&varOffsetLoc!=null&&gpgpu.gl.uniform1i(varOffsetLoc,input2.texData.slice.flatOffset),gpgpu.setInputMatrixTexture(input2.texData.texture,varLoc,i)}),customSetup!=null&&customSetup(gpgpu,binary.webGLProgram),gpgpu.executeProgram()}function makeShaderKey(program,inputs,output){let keyInputs="";inputs.concat(output).forEach(x=>{const hasOffset=x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0,texShape=x.isUniform?"uniform":x.texData.texShape;keyInputs+=`${x.shape}_${texShape}_${hasOffset}`});const keyUserCode=program.userCode;let key=program.constructor.name;return key+="_"+keyInputs+"_"+keyUserCode,key}class Im2ColPackedProgram{constructor(outputShape,inputShape,convInfo){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;const{filterWidth,inChannels,strideWidth,strideHeight,padInfo,outWidth,dilationWidth,dilationHeight,dataFormat}=convInfo,{left,top}=padInfo,itemsPerBlockRow=inChannels*filterWidth,glsl=getGlslDifferences(),isChannelsLast=dataFormat==="channelsLast",rowDim=isChannelsLast?0:1,colDim=isChannelsLast?1:2;let unrolled="";for(let row=0;row<=1;row++)for(let col=0;col<=1;col++)unrolled+=`
          blockIndex = rc.y + ${col};
          pos = rc.x + ${row};

          if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {
            offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${top};
            d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});

            if(d0 < ${inputShape[rowDim]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${strideWidth}. - ${left}.);
              d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${itemsPerBlockRow}.) / ${inChannels}.));

              if(d1 < ${inputShape[colDim]} && d1 >= 0) {

                ch = int(mod(float(pos), ${inChannels}.));

                if (${isChannelsLast}) {
                  innerDims = vec2(d1, ch);
                  result[${row*2+col}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${row*2+col}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${unrolled}

        ${glsl.output} = result;
      }
    `}}class LRNProgram{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[];const rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator;const basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${rad}; j <= ${rad}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${maxD}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${powOperator};
        setOutput(val);
      }
    `}}class LRNGradProgram{constructor(inputShape,depthRadius,bias,alpha,beta){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=inputShape,this.depth=inputShape[3],this.depthRadius=depthRadius,this.bias=bias,this.alpha=alpha,this.beta=beta,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${depthRadius} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${alpha}) * norm + float(${bias});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${alpha})
                * float(${beta})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${beta});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}}class LRNPackedProgram{constructor(xShape,radius,bias,alpha,beta){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const rad=radius,maxD=xShape[3]-1;this.outputShape=xShape;let powOperator;const basis=`float(${bias}) + float(${alpha}) * sum`;beta===.5?powOperator=`inversesqrt(${basis})`:beta===1?powOperator=`1.0/(${basis})`:powOperator=`exp(log(${basis}) * float(-${beta}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${rad};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${rad}; j <= ${rad}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${powOperator};
        setOutput(result);
      }
    `}}class MaxPool2DBackpropProgram{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;const strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${effectiveFilterHeight};
          wR += ${dilationHeight}) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${effectiveFilterWidth} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}}class MaxPool3DBackpropProgram{constructor(convInfo){this.variableNames=["dy","maxPos"],this.outputShape=convInfo.inShape;const strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=effectiveFilterDepth-1-convInfo.padInfo.front,padTop=effectiveFilterHeight-1-convInfo.padInfo.top,padLeft=effectiveFilterWidth-1-convInfo.padInfo.left,lastIndex=effectiveFilterDepth*effectiveFilterHeight*effectiveFilterWidth-1;this.userCode=`
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
           wD += ${dilationDepth}) {
          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;

          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${lastIndex} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
                  wR * ${effectiveFilterWidth} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class MatMulPackedProgram{constructor(aShape,bShape,outputShape,transposeA=!1,transposeB=!1,addBias=!1,activation2=null,hasPreluActivation=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;const sharedDim=transposeA?aShape[1]:aShape[2],sharedDimensionPacked=Math.ceil(sharedDim/2),aSample=transposeA?"i * 2, rc.y":"rc.y, i * 2",bSample=transposeB?"rc.z, i * 2":"i * 2, rc.z",aSwizzle=transposeA?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],bSwizzle=transposeB?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let activationSnippet="",applyActivationSnippet="";activation2&&(hasPreluActivation?activationSnippet=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation2}
        }`:activationSnippet=`vec4 activation(vec4 x) {
          ${activation2}
        }`,applyActivationSnippet="result = activation(result);");const addBiasSnippet=addBias?"result += getBiasAtOutCoords();":"";addBias&&this.variableNames.push("bias"),hasPreluActivation&&this.variableNames.push("preluActivationWeights");let batchASnippet="rc.x",batchBSnippet="rc.x";aShape[0]<bShape[0]?batchASnippet=`int(min(float(rc.x), ${aShape[0]-1}.))`:bShape[0]<aShape[0]&&(batchBSnippet=`int(min(float(rc.x), ${bShape[0]-1}.))`),this.userCode=`
      ${activationSnippet}

      const float sharedDimension = ${sharedDimensionPacked}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
          int batchA = ${batchASnippet};
          int batchB = ${batchBSnippet};
          vec4 a = getMatrixA(batchA, ${aSample});
          vec4 b = getMatrixB(batchB, ${bSample});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${aSwizzle[0]} * ${bSwizzle[0]});
          result += (${aSwizzle[1]} * ${bSwizzle[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${addBiasSnippet}

        ${applyActivationSnippet}

        setOutput(result);
      }
    `}}class MultinomialProgram{constructor(batchSize,numOutcomes,numSamples){this.variableNames=["probs"],this.outputShape=[batchSize,numSamples],this.userCode=`
      uniform float seed;

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${numOutcomes-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${numOutcomes-1}));
      }
    `}getCustomSetupFunc(seed){return(gpgpu,webGLProgram)=>{this.seedLoc==null&&(this.seedLoc=gpgpu.getUniformLocation(webGLProgram,"seed")),gpgpu.gl.uniform1f(this.seedLoc,seed)}}}class OneHotProgram{constructor(numIndices,depth,onValue,offValue){this.variableNames=["indices"],this.outputShape=[numIndices,depth],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${offValue}), float(${onValue}),
                      float(index == coords.y)));
      }
    `}}class PackProgram{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=outputShape;const rank=outputShape.length;if(rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{const channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),outOfBoundsCondition=getOutOfBoundsCondition(rank,outputShape,channels),setup38=getSetup(rank,outputShape[outputShape.length-1],outputShape[outputShape.length-2],channels),output=getOutput(outputShape,channels);this.userCode=`
        void main() {
          ${dtype} rc = getOutputCoords();

          if(${outOfBoundsCondition}) {
            setOutput(vec4(0));
          } else {
            ${setup38}

            setOutput(vec4(${output}));
          }
        }
      `}}}function getSourceCoordsArr(rank,dims){const coords2=[];for(let row=0;row<=1;row++)for(let col=0;col<=1;col++){let coord=`${row===0?"r":"rp1"}, ${col===0?"c":"cp1"}`;for(let d=2;d<rank;d++)coord=`${dims[dims.length-1-d]},`+coord;coords2.push(coord)}return coords2}function getOutOfBoundsCondition(rank,shape,dims){if(rank===1)return`rc > ${shape[0]}`;let cond="";for(let i=rank-2;i<rank;i++)cond+=`${dims[i]} >= ${shape[i]}`,i<rank-1&&(cond+="||");return cond}function getSetup(rank,cols,rows,dims){if(rank===1)return"";const innerDims=dims.slice(-2);return`
    int r = ${innerDims[0]};
    int c = ${innerDims[1]};
    int rp1 = r + 1;
    int cp1 = c + 1;

    bool cEdge = cp1 >= ${cols};
    bool rEdge = rp1 >= ${rows};
  `}function getOutput(shape,dims){const rank=shape.length,sourceCoords=getSourceCoordsArr(rank,dims);return rank===1?`getA(rc),
            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),
            0, 0`:`getA(${sourceCoords[0]}),
          cEdge ? 0. : getA(${sourceCoords[1]}),
          rEdge ? 0. : getA(${sourceCoords[2]}),
          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`}class PadProgram{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);const rank=xShape.length,type=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank);if(rank===1){this.userCode=`
        int start = ${start};
        int end = ${end};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(float(${constantValue}));
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${type} start = ${type}(${start});
      ${type} end = ${type}(${end});

      void main() {
        ${type} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(float(${constantValue}));
        } else {
          ${type} coords = outC - start;
          setOutput(getX(${unpackedCoords}));
        }
      }
    `}}class PadPackedProgram{constructor(xShape,paddings,constantValue){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);const rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,componentSetup=[`${dtype} rc = outputLoc;`,`${coords2[rank-1]} += 1;
       if(${cLimit}) {
      `,rank===1?"":`}
       rc = outputLoc;
       ${coords2[rank-2]} += 1;
       if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {`,rank===1?"":`  ${coords2[rank-1]} += 1;
         if(${cLimit}) {`],paddingArea=rank===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let mainLoop="";for(let i=0,j=rank===1?2:4;i<j;i++)mainLoop+=`
        ${componentSetup[i]}
        if (${paddingArea}) {
          result[${i}] = float(${constantValue});
        } else {
          ${dtype} source = rc - start;
          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
        }
      `;mainLoop+=rank===1?"} ":"}}",this.userCode=`
      const ${dtype} start = ${dtype}(${start});
      const ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${mainLoop}
        setOutput(result);
      }
    `}}class Pool2DProgram{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");const filterWidth=convInfo.filterWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;const isAvgPool=poolType==="avg",batchFlattenPositionStr=`((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`,flattenPositionStr=`(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;let initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){const compareOp2=">=";this.userCode=`
        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
        const ivec2 pads = ivec2(${padTop}, ${padLeft});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${compareOp2} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${flattenPositions?includeBatchInIndex?batchFlattenPositionStr:flattenPositionStr:`wR * ${effectiveFilterWidth} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}const compareOp="max";let returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");const filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
      if (${isAvgPool}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${convInfo.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${initializationValue});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${convInfo.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
            int xC = xCCorner + wC * ${dilationWidth};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
            );

            ${updateSnippet}
          }

          int xC = xCCorner + ${filterWidthNearestVec4};
          if (${filterWidthVec4Remainder===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${updateSnippet}
          } else if (${filterWidthVec4Remainder===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              initializationValue,
              initializationValue
            );

            ${updateSnippet}
          } else if (${filterWidthVec4Remainder===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
              initializationValue
            );

            ${updateSnippet}
          }
        }
        setOutput(${returnValue});
      }
    `}}class Pool3DProgram{constructor(convInfo,poolType,computePositions,flattenPositions=!1,includeBatchInIndex=!1){if(this.variableNames=["x"],poolType==="avg"&&computePositions)throw new Error("Cannot compute positions for average pool.");const filterWidth=convInfo.filterWidth,strideDepth=convInfo.strideDepth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,dilationDepth=convInfo.dilationDepth,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,effectiveFilterDepth=convInfo.effectiveFilterDepth,effectiveFilterHeight=convInfo.effectiveFilterHeight,effectiveFilterWidth=convInfo.effectiveFilterWidth,padFront=convInfo.padInfo.front,padTop=convInfo.padInfo.top,padLeft=convInfo.padInfo.left;this.outputShape=convInfo.outShape;const isAvgPool=poolType==="avg";let initializationValue="0.0";if(isAvgPool||(initializationValue="-1.0 / 1e-20"),computePositions){const compareOp2=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${effectiveFilterDepth};
              wD += ${dilationDepth}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${convInfo.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${effectiveFilterHeight};
                wR += ${dilationHeight}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${effectiveFilterWidth};
                  wC += ${dilationWidth}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${compareOp2} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${flattenPositions?includeBatchInIndex?`(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch`:`wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
                      wR * ${effectiveFilterWidth} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}const compareOp="max";let returnValue=`${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;poolType==="avg"&&(returnValue="avgValue / count");const filterWidthNearestVec4=Math.floor(filterWidth/4)*4,filterWidthVec4Remainder=filterWidth%4,updateSnippet=`
      if (${isAvgPool}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${convInfo.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${initializationValue});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
            wD += ${dilationDepth}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${convInfo.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
              int xC = xCCorner + wC * ${dilationWidth};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
              );

              ${updateSnippet}
            }

            int xC = xCCorner + ${filterWidthNearestVec4};
            if (${filterWidthVec4Remainder===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${updateSnippet}
            } else if (${filterWidthVec4Remainder===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                initializationValue,
                initializationValue
              );

              ${updateSnippet}
            } else if (${filterWidthVec4Remainder===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                initializationValue
              );

              ${updateSnippet}
            }
          }
          setOutput(${returnValue});
        }
      }
    `}}class ReduceProgram{constructor(reduceInfo,reduceType){this.variableNames=["x"];const{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];let initializationValue="0.0",compareOp="";reduceType==="prod"?initializationValue="1.0":reduceType==="min"?(initializationValue="1.0 / 1e-20",compareOp="min"):reduceType==="max"&&(initializationValue="-1.0 / 1e-20",compareOp="max");let returnValue=`${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;reduceType==="sum"?returnValue="sumValue":reduceType==="prod"?returnValue="prodValue":reduceType==="all"?returnValue="allValue":reduceType==="any"&&(returnValue="anyValue");const windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4;let updateSnippet=`
      if (${reduceType==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${reduceType==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `,vecType="vec4";reduceType==="all"?(initializationValue="1.0",updateSnippet=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,vecType="bvec4"):reduceType==="any"&&(initializationValue="0.0",updateSnippet=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,vecType="bvec4");let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${checkOutOfBounds}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        vec4 minMaxValue = vec4(${initializationValue});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder===1}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder===2}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder===3}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${updateSnippet}
        }
        setOutput(${returnValue});
      }
    `}}class ReshapePackedProgram{constructor(outputShape,inputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=outputShape;let mainLoop="";for(let i=0;i<4;i++){let thisRC="thisRC = rc;";i%2===1&&(thisRC+="thisRC.z += 1;"),i>1&&(thisRC+="thisRC.y += 1;"),mainLoop+=`
        ${thisRC}
        ${i>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${i}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${i>0?"}":""}
      `}this.userCode=`
      ${getReshapedInputCoords(inputShape)}
      ${getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${outputShape[1]};
        int cols = ${outputShape[2]};

        ${mainLoop}

        setOutput(result);
      }
    `}}function getReshapedInputCoords(shape){const coordsFromIndexSnippet=getLogicalCoordinatesFromFlatIndex(["r","c","d"],shape);return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${coordsFromIndexSnippet}
      return ivec3(r, c, d);
    }
  `}class ResizeBilinearBackpropProgram{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;const[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${heightScale});
        const float widthScale = float(${widthScale});

        const float invHeightScale = float(${invHeightScale});
        const float invWidthScale = float(${invWidthScale});

        const int winHeight = int(${winHeight});
        const int winWidth = int(${winWidth});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${yHeight}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${yWidth}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight-1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${xWidth-1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}}class ResizeBilinearProgram{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];const[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];const effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${effectiveInSize[0]/effectiveOutSize[0]},
          ${effectiveInSize[1]/effectiveOutSize[1]});
      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `}}class ResizeBilinearPackedProgram{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];const effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth];this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${effectiveInSize[0]/effectiveOutSize[0]},
          ${effectiveInSize[1]/effectiveOutSize[1]},
          ${effectiveInSize[1]/effectiveOutSize[1]});
      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
                                     ${oldWidth}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = vec3(yRC) * effectiveInputOverOutputRatioRC;

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(sourceFracIndexRC);
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${depth-1};
        bool hasNextRow = coords.z < ${newWidth-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}}class ResizeNearestNeigborBackpropProgram{constructor(dy,x,alignCorners){this.variableNames=["dy"],this.outputShape=[],this.outputShape=x.shape;const[,xHeight,xWidth]=x.shape,[,yHeight,yWidth]=dy.shape,effectiveXSize=[alignCorners&&yHeight>1?xHeight-1:xHeight,alignCorners&&yWidth>1?xWidth-1:xWidth],effectiveYSize=[alignCorners&&yHeight>1?yHeight-1:yHeight,alignCorners&&yWidth>1?yWidth-1:yWidth],heightScale=effectiveXSize[0]/effectiveYSize[0],widthScale=effectiveXSize[1]/effectiveYSize[1],invHeightScale=1/heightScale,invWidthScale=1/widthScale,winHeight=Math.ceil(invHeightScale)*2+2,winWidth=Math.ceil(invWidthScale)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${heightScale});
        const float widthScale = float(${widthScale});

        const float invHeightScale = float(${invHeightScale});
        const float invWidthScale = float(${invWidthScale});

        const int winHeight = int(${winHeight});
        const int winWidth = int(${winWidth});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${yHeight}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${yWidth}) {
              continue;
            }

            float sourceFracRow =
              float(${effectiveXSize[0]}) *
                (float(dyR) / float(${effectiveYSize[0]}));

            float sourceFracCol =
                float(${effectiveXSize[1]}) *
                  (float(dyC) / float(${effectiveYSize[1]}));

            int sourceNearestRow = int(min(
                float(int(${xHeight}) - 1),
                ${alignCorners} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${xWidth}) - 1),
                ${alignCorners} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}}class ResizeNearestNeighborProgram{constructor(inputShape,newHeight,newWidth,alignCorners){this.variableNames=["A"],this.outputShape=[];const[batch,oldHeight,oldWidth,depth]=inputShape;this.outputShape=[batch,newHeight,newWidth,depth];const effectiveInSize=[alignCorners&&newHeight>1?oldHeight-1:oldHeight,alignCorners&&newWidth>1?oldWidth-1:oldWidth],effectiveOutSize=[alignCorners&&newHeight>1?newHeight-1:newHeight,alignCorners&&newWidth>1?newWidth-1:newWidth],roundBase=alignCorners?"0.5":"0.0";this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${effectiveInSize[0]/effectiveOutSize[0]},
          ${effectiveInSize[1]/effectiveOutSize[1]});
      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));

        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}}class ReverseProgram{constructor(xShape,axis){this.variableNames=["x"];const rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);if(this.outputShape=xShape,rank===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${xShape[0]} - coord - 1));
        }
      `;return}const getInCoord=i=>axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - coords[${i}] - 1`:`coords[${i}]`,inCoords=xShape.map((_,i)=>getInCoord(i)).join(","),type=getCoordsDataType(rank);this.userCode=`
      void main() {
        ${type} coords = getOutputCoords();
        setOutput(getX(${inCoords}));
      }
    `}}class ReversePackedProgram{constructor(xShape,axis){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const rank=xShape.length;if(rank>4)throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);this.outputShape=xShape;const channels=getChannels("rc",rank),nextColumn=`${channels[rank-1]} + 1 < ${this.outputShape[rank-1]}`,nextRow=`${channels[rank-2]} + 1 < ${this.outputShape[rank-2]}`,type=getCoordsDataType(rank);rank===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${xShape[0]} - rc - 1),
            ${xShape[0]} - rc - 1);
          if(${nextColumn}){
              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
                ${xShape[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${type} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${getR(channels.slice())};
          if(${nextColumn}){
            result.g = ${getG(channels.slice())};
          }
          if(${nextRow}) {
            result.b = ${getB(channels.slice())};
            if(${nextColumn}) {
              result.a = ${getA(channels.slice())};
            }
          }
          setOutput(result);
        }
    `;function getR(channels2){return getChannel(channels2)}function getG(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",getChannel(channels2)}function getB(channels2){return channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getA(channels2){return channels2[rank-1]="("+channels2[rank-1]+" + 1)",channels2[rank-2]="("+channels2[rank-2]+" + 1)",getChannel(channels2)}function getChannel(channels2){const inCoordsArray=xShape.map((_,i)=>getInCoord(i,channels2)),inCoords=inCoordsArray.join(","),innerDims=inCoordsArray.slice(-2).join(",");return`getChannel(getX(${inCoords}), vec2(${innerDims}))`}function getInCoord(i,channels1){return axis.indexOf(i)!==-1&&xShape[i]!==1?`${xShape[i]} - ${channels1[i]} - 1`:`${channels1[i]}`}}}class ScatterProgram{constructor(updateSize,sliceDim,indicesRank,updatesRank,strides,shape,summingDupeIndex=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=shape;const stridesType=getCoordsDataType(strides.length),dtype=getCoordsDataType(shape.length);let indicesString="";indicesRank===1?indicesString="i":indicesRank===2&&(indicesString="i, j");const indicesSnippet=`getIndices(${indicesString})`;let updatesString="";updatesRank===1?updatesString="i":updatesRank===2&&(updatesString="i, coords[1]");const updatesSnippet=`getUpdates(${updatesString})`,strideString=sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${stridesType} strides = ${stridesType}(${strides});

        void main() {
          ${dtype} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${updateSize}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${sliceDim}; j++) {
              int index = round(${indicesSnippet});
              flattenedIndex += index * ${strideString};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${updatesSnippet};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}}class SegmentOpProgram{constructor(segOpInfo,segOpType){this.variableNames=["x","segmentIds"];const windowSize=segOpInfo.windowSize,batchSize=segOpInfo.batchSize,inSize=segOpInfo.inSize,numSegments=segOpInfo.numSegments,outSize=numSegments*Math.ceil(inSize/windowSize);this.outputShape=[batchSize,outSize];const initializationValue="0.0",returnValue="sumValue",windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4,updateSnippet=`
        sumValue += dot(values, segFilter);
    `;let checkValueOutOfBounds="";inSize%windowSize>0&&(checkValueOutOfBounds=`
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return initializationValue;
        }
      `);let checkSegmentIdOutOfBounds="";inSize%windowSize>0&&(checkSegmentIdOutOfBounds=`
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${initializationValue};

      float getValue(int batch, int inIdx) {
        ${checkValueOutOfBounds}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${checkSegmentIdOutOfBounds}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${numSegments})) * float(${windowSize}));
        int currentSeg = int(mod(float(outIdx), float(${numSegments})));

        float sumValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder===1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${updateSnippet}
        }
        setOutput(${returnValue});
      }
    `}}class SelectProgram{constructor(cRank,shape,rank){this.variableNames=["c","a","b"],this.outputShape=shape;let cCoords,abCoords;if(rank>4)throw Error(`Where for rank ${rank} is not yet supported`);if(rank===1)abCoords="resRC",cCoords="resRC";else{const currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w"],cCoordVars=[],abCoordVars=[];for(let i=0;i<shape.length;i++)abCoordVars.push(`${currentCoords[i]}`),i<cRank&&cCoordVars.push(`${currentCoords[i]}`);cCoords=cCoordVars.join(),abCoords=abCoordVars.join()}const dtype=getCoordsDataType(rank);this.userCode=`
      void main() {
        ${dtype} resRC = getOutputCoords();
        float cVal = getC(${cCoords});
        if (cVal >= 1.0) {
          setOutput(getA(${abCoords}));
        } else {
          setOutput(getB(${abCoords}));
        }
      }
    `}}class SliceProgram{constructor(destSize){this.variableNames=["source"],this.outputShape=destSize,this.rank=destSize.length;const dtype=getCoordsDataType(this.rank),uniformPart=`uniform int start[${this.rank}];`,sourceCoords=getCoords2(this.rank);let body2;const coordSum=destSize.map((_,i)=>`sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`);body2=`
        ${dtype} sourceLoc;
        ${dtype} coords = getOutputCoords();
        ${coordSum.join(`
`)}
      `,this.userCode=`
      ${uniformPart}
      void main() {
        ${body2}
        setOutput(getSource(${sourceCoords}));
      }
    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}}const coords=["x","y","z","w","u","v"];function getCoords2(rank){if(rank===1)return"sourceLoc";if(rank<=6)return coords.slice(0,rank).map(x=>"sourceLoc."+x).join(",");throw Error(`Slicing for rank ${rank} is not yet supported`)}class SlicePackedProgram{constructor(destSize){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=destSize,this.rank=destSize.length;const dtype=getCoordsDataType(this.rank),coords2=getChannels("coords",this.rank),sourceLoc=getChannels("sourceLoc",this.rank),innerDims=this.rank===1?"sourceLoc":`vec2(${sourceLoc.slice(-2).join()})`,getChannel=`getChannel(getSource(${sourceLoc.join()}), ${innerDims})`,upperRow=`
      result.x = ${getChannel};
      if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
        ++${sourceLoc[this.rank-1]};
        result.y = ${getChannel};
        --${sourceLoc[this.rank-1]};
      }
    `,lowerRow=this.rank===1?"":`
      --${coords2[this.rank-1]};
      if (++${coords2[this.rank-2]} < ${destSize[this.rank-2]}) {
        ++${sourceLoc[this.rank-2]};
        result.z = ${getChannel};
        if (++${coords2[this.rank-1]} < ${destSize[this.rank-1]}) {
          ++${sourceLoc[this.rank-1]};
          result.w = ${getChannel};
        }
      }
    `,sourceLocSetup=this.rank<=4?`sourceLoc = coords +
            ${dtype}(${destSize.map((_,i)=>`start[${i}]`).join()});`:destSize.map((_,i)=>`${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join(`
`);this.userCode=`
      uniform int start[${this.rank}];
      void main() {
        ${dtype} coords = getOutputCoords();
        ${dtype} sourceLoc;
        ${sourceLocSetup}
        vec4 result = vec4(0.);
        ${upperRow}
        ${lowerRow}
        setOutput(result);
      }
    `}getCustomSetupFunc(start){if(start.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${start.length})`);return(gpgpu,webGLProgram)=>{if(this.startLoc==null&&(this.startLoc=gpgpu.getUniformLocationNoThrow(webGLProgram,"start"),this.startLoc==null))return;gpgpu.gl.uniform1iv(this.startLoc,start)}}}class StridedSliceProgram{constructor(begin,strides,size){this.variableNames=["x"],this.outputShape=size;const rank=size.length,inputDtype=getCoordsDataType(size.length),dtype=getCoordsDataType(size.length);let newCoords="";if(rank===1)newCoords="coords * strides + begin";else{let outputAxis=0;newCoords=size.map((_,i)=>(outputAxis++,size.length===1?`coords * strides[${i}] + begin[${i}]`:`coords[${outputAxis-1}] * strides[${i}] + begin[${i}]`)).join(",")}this.userCode=`
      ${inputDtype} begin = ${inputDtype}(${begin});
      ${inputDtype} strides = ${inputDtype}(${strides});

      void main() {
        ${dtype} coords = getOutputCoords();
        setOutput(getX(${newCoords}));
      }
    `}}class TextureManager{constructor(gpgpu){this.gpgpu=gpgpu,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(shapeRC,usage,isPacked){const physicalTexType=getPhysicalFromLogicalTextureType(usage,isPacked),shapeKey=getKeyFromTextureShape(shapeRC,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]),shapeKey in this.usedTextures||(this.usedTextures[shapeKey]=[]);const texBytes=computeBytes(shapeRC,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked);if(this.freeTextures[shapeKey].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=texBytes,this.log();const newTexture2=this.freeTextures[shapeKey].shift();return this.usedTextures[shapeKey].push(newTexture2),newTexture2}let newTexture;return physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT32?newTexture=this.gpgpu.createPackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_2X2_FLOAT16?newTexture=this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT32?newTexture=this.gpgpu.createFloat32MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.UNPACKED_FLOAT16?newTexture=this.gpgpu.createFloat16MatrixTexture(shapeRC[0],shapeRC[1]):physicalTexType===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(newTexture=this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0],shapeRC[1])),this.usedTextures[shapeKey].push(newTexture),this.numUsedTextures++,this._numBytesAllocated+=texBytes,this.log(),newTexture}releaseTexture(texture,shape,logicalTexType,isPacked){if(this.freeTextures==null)return;const physicalTexType=getPhysicalFromLogicalTextureType(logicalTexType,isPacked),shapeKey=getKeyFromTextureShape(shape,physicalTexType,isPacked);shapeKey in this.freeTextures||(this.freeTextures[shapeKey]=[]);const texBytes=computeBytes(shape,physicalTexType,this.gpgpu.gl,this.gpgpu.textureConfig,isPacked),deleteTexThreshold=env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");deleteTexThreshold!==-1&&this._numBytesAllocated>deleteTexThreshold?(this.gpgpu.deleteMatrixTexture(texture),this._numBytesAllocated-=texBytes):(this.freeTextures[shapeKey].push(texture),this.numFreeTextures++,this._numBytesFree+=texBytes),this.numUsedTextures--;const texList=this.usedTextures[shapeKey],texIndex=texList.indexOf(texture);if(texIndex<0)throw new Error("Cannot release a texture that was never provided by this texture manager");texList.splice(texIndex,1),this.log()}log(){if(!this.logEnabled)return;const total=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${total})`);const freeRatio=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*freeRatio)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(const texShape in this.freeTextures)this.freeTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});for(const texShape in this.usedTextures)this.usedTextures[texShape].forEach(tex=>{this.gpgpu.deleteMatrixTexture(tex)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function numBytesForInternalFormat(gl,internalFormat){const glany=gl;if(internalFormat===glany.R32F)return 4;if(internalFormat===glany.R16F)return 2;if(internalFormat===glany.RGBA32F)return 16;if(internalFormat===gl.RGBA)return 16;if(internalFormat===glany.RGBA16F)return 8;throw new Error(`Unknown internal format ${internalFormat}`)}function computeBytes(shape,physicalTexType,gl,textureConfig,isPacked){const internalFormat=internalFormatForPhysicalTexType(physicalTexType,textureConfig);let numElements;if(isPacked){const[packedWidth,packedHeight]=getPackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=packedWidth*packedHeight}else{const[width,height]=getUnpackedMatrixTextureShapeWidthHeight(shape[0],shape[1]);numElements=width*height}const bytesPerElement2=numBytesForInternalFormat(gl,internalFormat);return numElements*bytesPerElement2}function internalFormatForPhysicalTexType(physicalTexType,textureConfig){switch(physicalTexType){case PhysicalTextureType.PACKED_2X2_FLOAT32:return getInternalFormatForPackedMatrixTexture(textureConfig);case PhysicalTextureType.PACKED_2X2_FLOAT16:return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT32:return getInternalFormatForFloat32MatrixTexture(textureConfig);case PhysicalTextureType.UNPACKED_FLOAT16:return getInternalFormatForFloat16MatrixTexture(textureConfig);case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);default:throw new Error(`Unknown physical texture type ${physicalTexType}`)}}function getPhysicalTextureForRendering(isPacked){return env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?isPacked?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32:isPacked?PhysicalTextureType.PACKED_2X2_FLOAT16:PhysicalTextureType.UNPACKED_FLOAT16}function getPhysicalFromLogicalTextureType(logicalTexType,isPacked){if(logicalTexType===TextureUsage.UPLOAD)return PhysicalTextureType.PACKED_2X2_FLOAT32;if(logicalTexType===TextureUsage.RENDER||logicalTexType==null)return getPhysicalTextureForRendering(isPacked);if(logicalTexType===TextureUsage.DOWNLOAD||logicalTexType===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${logicalTexType}`)}function getKeyFromTextureShape(shapeRowsCol,physicalTexType,isPacked){return`${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`}class TileProgram{constructor(aShape,reps){this.variableNames=["A"];const outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[i]*reps[i];this.outputShape=outputShape,this.rank=outputShape.length;const dtype=getCoordsDataType(this.rank),sourceCoords=getSourceCoords3(aShape);this.userCode=`
      void main() {
        ${dtype} resRC = getOutputCoords();
        setOutput(getA(${sourceCoords}));
      }
    `}}function getSourceCoords3(aShape){const rank=aShape.length;if(rank>5)throw Error(`Tile for rank ${rank} is not yet supported`);if(rank===1)return`imod(resRC, ${aShape[0]})`;const currentCoords=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],sourceCoords=[];for(let i=0;i<aShape.length;i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);return sourceCoords.join()}class UnaryOpProgram{constructor(aShape,opSnippet){this.variableNames=["A"],this.outputShape=aShape,this.userCode=`
      float unaryOperation(float x) {
        ${opSnippet}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}}const CHECK_NAN_SNIPPET3="if (isnan(x)) return x;",LINEAR="return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET3+`
  return (x < 0.0) ? 0.0 : x;
`,RELU6=CHECK_NAN_SNIPPET3+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,ELU2="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
  float scale = ${backend_util_exports.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function STEP(alpha=0){return CHECK_NAN_SNIPPET3+`
    return x > 0.0 ? 1.0 : float(${alpha});
  `}const NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,IS_NAN="return float(isnan(x));",IS_INF="return float(isinf(x));",IS_FINITE="return float(!isnan(x) && !isinf(x));",ROUND=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG=`if (x < 0.0) return NAN;
  return log(x);`,LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS=`
  float epsilon = 1.1920928955078125e-7;
  float threshold = log(epsilon) + 2.0;

  bool too_large = x > -threshold;
  bool too_small = x < threshold;

  float result;
  float exp_x = exp(x);

  if (too_large){
    result = x;
  }
  else if (too_small){
    result = exp_x;
  }
  else{
    result = log(exp_x + 1.0);
  }
  return result;
`,ASIN=CHECK_NAN_SNIPPET3+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,ACOS=CHECK_NAN_SNIPPET3+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,ATAN=CHECK_NAN_SNIPPET3+`
  return atan(x);
`,SINH=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,COSH=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,TANH=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,ASINH=CHECK_NAN_SNIPPET3+"return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET3+`
  if (x < 1.0) return NAN;
  return log(x + sqrt(x * x - 1.0));`,ATANH=CHECK_NAN_SNIPPET3+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ERF=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${backend_util_exports.ERF_P};
  float a1 = ${backend_util_exports.ERF_A1};
  float a2 = ${backend_util_exports.ERF_A2};
  float a3 = ${backend_util_exports.ERF_A3};
  float a4 = ${backend_util_exports.ERF_A4};
  float a5 = ${backend_util_exports.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,RECIPROCAL="return 1.0 / x;",LOGICAL_NOT="return float(!(x >= 1.0));",CLONE="return x;",LINEAR2="return x;",LOG2=`
  vec4 result = log(x);
  vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
  result.r = isNaN.r == 1.0 ? NAN : result.r;
  result.g = isNaN.g == 1.0 ? NAN : result.g;
  result.b = isNaN.b == 1.0 ? NAN : result.b;
  result.a = isNaN.a == 1.0 ? NAN : result.a;

  return result;
`,RELU2=`
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,RELU62=`
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`,ELU3=`
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`;class UnaryOpPackedProgram{constructor(aShape,opSnippet){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=aShape,this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${opSnippet}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}}class UnpackProgram{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=outputShape;const rank=outputShape.length,channels=getChannels("rc",rank),dtype=getCoordsDataType(rank),sourceCoords=getSourceCoords(rank,channels),innerDims=channels.slice(-2),coords2=rank<=1?"rc":`vec2(${innerDims.join(",")})`;this.userCode=`
      void main() {
        ${dtype} rc = getOutputCoords();
        vec4 packedInput = getA(${sourceCoords});

        setOutput(getChannel(packedInput, ${coords2}));
      }
    `}}const{segment_util:segment_util2}=backend_util_exports,split11=kernel_impls_exports.split,tile10=kernel_impls_exports.tile,topkImpl3=kernel_impls_exports.topkImpl,whereImpl3=kernel_impls_exports.whereImpl,EPSILON_FLOAT322=1e-7,EPSILON_FLOAT162=1e-4,binaryCaches={};function getBinaryCache(webGLVersion){return webGLVersion in binaryCaches||(binaryCaches[webGLVersion]={}),binaryCaches[webGLVersion]}function mapActivationToShaderProgram(activation2,packed=!1){if(activation2==="linear")return packed?LINEAR2:LINEAR;if(activation2==="relu")return packed?RELU2:RELU;if(activation2==="elu")return packed?ELU3:ELU2;if(activation2==="relu6")return packed?RELU62:RELU6;if(activation2==="prelu")return packed?PRELU2:PRELU;throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`)}const CPU_HANDOFF_SIZE_THRESHOLD=128,BEFORE_PAGING_CONSTANT=600;function numMBBeforeWarning(){return env().global.screen==null?1024:env().global.screen.height*env().global.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT/1024/1024}const MATMUL_SHARED_DIM_THRESHOLD=1e3;class MathBackendWebGL extends KernelBackend{constructor(gpgpu){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!env().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(gpgpu==null){const gl=getWebGLContext(env().getNumber("WEBGL_VERSION"));this.binaryCache=getBinaryCache(env().getNumber("WEBGL_VERSION")),this.gpgpu=new GPGPUContext(gl),this.canvas=gl.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=gpgpu,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=gpgpu.gl.canvas;this.textureManager=new TextureManager(this.gpgpu),this.numMBBeforeWarning=numMBBeforeWarning(),this.texData=new DataStorage(this,engine15())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(values,shape,dtype){if((env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||env().getBool("DEBUG"))&&this.checkNumericalProblems(values),dtype==="complex64"&&values!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const dataId={};return this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0}),dataId}incRef(dataId){const texData=this.texData.get(dataId);texData.refCount++}decRef(dataId){if(this.texData.has(dataId)){const texData=this.texData.get(dataId);texData.refCount--}}move(dataId,values,shape,dtype){if(env().getBool("DEBUG")&&this.checkNumericalProblems(values),dtype==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(dataId,{shape,dtype,values,usage:TextureUsage.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(tensorInfo){const dataId=tensorInfo.dataId;if(this.texData.has(dataId)){const textureData=this.texData.get(dataId);textureData.refCount--,textureData.refCount<1&&this.disposeData(dataId)}}readSync(dataId){const texData=this.texData.get(dataId),{values,dtype,complexTensorInfos,slice:slice21,shape,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);const res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data2=this.readSync(res.dataId);return this.disposeIntermediateTensorInfo(res),data2}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(dtype==="string")return values;const shouldTimeProgram=this.activeTimers!=null;let start;shouldTimeProgram&&(start=util_exports.now());let result;if(dtype==="complex64"){const realValues=this.readSync(complexTensorInfos.real.dataId),imagValues=this.readSync(complexTensorInfos.imag.dataId);result=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else result=this.getValuesFromTexture(dataId);return shouldTimeProgram&&(this.downloadWaitMs+=util_exports.now()-start),this.convertAndCacheOnCPU(dataId,result)}async read(dataId){if(this.pendingRead.has(dataId)){const subscribers2=this.pendingRead.get(dataId);return new Promise(resolve=>subscribers2.push(resolve))}const texData=this.texData.get(dataId),{values,shape,slice:slice21,dtype,complexTensorInfos,isPacked}=texData;if(slice21!=null){let program;isPacked?program=new UnaryOpPackedProgram(shape,CLONE):program=new UnaryOpProgram(shape,CLONE);const res=this.runWebGLProgram(program,[{dataId,shape,dtype}],dtype),data2=this.read(res.dataId);return this.disposeIntermediateTensorInfo(res),data2}if(values!=null)return this.convertAndCacheOnCPU(dataId);if(!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&env().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let buffer11=null,tmpDownloadTarget;if(dtype!=="complex64"&&env().get("WEBGL_BUFFER_SUPPORTED")){tmpDownloadTarget=this.decode(dataId);const tmpData=this.texData.get(tmpDownloadTarget.dataId);buffer11=this.gpgpu.createBufferFromTexture(tmpData.texture,...getDenseTexShape(shape))}this.pendingRead.set(dataId,[]),dtype!=="complex64"&&await this.gpgpu.createAndWaitForFence();let vals;if(dtype==="complex64"){const ps=await Promise.all([this.read(complexTensorInfos.real.dataId),this.read(complexTensorInfos.imag.dataId)]),realValues=ps[0],imagValues=ps[1];vals=backend_util_exports.mergeRealAndImagArrays(realValues,imagValues)}else if(buffer11==null)vals=this.getValuesFromTexture(dataId);else{const size=util_exports.sizeFromShape(shape);vals=this.gpgpu.downloadFloat32MatrixFromBuffer(buffer11,size)}tmpDownloadTarget!=null&&this.disposeIntermediateTensorInfo(tmpDownloadTarget);const dTypeVals=this.convertAndCacheOnCPU(dataId,vals),subscribers=this.pendingRead.get(dataId);return this.pendingRead.delete(dataId),subscribers.forEach(resolve=>resolve(dTypeVals)),this.pendingDisposal.has(dataId)&&(this.pendingDisposal.delete(dataId),this.disposeData(dataId),this.pendingDeletes--),dTypeVals}checkNumericalProblems(values){if(values==null)return;for(let i=0;i<values.length;i++){const num=values[i];if(!canBeRepresented(num))throw env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${num} cannot be represented on this device.`)}}getValuesFromTexture(dataId){const{shape,dtype,isPacked}=this.texData.get(dataId),size=util_exports.sizeFromShape(shape);if(env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const tmpTarget=this.decode(dataId),tmpData2=this.texData.get(tmpTarget.dataId),vals2=this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture,...getDenseTexShape(shape)).subarray(0,size);return this.disposeIntermediateTensorInfo(tmpTarget),vals2}const shouldUsePackedProgram=env().getBool("WEBGL_PACK")&&isPacked===!0,outputShape=shouldUsePackedProgram?getShapeAs3D(shape):shape,program=shouldUsePackedProgram?new EncodeFloatPackedProgram(outputShape):new EncodeFloatProgram(outputShape),output=this.runWebGLProgram(program,[{shape:outputShape,dtype,dataId}],"float32"),tmpData=this.texData.get(output.dataId),vals=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture,tmpData.texShape[0],tmpData.texShape[1]).subarray(0,size);return this.disposeIntermediateTensorInfo(output),vals}async time(f){const oldActiveTimers=this.activeTimers,newActiveTimers=[];let outerMostTime=!1;this.programTimersStack==null?(this.programTimersStack=newActiveTimers,outerMostTime=!0):this.activeTimers.push(newActiveTimers),this.activeTimers=newActiveTimers,f();const flattenedActiveTimerQueries=util_exports.flatten(this.activeTimers.map(d=>d.query)).filter(d=>d!=null),flattenedActiveTimerNames=util_exports.flatten(this.activeTimers.map(d=>d.name)).filter(d=>d!=null);this.activeTimers=oldActiveTimers,outerMostTime&&(this.programTimersStack=null);const res={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const kernelMs=await Promise.all(flattenedActiveTimerQueries);res.kernelMs=util_exports.sum(kernelMs),res.getExtraProfileInfo=()=>kernelMs.map((d,i)=>({name:flattenedActiveTimerNames[i],ms:d})).map(d=>`${d.name}: ${d.ms}`).join(", ")}else res.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,res}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:util_exports.now(),endMs:null}}endTimer(query){return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),query):(query.endMs=util_exports.now(),query)}async getQueryTime(query){if(env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(query);const timerQuery=query;return timerQuery.endMs-timerQuery.startMs}disposeData(dataId){if(this.pendingDisposal.has(dataId))return;if(this.pendingRead.has(dataId)){this.pendingDisposal.add(dataId),this.pendingDeletes++;return}if(!this.texData.has(dataId))return;if(this.texData.get(dataId).complexParentRefCount>0){this.texData.get(dataId).refCount--;return}this.releaseGPUData(dataId);const{complexTensorInfos}=this.texData.get(dataId);complexTensorInfos!=null&&(this.texData.get(complexTensorInfos.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.real),this.texData.get(complexTensorInfos.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(complexTensorInfos.imag)),this.texData.delete(dataId)}releaseGPUData(dataId){const{texture,dtype,texShape,usage,isPacked,slice:slice21}=this.texData.get(dataId),key=slice21&&slice21.origDataId||dataId,refCount=this.dataRefCount.get(key);refCount>1?this.dataRefCount.set(key,refCount-1):(this.dataRefCount.delete(key),texture!=null&&(this.numBytesInGPU-=this.computeBytes(texShape,dtype),this.textureManager.releaseTexture(texture,texShape,usage,isPacked)));const texData=this.texData.get(dataId);texData.texture=null,texData.texShape=null,texData.isPacked=!1,texData.slice=null}getTexture(dataId){return this.uploadToGPU(dataId),this.texData.get(dataId).texture}getDataInfo(dataId){return this.texData.get(dataId)}getCPUBackend(){return env().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=engine15().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(inputs,sizeThreshold=CPU_HANDOFF_SIZE_THRESHOLD){const cpuBackend=this.getCPUBackend();return!this.warnedAboutCPUBackend&&cpuBackend==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),cpuBackend!=null&&inputs.every(input2=>this.texData.get(input2.dataId).texture==null&&util_exports.sizeFromShape(input2.shape)<sizeThreshold)}getGPGPUContext(){return this.gpgpu}slice(x,begin,size){if(this.shouldExecuteOnCPU([x])){const outValues=sliceImplCPU(this.texData.get(x.dataId).values,begin,size,x.shape,x.dtype);return this.makeOutput(size,x.dtype,outValues)}if(util_exports.sizeFromShape(size)===0)return tensor4([],size,x.dtype);const{isPacked}=this.texData.get(x.dataId),isContinous=slice_util_exports.isSliceContinous(x.shape,begin,size);if(isPacked||!isContinous){const program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SlicePackedProgram(size):new SliceProgram(size),customSetup=program.getCustomSetupFunc(begin);return this.compileAndRun(program,[x],null,customSetup)}return this.uploadToGPU(x.dataId),this.shallowSlice(x,begin,size)}shallowSlice(x,begin,size){const xTexData=this.texData.get(x.dataId),t=this.makeOutput(size,x.dtype),newTexData=this.texData.get(t.dataId);Object.assign(newTexData,xTexData),newTexData.shape=size,newTexData.dtype=x.dtype;let flatOffset=slice_util_exports.computeFlatOffset(begin,x.strides);xTexData.slice&&(flatOffset+=xTexData.slice.flatOffset),newTexData.slice={flatOffset,origDataId:xTexData.slice&&xTexData.slice.origDataId||x.dataId};const refCount=this.dataRefCount.get(newTexData.slice.origDataId)||1;return this.dataRefCount.set(newTexData.slice.origDataId,refCount+1),t}stridedSlice(x,begin,end,strides){const cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.stridedSlice(x,begin,end,strides));if(cpuRes)return cpuRes;const outShape=slice_util_exports.computeOutShape(begin,end,strides);if(outShape.some(axis=>axis===0))return tensor4([],outShape);const program=new StridedSliceProgram(begin,strides,outShape);return this.compileAndRun(program,[x])}reverse(x,axis){const program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ReversePackedProgram(x.shape,axis):new ReverseProgram(x.shape,axis);return this.compileAndRun(program,[x])}neg(x){const cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.neg(x));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,NEG,x.dtype);const program=new UnaryOpProgram(x.shape,NEG);return this.compileAndRun(program,[x])}batchMatMul(a,b,transposeA,transposeB){const outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],sharedDim=transposeA?a.shape[1]:a.shape[2],batch=Math.max(a.shape[0],b.shape[0]);if((outerShapeA===1||outerShapeB===1)&&sharedDim>MATMUL_SHARED_DIM_THRESHOLD){transposeA&&(a=transpose(a,[0,2,1])),transposeB&&(b=transpose(b,[0,2,1]));const a3D=outerShapeB===1?a:a.as3D(batch,sharedDim,1),axis=outerShapeB===1?2:1,b3D=outerShapeB===1?b.as3D(batch,1,sharedDim):b,product=mul(a3D,b3D);return product.sum(axis,!0)}const dtype=upcastType(a.dtype,b.dtype),program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB);return this.compileAndRun(program,[a,b],dtype)}fusedBatchMatMul({a,b,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}){const outerShapeA=transposeA?a.shape[2]:a.shape[1],outerShapeB=transposeB?b.shape[1]:b.shape[2],batch=Math.max(a.shape[0],b.shape[0]),dtype=upcastType(a.dtype,b.dtype),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,program=new MatMulPackedProgram(a.shape,b.shape,[batch,outerShapeA,outerShapeB],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[a,b];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs,dtype)}localResponseNormalization4D(x,radius,bias,alpha,beta){const program=env().getBool("WEBGL_PACK_NORMALIZATION")?new LRNPackedProgram(x.shape,radius,bias,alpha,beta):new LRNProgram(x.shape,radius,bias,alpha,beta);return this.compileAndRun(program,[x])}LRNGrad(dy,inputImage,outputImage,depthRadius,bias,alpha,beta){const program=new LRNGradProgram(inputImage.shape,depthRadius,bias,alpha,beta);return this.compileAndRun(program,[inputImage,outputImage,dy])}tile(x,reps){if(x.dtype==="string"){const data2=this.readSync(x.dataId),decodedData=data2.map(d=>util_exports.decodeString(d)),buf=buffer(x.shape,x.dtype,decodedData);return tile10(buf,reps)}const program=new TileProgram(x.shape,reps);return this.compileAndRun(program,[x])}pad(x,paddings,constantValue){const program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PadPackedProgram(x.shape,paddings,constantValue):new PadProgram(x.shape,paddings,constantValue);return this.compileAndRun(program,[x])}gather(x,indices,axis){const cpuRes=this.tryRunOnCpuOrThrow([x,indices],()=>this.cpuBackend.gather(x,indices,axis));if(cpuRes)return cpuRes;const program=new GatherProgram(x.shape,indices.size,axis);return this.compileAndRun(program,[x,indices])}batchToSpaceND(x,blockShape,crops){util_exports.assert(x.rank<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const prod5=blockShape.reduce((a,b)=>a*b),reshaped=backend_util_exports.getReshaped(x.shape,blockShape,prod5),permuted=backend_util_exports.getPermuted(reshaped.length,blockShape.length),reshapedPermuted=backend_util_exports.getReshapedPermuted(x.shape,blockShape,prod5),sliceBeginCoords=backend_util_exports.getSliceBeginCoords(crops,blockShape.length),sliceSize=backend_util_exports.getSliceSize(reshapedPermuted,crops,blockShape.length);return transpose(x.reshape(reshaped),permuted).reshape(reshapedPermuted).slice(sliceBeginCoords,sliceSize)}spaceToBatchND(x,blockShape,paddings){util_exports.assert(x.rank<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const prod5=blockShape.reduce((a,b)=>a*b),completePaddings=[[0,0]];completePaddings.push(...paddings);for(let i=1+blockShape.length;i<x.shape.length;++i)completePaddings.push([0,0]);const paddedX=x.pad(completePaddings),reshapedPaddedShape=backend_util_exports.getReshaped(paddedX.shape,blockShape,prod5,!1),permutedReshapedPaddedPermutation=backend_util_exports.getPermuted(reshapedPaddedShape.length,blockShape.length,!1),flattenShape=backend_util_exports.getReshapedPermuted(paddedX.shape,blockShape,prod5,!1),paddedXT=transpose(paddedX.reshape(reshapedPaddedShape),permutedReshapedPaddedPermutation);return reshape(paddedXT,flattenShape)}reduce(x,reduceType,dtype){const batchSize=x.shape[0],inSize=x.shape[1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),outSize=Math.ceil(inSize/windowSize),reduceInfo={windowSize,inSize,batchSize,outSize},program=new ReduceProgram(reduceInfo,reduceType),output=this.compileAndRun(program,[x],dtype);return output.shape[1]===1?output:this.reduce(output,reduceType,dtype)}argReduce(x,reduceType,bestIndicesA=null){let batchSize=x.shape[0],inSize=x.shape[1];bestIndicesA!=null&&(batchSize=bestIndicesA.shape[0],inSize=bestIndicesA.shape[1]);const windowSize=backend_util_exports.computeOptimalWindowSize(inSize),reduceInfo={windowSize,inSize,batchSize,outSize:Math.ceil(inSize/windowSize)},program=new ArgMinMaxProgram(reduceInfo,reduceType,bestIndicesA==null),inputs=[x];bestIndicesA!=null&&inputs.push(bestIndicesA);const output=this.compileAndRun(program,inputs,"int32");return output.shape[1]===1?output:this.argReduce(x,reduceType,output)}argReducePacked(x,reduceType,bestIndicesA=null){const inShape=bestIndicesA!=null?bestIndicesA.shape:x.shape,inSize=inShape[inShape.length-1],windowSize=backend_util_exports.computeOptimalWindowSize(inSize),program=new ArgMinMaxPackedProgram(inShape,windowSize,reduceType,bestIndicesA==null),inputs=bestIndicesA==null?[x]:[x,bestIndicesA],output=this.compileAndRun(program,inputs,"int32");return output.rank===x.rank?this.argReducePacked(x,reduceType,output):output}sum(x,axes){backend_util_exports.assertAxesAreInnerMostDims("sum",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"sum",outputDType).reshape(outShape)}prod(x,axes){const cpuRes=this.tryRunOnCpuOrThrow([x],()=>this.cpuBackend.prod(x,axes));if(cpuRes)return cpuRes;const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize),outputDType=sumOutType(x.dtype);return this.reduce(a2D,"prod",outputDType).reshape(outShape)}unsortedSegmentSum(x,segmentIds,numSegments){let axis=0;const permutation=backend_util_exports.getAxesPermutation([axis],x.rank);let permutedX=x;permutation!=null&&(permutedX=transpose(x,permutation),axis=backend_util_exports.getInnerMostAxes(1,x.rank)[0]);const outShape=segment_util2.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=permutedX.as2D(-1,inSize),outputDType=sumOutType(x.dtype);let result=this.segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments).reshape(outShape);return permutation!=null&&(result=transpose(result,backend_util_exports.getUndoAxesPermutation(permutation))),result}segOpCompute(x,segOpType,segmentIds,dtype,numSegments){const batchSize=x.shape[0],inSize=x.shape[1],windowSize=segment_util2.segOpComputeOptimalWindowSize(inSize,numSegments),segOpInfo={windowSize,inSize,batchSize,numSegments},program=new SegmentOpProgram(segOpInfo,segOpType),output=this.compileAndRun(program,[x,segmentIds],dtype);return output.shape[1]===numSegments?output:(segmentIds=range(0,numSegments).tile([inSize/windowSize]),this.segOpCompute(output,segOpType,segmentIds,dtype,numSegments))}argMinMaxReduce(x,axis,reduceType){const axes=[axis];if(backend_util_exports.assertAxesAreInnerMostDims("arg"+reduceType.charAt(0).toUpperCase()+reduceType.slice(1),axes,x.rank),!env().getBool("WEBGL_PACK_REDUCE")||x.rank<=2){const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.argReduce(a2D,reduceType).reshape(outShape)}return this.argReducePacked(x,reduceType)}argMin(x,axis){return this.argMinMaxReduce(x,axis,"min")}argMax(x,axis){return this.argMinMaxReduce(x,axis,"max")}cumsum(x,axis,exclusive,reverse12){if(axis!==x.rank-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${x.rank-1} but got axis=${axis}`);const size=x.shape[axis];let result=x;for(let i=0;i<=Math.ceil(Math.log2(size))-1;i++){const program=new CumSumProgram(x.shape,!1,reverse12),customSetup=program.getCustomSetupFunc(i),prevResult=result;result=this.compileAndRun(program,[result],result.dtype,customSetup),prevResult.dispose()}if(exclusive){const program=new CumSumProgram(x.shape,exclusive,reverse12),prevResult=result;result=this.compileAndRun(program,[result]),prevResult.dispose()}return result}equal(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,EQUAL2,"bool");const program=new BinaryOpProgram(EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}less(a,b){const cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.less(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS2,"bool");const program=new BinaryOpProgram(LESS,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}lessEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LESS_EQUAL2,"bool");const program=new BinaryOpProgram(LESS_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greater(a,b){const cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.greater(a,b));if(cpuRes)return cpuRes;if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER2,"bool");const program=new BinaryOpProgram(GREATER,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}greaterEqual(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,GREATER_EQUAL2,"bool");const program=new BinaryOpProgram(GREATER_EQUAL,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalNot(x){const program=new UnaryOpProgram(x.shape,LOGICAL_NOT);return this.compileAndRun(program,[x])}logicalAnd(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_AND2,"bool");const program=new BinaryOpProgram(LOGICAL_AND,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}logicalOr(a,b){if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,LOGICAL_OR2,"bool");const program=new BinaryOpProgram(LOGICAL_OR,a.shape,b.shape);return this.compileAndRun(program,[a,b],"bool")}select(condition,a,b){const program=new SelectProgram(condition.rank,a.shape,a.rank);return this.compileAndRun(program,[condition,a,b],upcastType(a.dtype,b.dtype))}where(condition){backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const condVals=condition.dataSync();return whereImpl3(condition.shape,condVals)}topk(x,k,sorted){const xVals=x.dataSync();return topkImpl3(xVals,x.shape,x.dtype,k,sorted)}min(x,axes){backend_util_exports.assertAxesAreInnerMostDims("min",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"min",a2D.dtype).reshape(outShape)}minimum(a,b){const cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.minimum(a,b));if(cpuRes)return cpuRes;const program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MIN2,a.shape,b.shape):new BinaryOpProgram(MIN,a.shape,b.shape);return this.compileAndRun(program,[a,b])}mod(a,b){const program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MOD2,a.shape,b.shape):new BinaryOpProgram(MOD,a.shape,b.shape);return this.compileAndRun(program,[a,b])}maximum(a,b){const cpuRes=this.tryRunOnCpuOrThrow([a,b],()=>this.cpuBackend.maximum(a,b));if(cpuRes)return cpuRes;const program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(MAX2,a.shape,b.shape):new BinaryOpProgram(MAX,a.shape,b.shape);return this.compileAndRun(program,[a,b])}all(x,axes){backend_util_exports.assertAxesAreInnerMostDims("all",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"all",a2D.dtype).reshape(outShape)}any(x,axes){backend_util_exports.assertAxesAreInnerMostDims("any",axes,x.rank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(x.shape,axes),inSize=util_exports.sizeFromShape(reduceShape),a2D=x.as2D(-1,inSize);return this.reduce(a2D,"any",a2D.dtype).reshape(outShape)}floorDiv(a,b){const op2=INT_DIV,outputDtype="int32";if(env().getBool("WEBGL_PACK_BINARY_OPERATIONS"))return this.packedBinaryOp(a,b,INT_DIV2,outputDtype);const program=new BinaryOpProgram(op2,a.shape,b.shape);return this.compileAndRun(program,[a,b],outputDtype)}packedUnaryOp(x,op2,dtype){const program=new UnaryOpPackedProgram(x.shape,op2);return this.compileAndRun(program,[x],dtype)}packedBinaryOp(a,b,op2,dtype,checkOutOfBounds=!1){const program=new BinaryOpPackedProgram(op2,a.shape,b.shape,checkOutOfBounds);return this.compileAndRun(program,[a,b],dtype)}makeComplexComponentTensorInfo(complexTensor,complexPart){return{dataId:complexPart.dataId,dtype:complexPart.dtype,shape:complexTensor.shape}}addN(tensors){if(tensors.length===1)return tensors[0];if(tensors.length>env().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const midIndex=Math.floor(tensors.length/2),leftSide=this.addN(tensors.slice(0,midIndex)),rightSide=this.addN(tensors.slice(midIndex));return this.addN([leftSide,rightSide])}const dtype=tensors.map(t=>t.dtype).reduce((d1,d2)=>upcastType(d1,d2)),shapes=tensors.map(t=>t.shape),usePackedOp=env().getBool("WEBGL_PACK"),program=usePackedOp?new AddNPackedProgram(tensors[0].shape,shapes):new AddNProgram(tensors[0].shape,shapes);return this.compileAndRun(program,tensors,dtype)}pow(a,b){const usePackedOp=env().getBool("WEBGL_PACK_BINARY_OPERATIONS"),program=usePackedOp?new BinaryOpPackedProgram(POW2,a.shape,b.shape):new BinaryOpProgram(POW,a.shape,b.shape),dtype=upcastType(a.dtype,b.dtype);return this.compileAndRun(program,[a,b],dtype)}ceil(x){if(this.shouldExecuteOnCPU([x])){const outValues=ceilImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,CEIL,x.dtype);const program=new UnaryOpProgram(x.shape,CEIL);return this.compileAndRun(program,[x])}floor(x){if(this.shouldExecuteOnCPU([x])){const outValues=floorImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,FLOOR,x.dtype);const program=new UnaryOpProgram(x.shape,FLOOR);return this.compileAndRun(program,[x])}sign(x){const program=new UnaryOpProgram(x.shape,SIGN);return this.compileAndRun(program,[x])}isNaN(x){const program=new UnaryOpProgram(x.shape,IS_NAN);return this.compileAndRun(program,[x],"bool")}isInf(x){const program=new UnaryOpProgram(x.shape,IS_INF);return this.compileAndRun(program,[x],"bool")}isFinite(x){const program=new UnaryOpProgram(x.shape,IS_FINITE);return this.compileAndRun(program,[x],"bool")}round(x){const program=new UnaryOpProgram(x.shape,ROUND);return this.compileAndRun(program,[x])}exp(x){if(this.shouldExecuteOnCPU([x])){const outValues=expImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXP,x.dtype);const program=new UnaryOpProgram(x.shape,EXP);return this.compileAndRun(program,[x])}expm1(x){if(this.shouldExecuteOnCPU([x])){const outValues=expm1ImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,EXPM1,x.dtype);const program=new UnaryOpProgram(x.shape,EXPM1);return this.compileAndRun(program,[x])}softmax(logits,dim){const axes=util_exports.parseAxisParam([dim],logits.shape),maxLogit=max(logits,axes),expandedShape=backend_util_exports.expandShapeToKeepDim(maxLogit.shape,axes),a=sub(logits,maxLogit.reshape(expandedShape)),b=this.exp(a),sumExp=this.sum(b,axes).reshape(expandedShape);return div(b,sumExp)}log(x){if(this.shouldExecuteOnCPU([x])){const outValues=logImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,LOG2,x.dtype);const program=new UnaryOpProgram(x.shape,LOG);return this.compileAndRun(program,[x])}log1p(x){const program=new UnaryOpProgram(x.shape,LOG1P);return this.compileAndRun(program,[x])}sqrt(x){const program=new UnaryOpProgram(x.shape,SQRT);return this.compileAndRun(program,[x])}rsqrt(x){if(this.shouldExecuteOnCPU([x])){const outValues=rsqrtImplCPU(this.texData.get(x.dataId).values,x.dtype);return this.makeOutput(x.shape,x.dtype,outValues)}const program=new UnaryOpProgram(x.shape,RSQRT);return this.compileAndRun(program,[x])}reciprocal(x){const program=new UnaryOpProgram(x.shape,RECIPROCAL);return this.compileAndRun(program,[x])}relu(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU2):program=new UnaryOpProgram(x.shape,RELU),this.compileAndRun(program,[x])}relu6(x){let program;return env().getBool("WEBGL_PACK")?program=new UnaryOpPackedProgram(x.shape,RELU62):program=new UnaryOpProgram(x.shape,RELU6),this.compileAndRun(program,[x])}prelu(x,alpha){const program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(PRELU2,x.shape,alpha.shape):new BinaryOpProgram(PRELU,x.shape,alpha.shape);return this.compileAndRun(program,[x,alpha])}elu(x){if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ELU3,x.dtype);const program=new UnaryOpProgram(x.shape,ELU2);return this.compileAndRun(program,[x])}eluDer(dy,y){const program=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new BinaryOpPackedProgram(ELU_DER2,dy.shape,y.shape):new BinaryOpProgram(ELU_DER,dy.shape,y.shape);return this.compileAndRun(program,[dy,y])}selu(x){const program=new UnaryOpProgram(x.shape,SELU);return this.compileAndRun(program,[x])}clip(x,min8,max10){let program;env().getBool("WEBGL_PACK_CLIP")?program=new ClipPackedProgram(x.shape):program=new ClipProgram(x.shape);const customSetup=program.getCustomSetupFunc(min8,max10);return this.compileAndRun(program,[x],null,customSetup)}abs(x){if(this.shouldExecuteOnCPU([x])&&x.dtype!=="complex64"){const outValues=simpleAbsImplCPU(this.texData.get(x.dataId).values);return this.makeOutput(x.shape,x.dtype,outValues)}if(env().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(x,ABS,x.dtype);const program=new UnaryOpProgram(x.shape,ABS);return this.compileAndRun(program,[x])}complexAbs(x){const xData=this.texData.get(x.dataId),program=new ComplexAbsProgram(x.shape),inputs=[this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.real),this.makeComplexComponentTensorInfo(x,xData.complexTensorInfos.imag)];return this.compileAndRun(program,inputs)}sigmoid(x){const program=new UnaryOpProgram(x.shape,SIGMOID);return this.compileAndRun(program,[x])}softplus(x){const program=new UnaryOpProgram(x.shape,SOFTPLUS);return this.compileAndRun(program,[x])}asin(x){const program=new UnaryOpProgram(x.shape,ASIN);return this.compileAndRun(program,[x])}acos(x){const program=new UnaryOpProgram(x.shape,ACOS);return this.compileAndRun(program,[x])}atan(x){const program=new UnaryOpProgram(x.shape,ATAN);return this.compileAndRun(program,[x])}sinh(x){const program=new UnaryOpProgram(x.shape,SINH);return this.compileAndRun(program,[x])}cosh(x){const program=new UnaryOpProgram(x.shape,COSH);return this.compileAndRun(program,[x])}tanh(x){const program=new UnaryOpProgram(x.shape,TANH);return this.compileAndRun(program,[x])}asinh(x){const program=new UnaryOpProgram(x.shape,ASINH);return this.compileAndRun(program,[x])}acosh(x){const program=new UnaryOpProgram(x.shape,ACOSH);return this.compileAndRun(program,[x])}atanh(x){const program=new UnaryOpProgram(x.shape,ATANH);return this.compileAndRun(program,[x])}erf(x){const program=new UnaryOpProgram(x.shape,ERF);return this.compileAndRun(program,[x])}step(x,alpha){const program=new UnaryOpProgram(x.shape,STEP(alpha));return this.compileAndRun(program,[x])}conv2dByMatMul(x,filter,convInfo,bias,activation2,preluActivationWeights){const xShape=x.shape,xTexData=this.texData.get(x.dataId),sharedMatMulDim=convInfo.inChannels,outerShapeX=xShape[0]*xShape[1]*xShape[2],outerShapeFilter=convInfo.outChannels,isChannelsLast=convInfo.dataFormat==="channelsLast",transposeA=!1,transposeB=!1,batchMatMulWillBeUnpacked=(outerShapeX===1||outerShapeFilter===1)&&sharedMatMulDim>MATMUL_SHARED_DIM_THRESHOLD,reshapeWillBeExpensive=xShape[2]%2!==0&&!!xTexData.isPacked;if(batchMatMulWillBeUnpacked||!env().getBool("WEBGL_LAZILY_UNPACK")||!env().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!reshapeWillBeExpensive){const targetShape2=isChannelsLast?xShape[0]*xShape[1]*xShape[2]:xShape[0]*xShape[2]*xShape[3],xReshaped2=reshape(x,[1,targetShape2,convInfo.inChannels]),filterReshaped2=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),result=this.fusedBatchMatMul({a:xReshaped2,b:filterReshaped2,transposeA,transposeB,bias,activation:activation2,preluActivationWeights});return reshape(result,convInfo.outShape)}const targetShape=isChannelsLast?xShape[0]*xShape[1]*(xShape[2]+1):xShape[0]*xShape[2]*(xShape[3]+1),xReshaped={dataId:x.dataId,shape:[1,targetShape,convInfo.inChannels],dtype:x.dtype},originalXTexDataShape=xTexData.shape;xTexData.shape=xTexData.shape.slice(),xTexData.shape[xTexData.shape.length-2]++,util_exports.assert(isReshapeFree(xTexData.shape,xReshaped.shape),()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);const filterReshaped=reshape(filter,[1,convInfo.inChannels,convInfo.outChannels]),pointwiseConv=this.fusedBatchMatMul({a:xReshaped,b:filterReshaped,transposeA,transposeB,bias,activation:activation2,preluActivationWeights}),pointwiseConvTexData=this.texData.get(pointwiseConv.dataId);return util_exports.assert(pointwiseConvTexData.isPacked,()=>"batchMatMul result is expected to be packed"),xTexData.shape=originalXTexDataShape,pointwiseConvTexData.shape=convInfo.outShape,engine15().makeTensorFromDataId(pointwiseConv.dataId,convInfo.outShape,pointwiseConv.dtype)}conv2dWithIm2Row(x,filter,convInfo,bias,activation2,preluActivationWeights){const{filterWidth,filterHeight,inChannels,outWidth,outHeight,dataFormat}=convInfo,isChannelsLast=dataFormat==="channelsLast",sharedDim=filterWidth*filterHeight*inChannels,numCols=outHeight*outWidth,x2ColShape=[sharedDim,numCols],transposeA=!0,transposeB=!1,xSqueezed=x.squeeze([0]),w2Row=filter.reshape([1,sharedDim,-1]),im2ColProgram=new Im2ColPackedProgram(x2ColShape,xSqueezed.shape,convInfo),im2Col=this.compileAndRun(im2ColProgram,[xSqueezed]).reshape([1,x2ColShape[0],x2ColShape[1]]),hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!0):null,matmulProgram=new MatMulPackedProgram(im2Col.shape,w2Row.shape,[1,numCols,convInfo.outChannels],transposeA,transposeB,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[im2Col,w2Row];bias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);const product=this.compileAndRun(matmulProgram,inputs);return isChannelsLast?product.reshape([1,outHeight,outWidth,convInfo.outChannels]):product.reshape([1,convInfo.outChannels,outHeight,outWidth])}fusedConv2d({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(input2,filter,convInfo,bias,activation2,preluActivationWeights);if(env().getBool("WEBGL_CONV_IM2COL")&&input2.shape[0]===1)return this.conv2dWithIm2Row(input2,filter,convInfo,bias,activation2,preluActivationWeights);const hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null,fusedActivation=activation2?mapActivationToShaderProgram(activation2,!1):null,program=new Conv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),inputs=[input2,filter];return bias&&inputs.push(bias),preluActivationWeights&&inputs.push(preluActivationWeights),this.compileAndRun(program,inputs)}conv2d(x,filter,convInfo){if(convInfo.filterHeight===1&&convInfo.filterWidth===1&&convInfo.dilationHeight===1&&convInfo.dilationWidth===1&&convInfo.strideHeight===1&&convInfo.strideWidth===1&&(convInfo.padInfo.type==="SAME"||convInfo.padInfo.type==="VALID"))return this.conv2dByMatMul(x,filter,convInfo);if(env().getBool("WEBGL_CONV_IM2COL")&&x.shape[0]===1)return this.conv2dWithIm2Row(x,filter,convInfo);const program=new Conv2DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv2dDerInput(dy,filter,convInfo){const program=new Conv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv2dDerFilter(x,dy,convInfo){const program=new Conv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}fusedDepthwiseConv2D({input:input2,filter,convInfo,bias,activation:activation2,preluActivationWeights}){const shouldPackDepthwiseConv=env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1,fusedActivation=activation2?mapActivationToShaderProgram(activation2,shouldPackDepthwiseConv):null,inputs=[input2,filter],hasBias=bias!=null,hasPreluActivationWeights=preluActivationWeights!=null;hasBias&&inputs.push(bias),hasPreluActivationWeights&&inputs.push(preluActivationWeights);let program;return shouldPackDepthwiseConv?(program=new DepthwiseConvPacked2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs)):(program=new DepthwiseConv2DProgram(convInfo,hasBias,fusedActivation,hasPreluActivationWeights),this.compileAndRun(program,inputs))}depthwiseConv2D(x,filter,convInfo){let program;return env().getBool("WEBGL_PACK_DEPTHWISECONV")&&convInfo.strideWidth<=2&&convInfo.outChannels/convInfo.inChannels===1?(program=new DepthwiseConvPacked2DProgram(convInfo),this.compileAndRun(program,[x,filter])):(program=new DepthwiseConv2DProgram(convInfo),this.compileAndRun(program,[x,filter]))}depthwiseConv2DDerInput(dy,filter,convInfo){const program=new DepthwiseConv2DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}depthwiseConv2DDerFilter(x,dy,convInfo){const program=new DepthwiseConv2DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}conv3d(x,filter,convInfo){const program=new Conv3DProgram(convInfo);return this.compileAndRun(program,[x,filter])}conv3dDerInput(dy,filter,convInfo){const program=new Conv3DDerInputProgram(convInfo);return this.compileAndRun(program,[dy,filter])}conv3dDerFilter(x,dy,convInfo){const program=new Conv3DDerFilterProgram(convInfo);return this.compileAndRun(program,[x,dy])}unstack(x,axis){const num=x.shape[axis],outShape=new Array(x.rank-1);let outIndex=0;for(let i=0;i<x.rank;i++)i!==axis&&(outShape[outIndex++]=x.shape[i]);const begin=new Array(x.rank).fill(0),size=x.shape.slice();size[axis]=1;const res=new Array(num);for(let i=0;i<res.length;i++)begin[axis]=i,res[i]=this.slice(x,begin,size).reshape(outShape);return res}avgPool3d(x,convInfo){const program=new Pool3DProgram(convInfo,"avg",!1);return this.compileAndRun(program,[x],"float32")}avgPool3dBackprop(dy,x,convInfo){const avgPool3dBackpropProgram=new AvgPool3DBackpropProgram(convInfo);return this.compileAndRun(avgPool3dBackpropProgram,[dy],x.dtype)}maxPool3d(x,convInfo){const program=new Pool3DProgram(convInfo,"max",!1);return this.compileAndRun(program,[x],"float32")}maxPool3dBackprop(dy,x,y,convInfo){const getPositions=!0,maxPool3dPositionsProgram=new Pool3DProgram(convInfo,"max",getPositions),maxPool3dPositions=this.compileAndRun(maxPool3dPositionsProgram,[x]),maxPool3dBackPropProgram=new MaxPool3DBackpropProgram(convInfo),result=this.compileAndRun(maxPool3dBackPropProgram,[dy,maxPool3dPositions],x.dtype);return maxPool3dPositions.dispose(),result}resizeBilinear(x,newHeight,newWidth,alignCorners){const program=env().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ResizeBilinearPackedProgram(x.shape,newHeight,newWidth,alignCorners):new ResizeBilinearProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x],"float32")}resizeBilinearBackprop(dy,x,alignCorners){const program=new ResizeBilinearBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}resizeNearestNeighbor(x,newHeight,newWidth,alignCorners){const program=new ResizeNearestNeighborProgram(x.shape,newHeight,newWidth,alignCorners);return this.compileAndRun(program,[x])}resizeNearestNeighborBackprop(dy,x,alignCorners){const program=new ResizeNearestNeigborBackpropProgram(dy,x,alignCorners);return this.compileAndRun(program,[dy])}multinomial(logits,normalized,numSamples,seed){const probs=normalized?logits:softmax(logits),batchSize=probs.shape[0],numOutcomes=probs.shape[1],program=new MultinomialProgram(batchSize,numOutcomes,numSamples),customSetup=program.getCustomSetupFunc(seed);return this.compileAndRun(program,[probs],"int32",customSetup)}oneHot(indices,depth,onValue,offValue){const program=new OneHotProgram(indices.size,depth,onValue,offValue);return this.compileAndRun(program,[indices])}diag(x){const program=new DiagProgram(x.size);return this.compileAndRun(program,[x])}cropAndResize(image3,boxes,boxIndex,cropSize,method,extrapolationValue){const program=new CropAndResizeProgram(image3.shape,boxes.shape,cropSize,method,extrapolationValue);return this.compileAndRun(program,[image3,boxes,boxIndex],"float32")}depthToSpace(x,blockSize,dataFormat){util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);const batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],program=new DepthToSpaceProgram(outputShape,blockSize,dataFormat);return this.compileAndRun(program,[x])}split(x,sizeSplits,axis){return split11(x,sizeSplits,axis)}scatterND(indices,updates,shape){const{sliceRank,numUpdates,sliceSize,strides,outputSize}=backend_util_exports.calculateShapes(updates,indices,shape),flattenShape=[outputSize/sliceSize,sliceSize],flattenIndices=indices.reshape([numUpdates,sliceRank]),flattenX=updates.reshape([numUpdates,sliceSize]);if(outputSize===0)return backend_util_exports.reshapeTensor(tensor4([]),shape);const defaultValue=scalar(0),program=new ScatterProgram(numUpdates,sliceRank,flattenIndices.rank,flattenX.rank,strides,flattenShape),res=this.compileAndRun(program,[flattenX,flattenIndices,defaultValue]);return res.reshape(shape)}sparseToDense(sparseIndices,sparseValues,outputShape,defaultValue){const{sliceRank,numUpdates,strides,outputSize}=backend_util_exports.calculateShapes(sparseValues,sparseIndices,outputShape),sumDupeIndices=!1,program=new ScatterProgram(numUpdates,sliceRank,sparseIndices.rank,sparseValues.rank,strides,[outputSize,1],sumDupeIndices),res=this.compileAndRun(program,[sparseValues,sparseIndices,defaultValue]);return res.reshape(outputShape)}gatherND(x,indices){const indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],[resultShape,numSlices,sliceSize,strides]=backend_util_exports.prepareAndValidate(x,indices),flattenIndices=indices.reshape([numSlices,sliceRank]),flattenX=x.reshape([x.size/sliceSize,sliceSize]),program=new GatherNDProgram(sliceRank,strides,[numSlices,sliceSize]),res=this.compileAndRun(program,[flattenX,flattenIndices]);return res.reshape(resultShape)}fill(shape,value,dtype){if(dtype=dtype||util_exports.inferDtype(value),dtype==="string"){const values=util_exports.getArrayFromDType(dtype,util_exports.sizeFromShape(shape));return values.fill(value),engine15().makeTensor(values,shape,dtype,this)}else{const program=new FillProgram(shape,value),customSetup=program.getCustomSetupFunc(value);return this.compileAndRun(program,[],dtype,customSetup)}}onesLike(x){if(x.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(x.shape,1,x.dtype)}zerosLike(x){return this.fill(x.shape,x.dtype==="string"?"":0,x.dtype)}linspace(start,stop,num){return backend_util_exports.linspaceImpl(start,stop,num)}makeTensorInfo(shape,dtype,values){const dataId=this.write(values,shape,dtype);return this.texData.get(dataId).usage=null,{dataId,shape,dtype}}makeOutput(shape,dtype,values){const{dataId}=this.makeTensorInfo(shape,dtype,values);return engine15().makeTensorFromDataId(dataId,shape,dtype,this)}unpackTensor(input2){const program=new UnpackProgram(input2.shape);return this.runWebGLProgram(program,[input2],input2.dtype)}packTensor(input2){const program=new PackProgram(input2.shape),preventEagerUnpackingOutput=!0;return this.runWebGLProgram(program,[input2],input2.dtype,null,preventEagerUnpackingOutput)}packedReshape(input2,afterShape){const input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=this.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}decode(dataId){const texData=this.texData.get(dataId),{isPacked,shape,dtype}=texData,shapeAs3D=getShapeAs3D(shape);let program;isPacked?program=new DecodeMatrixPackedProgram(shapeAs3D):program=new DecodeMatrixProgram(shapeAs3D);const preventEagerUnpackingOfOutput=!0,out=this.runWebGLProgram(program,[{shape:shapeAs3D,dtype,dataId}],dtype,null,preventEagerUnpackingOfOutput);return{dtype,shape,dataId:out.dataId}}runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){const output=this.makeTensorInfo(program.outputShape,outputDtype),outData=this.texData.get(output.dataId);if(program.packedOutput&&(outData.isPacked=!0),program.outPackingScheme===PackingScheme.DENSE){const texelShape=getDenseTexShape(program.outputShape);outData.texShape=texelShape.map(d=>d*2)}if(program.outTexUsage!=null&&(outData.usage=program.outTexUsage),util_exports.sizeFromShape(output.shape)===0)return outData.values=util_exports.getTypedArrayFromDType(output.dtype,0),output;const dataToDispose=[],inputsData=inputs.map(input2=>{if(input2.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let texData=this.texData.get(input2.dataId);if(texData.texture==null){if(!program.packedInputs&&util_exports.sizeFromShape(input2.shape)<=env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:input2.shape,texData:null,isUniform:!0,uniformValues:texData.values};program.packedInputs&&(texData.isPacked=!0,texData.shape=input2.shape)}else if(!!texData.isPacked!==!!program.packedInputs)input2=texData.isPacked?this.unpackTensor(input2):this.packTensor(input2),dataToDispose.push(input2),texData=this.texData.get(input2.dataId);else if(texData.isPacked&&!isReshapeFree(texData.shape,input2.shape)){const savedInput=input2,targetShape=input2.shape;input2.shape=texData.shape,input2=this.packedReshape(input2,targetShape),dataToDispose.push(input2),texData=this.texData.get(input2.dataId),savedInput.shape=targetShape}return this.uploadToGPU(input2.dataId),{shape:input2.shape,texData,isUniform:!1}});this.uploadToGPU(output.dataId);const outputData={shape:output.shape,texData:outData,isUniform:!1},key=makeShaderKey(program,inputsData,outputData),binary=this.getAndSaveBinary(key,()=>compileProgram(this.gpgpu,program,inputsData,outputData)),shouldTimeProgram=this.activeTimers!=null;let query;if(shouldTimeProgram&&(query=this.startTimer()),runProgram(this.gpgpu,binary,inputsData,outputData,customSetup),dataToDispose.forEach(info=>this.disposeIntermediateTensorInfo(info)),shouldTimeProgram&&(query=this.endTimer(query),this.activeTimers.push({name:program.constructor.name,query:this.getQueryTime(query)})),!env().getBool("WEBGL_LAZILY_UNPACK")&&outData.isPacked&&preventEagerUnpackingOfOutput===!1){const unpacked=this.unpackTensor(output);return this.disposeIntermediateTensorInfo(output),unpacked}return output}compileAndRun(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput=!1){outputDtype=outputDtype||inputs[0].dtype;const outInfo=this.runWebGLProgram(program,inputs,outputDtype,customSetup,preventEagerUnpackingOfOutput);return engine15().makeTensorFromDataId(outInfo.dataId,outInfo.shape,outInfo.dtype)}getAndSaveBinary(key,getBinary){return key in this.binaryCache||(this.binaryCache[key]=getBinary()),this.binaryCache[key]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!env().getBool("IS_TEST")){const allKeys=Object.keys(this.binaryCache);allKeys.forEach(key=>{this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram),delete this.binaryCache[key]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=tidy(()=>{if(!env().get("WEBGL_RENDER_FLOAT32_ENABLED")){const debugFlag=env().getBool("DEBUG");env().set("DEBUG",!1);const underflowCheckValue=this.abs(scalar(1e-8)).dataSync()[0];if(env().set("DEBUG",debugFlag),underflowCheckValue>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?EPSILON_FLOAT322:EPSILON_FLOAT162}uploadToGPU(dataId){const texData=this.texData.get(dataId),{shape,dtype,values,texture,usage,isPacked}=texData;if(texture!=null)return;const shouldTimeProgram=this.activeTimers!=null;let start;shouldTimeProgram&&(start=util_exports.now());let texShape=texData.texShape;if(texShape==null&&(texShape=getTextureShapeFromLogicalShape(shape,isPacked),texData.texShape=texShape),values!=null){const shapeAs3D=getShapeAs3D(shape);let program,width=texShape[1],height=texShape[0];const isByteArray=values instanceof Uint8Array;isPacked?([width,height]=getPackedMatrixTextureShapeWidthHeight(texShape[0],texShape[1]),program=new EncodeMatrixPackedProgram(shapeAs3D,[height,width],isByteArray)):program=new EncodeMatrixProgram(shapeAs3D,[height,width],isByteArray);const tempDenseInputHandle=this.makeTensorInfo([height,width],dtype);isByteArray?this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.PIXELS:this.texData.get(tempDenseInputHandle.dataId).usage=TextureUsage.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId),width,height,values);const preventEagerUnpacking=!0,encodedOutputTarget=this.runWebGLProgram(program,[tempDenseInputHandle],dtype,null,preventEagerUnpacking),outputTexData=this.texData.get(encodedOutputTarget.dataId);texData.texture=outputTexData.texture,texData.texShape=outputTexData.texShape,texData.isPacked=outputTexData.isPacked,texData.usage=outputTexData.usage,this.disposeIntermediateTensorInfo(tempDenseInputHandle),this.texData.delete(encodedOutputTarget.dataId),texData.values=null,shouldTimeProgram&&(this.uploadWaitMs+=util_exports.now()-start)}else{const newTexture=this.acquireTexture(texShape,usage,dtype,isPacked);texData.texture=newTexture}}convertAndCacheOnCPU(dataId,float32Values){const texData=this.texData.get(dataId),{dtype}=texData;return this.releaseGPUData(dataId),float32Values!=null&&(texData.values=float32ToTypedArray(float32Values,dtype)),texData.values}acquireTexture(texShape,texType,dtype,isPacked){if(this.numBytesInGPU+=this.computeBytes(texShape,dtype),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){const mb=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(texShape,texType,isPacked)}computeBytes(shape,dtype){return shape[0]*shape[1]*util_exports.bytesPerElement(dtype)}tryRunOnCpuOrThrow(inputs,fn){if(this.shouldExecuteOnCPU(inputs))try{return fn()}catch(e){if(env().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function float32ToTypedArray(a,dtype){if(dtype==="float32"||dtype==="complex64")return a;if(dtype==="int32"||dtype==="bool"){const result=dtype==="int32"?new Int32Array(a.length):new Uint8Array(a.length);for(let i=0;i<result.length;++i)result[i]=Math.round(a[i]);return result}else throw new Error(`Unknown dtype ${dtype}`)}const version12="2.7.0";device_util_exports.isBrowser()&&registerBackend("webgl",()=>new MathBackendWebGL,2);function identity3(args){const{inputs,backend:backend3}=args,{x}=inputs;return backend3.incRef(x.dataId),{dataId:x.dataId,shape:x.shape,dtype:x.dtype}}const identityConfig2={kernelName:Identity,backendName:"webgl",kernelFunc:identity3};function complex10(args){const{inputs,backend:backend3}=args,{real:real8,imag:imag8}=inputs,complexInfo=backend3.makeTensorInfo(real8.shape,"complex64"),complex11=backend3.texData.get(complexInfo.dataId),realTensorInfo=identity3({inputs:{x:real8},backend:backend3}),realData=backend3.texData.get(realTensorInfo.dataId);realData.complexParentRefCount++;const imagTensorInfo=identity3({inputs:{x:imag8},backend:backend3}),imagData=backend3.texData.get(imagTensorInfo.dataId);return imagData.complexParentRefCount++,complex11.complexTensorInfos={real:realTensorInfo,imag:imagTensorInfo},complexInfo}const complexConfig2={kernelName:Complex,backendName:"webgl",kernelFunc:complex10},CHECK_NAN_SNIPPET_UNARY="if (isnan(x)) return x;",CHECK_NAN_SNIPPET_BINARY=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,CHECK_NAN_SNIPPET_BINARY_PACKED=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function unaryKernelFunc2(opSnippet){return({inputs,backend:backend3})=>{const{x}=inputs,webglBackend=backend3,program=new UnaryOpProgram(x.shape,opSnippet);return webglBackend.runWebGLProgram(program,[x],x.dtype)}}function binaryKernelFunc2({opSnippet,packedOpSnippet,checkOutOfBounds=!1,supportsComplex=!1,cpuKernelImpl,dtype}){return({inputs,backend:backend3})=>{const{a,b}=inputs,webglBackend=backend3;if(supportsComplex&&a.dtype==="complex64"){const aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[real8,imag8]=[[aData.complexTensorInfos.real,bData.complexTensorInfos.real],[aData.complexTensorInfos.imag,bData.complexTensorInfos.imag]].map(complexParts=>{const[aPart,bPart]=complexParts,aHandle={dataId:aPart.dataId,dtype:aPart.dtype,shape:a.shape},bHandle={dataId:bPart.dataId,dtype:bPart.dtype,shape:b.shape},program2=new BinaryOpProgram(opSnippet,a.shape,b.shape);return webglBackend.runWebGLProgram(program2,[aHandle,bHandle],upcastType(aPart.dtype,bPart.dtype))}),complexOutput=complex10({inputs:{real:real8,imag:imag8},backend:webglBackend});return webglBackend.disposeIntermediateTensorInfo(real8),webglBackend.disposeIntermediateTensorInfo(imag8),complexOutput}const $dtype=dtype||upcastType(a.dtype,b.dtype);if(webglBackend.shouldExecuteOnCPU([a,b])&&cpuKernelImpl!=null){const aData=webglBackend.texData.get(a.dataId),bData=webglBackend.texData.get(b.dataId),[outValues,outShape]=cpuKernelImpl(a.shape,b.shape,aData.values,bData.values,$dtype),out=webglBackend.makeTensorInfo(outShape,$dtype),outData=webglBackend.texData.get(out.dataId);return outData.values=outValues,out}const shouldUsePackedProgram=env().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&packedOpSnippet!=null;let program;return shouldUsePackedProgram?program=new BinaryOpPackedProgram(packedOpSnippet,a.shape,b.shape,checkOutOfBounds):program=new BinaryOpProgram(opSnippet,a.shape,b.shape),webglBackend.runWebGLProgram(program,[a,b],$dtype)}}const ADD="return a + b;",addKernelFunc=binaryKernelFunc2({opSnippet:ADD,packedOpSnippet:ADD,supportsComplex:!0,cpuKernelImpl:addImplCPU}),addConfig2={kernelName:Add,backendName:"webgl",kernelFunc:addKernelFunc},ATAN2=CHECK_NAN_SNIPPET_BINARY+`
  return atan(a, b);
`,ATAN2_PACKED=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+CHECK_NAN_SNIPPET_BINARY_PACKED+`
  return result;
`,atan25=binaryKernelFunc2({opSnippet:ATAN2,packedOpSnippet:ATAN2_PACKED}),atan2Config={kernelName:Atan2,backendName:"webgl",kernelFunc:atan25};function avgPool3(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"avgPool");const{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});const avgPoolProgram=new Pool2DProgram(convInfo,"avg",!1);return backend3.runWebGLProgram(avgPoolProgram,[x],"float32")}const avgPoolConfig2={kernelName:AvgPool,backendName:"webgl",kernelFunc:avgPool3};function avgPoolBackprop3(args){const{inputs,backend:backend3,attrs}=args,{dy,input:input2}=inputs,x=input2;assertNotComplex2([dy,input2],"avgPoolBackprop");const{filterSize,strides,pad:pad11}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11),avgPoolBackpropProgram=new AvgPool2DBackpropProgram(convInfo);return backend3.runWebGLProgram(avgPoolBackpropProgram,[dy],x.dtype)}const avgPoolBackpropConfig2={kernelName:AvgPoolBackprop,backendName:"webgl",kernelFunc:avgPoolBackprop3};class BatchNormProgram{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.outputShape=[],this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="0.0";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="1.0";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${offsetSnippet};
        float scale = ${scaleSnippet};
        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}}class BatchNormPackedProgram{constructor(xShape,meanShape,varianceShape,offsetShape,scaleShape,varianceEpsilon){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],backend_util_exports.assertAndGetBroadcastShape(xShape,meanShape),backend_util_exports.assertAndGetBroadcastShape(xShape,varianceShape);let offsetSnippet="vec4(0.0)";offsetShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,offsetShape),this.variableNames.push("offset"),offsetSnippet="getOffsetAtOutCoords()");let scaleSnippet="vec4(1.0)";scaleShape!=null&&(backend_util_exports.assertAndGetBroadcastShape(xShape,scaleShape),this.variableNames.push("scale"),scaleSnippet="getScaleAtOutCoords()"),this.outputShape=xShape,this.userCode=`
      void main() {
        vec4 offset = ${offsetSnippet};
        vec4 scale = ${scaleSnippet};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));

        setOutput((x - mean) * inv + offset);
      }
    `}}const batchNorm3=({inputs,backend:backend3,attrs})=>{const{x,mean:mean7,variance,offset,scale:scale2}=inputs;util_exports.assert(mean7.shape.length===variance.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),util_exports.assert(offset==null||mean7.shape.length===offset.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),util_exports.assert(scale2==null||mean7.shape.length===scale2.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon}=attrs;varianceEpsilon==null&&(varianceEpsilon=.001);const finalInputs=[x,mean7,variance];let offsetShape=null;offset!=null&&(offsetShape=offset.shape,finalInputs.push(offset));let scaleShape=null;scale2!=null&&(scaleShape=scale2.shape,finalInputs.push(scale2));const program=env().getBool("WEBGL_PACK_NORMALIZATION")?new BatchNormPackedProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon):new BatchNormProgram(x.shape,mean7.shape,variance.shape,offsetShape,scaleShape,varianceEpsilon),output=backend3.runWebGLProgram(program,finalInputs,finalInputs[0].dtype);return output},batchNormConfig2={kernelName:FusedBatchNorm,backendName:"webgl",kernelFunc:batchNorm3},NOT_EQUAL="return float(a != b);",notEqual3=binaryKernelFunc2({opSnippet:NOT_EQUAL,dtype:"bool"}),notEqualConfig2={kernelName:NotEqual,backendName:"webgl",kernelFunc:notEqual3};function real7(args){const{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.real},backend:backend3})}const realConfig2={kernelName:Real,backendName:"webgl",kernelFunc:real7},TO_INT="return float(int(x));";function int(input2,backend3){const program=new UnaryOpProgram(input2.shape,TO_INT),output=backend3.runWebGLProgram(program,[input2],"int32");return{dataId:output.dataId,shape:output.shape,dtype:output.dtype}}function cast50(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{dtype}=attrs;if(dtype==="complex64"){if(x.dtype==="complex64")return identity3({inputs:{x},backend:backend3});const zerosTensor=zeros(x.shape),floatX=cast50({inputs:{x},backend:backend3,attrs:{dtype:"float32"}}),result=complex10({inputs:{real:floatX,imag:zerosTensor},backend:backend3});return zerosTensor.dispose(),backend3.disposeIntermediateTensorInfo(floatX),result}if(x.dtype==="complex64"){const realPart=real7({inputs:{input:x},backend:backend3}),result=cast50({inputs:{x:realPart},backend:backend3,attrs:{dtype}});return backend3.disposeIntermediateTensorInfo(realPart),result}if(!util_exports.hasEncodingLoss(x.dtype,dtype)){const result=identity3({inputs:{x},backend:backend3});return{dataId:result.dataId,shape:result.shape,dtype}}if(dtype==="int32")return int(x,backend3);if(dtype==="bool"){const zerosTensorInfo=backend3.makeTensorInfo([],"bool",util_exports.getTypedArrayFromDType("bool",1)),binaryInputs={a:x,b:zerosTensorInfo},result=notEqual3({inputs:binaryInputs,backend:backend3});return backend3.disposeIntermediateTensorInfo(zerosTensorInfo),result}throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`)}const castConfig2={kernelName:Cast,backendName:"webgl",kernelFunc:cast50};class ConcatProgram{constructor(shapes){this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,1),this.variableNames=shapes.map((_,i)=>`T${i}`);const offsets=new Array(shapes.length-1);offsets[0]=shapes[0][1];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][1];const snippets=[`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<offsets.length;i++){const shift=offsets[i-1];snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`)}const lastIndex=offsets.length,lastShift=offsets[offsets.length-1];snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${snippets.join(`
        `)}
      }
    `}}class ConcatPackedProgram{constructor(shapes,axis){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=backend_util_exports.computeOutShape(shapes,axis);const shape=this.outputShape,rank=shape.length,dtype=getCoordsDataType(rank),coords2=getChannels("coords",rank),channels=["x","y","z","w","u","v"].slice(0,rank);this.variableNames=shapes.map((_,i)=>`T${i}`);const offsets=new Array(shapes.length-1);offsets[0]=shapes[0][axis];for(let i=1;i<offsets.length;i++)offsets[i]=offsets[i-1]+shapes[i][axis];const channel=channels[axis],lastChannels=channels.slice(-2),allChannels=channels.join();let getValueSnippet=`if (${channel} < ${offsets[0]}) {
        return getChannel(
            getT0(${allChannels}), vec2(${lastChannels.join()}));
        }`;for(let i=1;i<offsets.length;i++){const shift2=offsets[i-1];getValueSnippet+=`
        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i-1]}) {
          return getChannel(
            getT${i}(${shiftedChannels(channels,channel,shift2)}),
            vec2(${shiftedChannels(lastChannels,channel,shift2)}));
        }`}const lastIndex=offsets.length,shift=offsets[offsets.length-1];getValueSnippet+=`
        return getChannel(
          getT${lastIndex}(${shiftedChannels(channels,channel,shift)}),
          vec2(${shiftedChannels(lastChannels,channel,shift)}));`,this.userCode=`
      float getValue(${channels.map(x=>"int "+x)}) {
        ${getValueSnippet}
      }

      void main() {
        ${dtype} coords = getOutputCoords();
        vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);

        ${coords2[rank-1]} = ${coords2[rank-1]} + 1;
        if (${coords2[rank-1]} < ${shape[rank-1]}) {
          result.g = getValue(${coords2});
        }

        ${coords2[rank-2]} = ${coords2[rank-2]} + 1;
        if (${coords2[rank-2]} < ${shape[rank-2]}) {
          result.a = getValue(${coords2});
        }

        ${coords2[rank-1]} = ${coords2[rank-1]} - 1;
        if (${coords2[rank-2]} < ${shape[rank-2]} &&
            ${coords2[rank-1]} < ${shape[rank-1]}) {
          result.b = getValue(${coords2});
        }
        setOutput(result);
      }
    `}}function shiftedChannels(channels,channel,shift){const channelIdx=channels.indexOf(channel),res=channels.map((c,idx)=>idx===channelIdx?`${c} - ${shift}`:c);return res.join()}function imag7(args){const{inputs,backend:backend3}=args,{input:input2}=inputs,inputData=backend3.texData.get(input2.dataId);return identity3({inputs:{x:inputData.complexTensorInfos.imag},backend:backend3})}const imagConfig2={kernelName:Imag,backendName:"webgl",kernelFunc:imag7};function packedReshape(input2,afterShape,backend3){const input3DShape=[getBatchDim(input2.shape),...getRowsCols(input2.shape)],input3D={dtype:input2.dtype,shape:input3DShape,dataId:input2.dataId},afterShapeAs3D=[getBatchDim(afterShape),...getRowsCols(afterShape)],program=new ReshapePackedProgram(afterShapeAs3D,input3DShape),preventEagerUnpackingOfOutput=!0,output=backend3.runWebGLProgram(program,[input3D],input2.dtype,null,preventEagerUnpackingOfOutput);return{dataId:output.dataId,shape:afterShape,dtype:output.dtype}}function reshape90(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{shape}=attrs,webglBackend=backend3,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize),$xSize=util_exports.sizeFromShape($shape);util_exports.assert(xSize===$xSize,()=>`The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);const xTexData=webglBackend.texData.get(x.dataId);return xTexData.isPacked&&!isReshapeFree(x.shape,$shape)&&!(xTexData.texture!==null&&isReshapeFree(xTexData.shape,$shape))?packedReshape(x,$shape,webglBackend):(webglBackend.incRef(x.dataId),{dataId:x.dataId,shape:$shape,dtype:x.dtype})}const reshapeConfig2={kernelName:Reshape,backendName:"webgl",kernelFunc:reshape90};function concatImpl(inputs,axis,backend3){const dtype=inputs[0].dtype;if(dtype==="complex64"){const reals=inputs.map(t=>real7({inputs:{input:t},backend:backend3})),imags=inputs.map(t=>imag7({inputs:{input:t},backend:backend3})),realConcated=concatImpl(reals,axis,backend3),imagConcated=concatImpl(imags,axis,backend3),result2=complex10({inputs:{real:realConcated,imag:imagConcated},backend:backend3});return reals.forEach(r=>backend3.disposeIntermediateTensorInfo(r)),imags.forEach(i=>backend3.disposeIntermediateTensorInfo(i)),backend3.disposeIntermediateTensorInfo(realConcated),backend3.disposeIntermediateTensorInfo(imagConcated),result2}if(inputs.length>env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const midIndex=Math.floor(inputs.length/2),leftSide=concatImpl(inputs.slice(0,midIndex),axis,backend3),rightSide=concatImpl(inputs.slice(midIndex),axis,backend3),result2=concatImpl([leftSide,rightSide],axis,backend3);return backend3.disposeIntermediateTensorInfo(leftSide),backend3.disposeIntermediateTensorInfo(rightSide),result2}if(env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&inputs[0].shape.length>1){const program2=new ConcatPackedProgram(inputs.map(t=>t.shape),axis);return backend3.runWebGLProgram(program2,inputs,dtype)}const outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),tensors2D=inputs.map(x=>reshape90({inputs:{x},attrs:{shape:[-1,util_exports.sizeFromShape(x.shape.slice(axis))]},backend:backend3})),program=new ConcatProgram(tensors2D.map(t=>t.shape)),result=backend3.runWebGLProgram(program,tensors2D,dtype);tensors2D.forEach(r=>backend3.disposeIntermediateTensorInfo(r));const reshapedResult=reshape90({inputs:{x:result},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(result),reshapedResult}function concat18(args){const{inputs,backend:backend3,attrs}=args,{axis}=attrs,$axis=util_exports.parseAxisParam(axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),$axis);if(util_exports.sizeFromShape(outShape)===0)return backend3.makeTensorInfo(outShape,inputs[0].dtype,[]);const $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];const shapes=$inputs.map(t=>t.shape);return backend_util_exports.assertParamsConsistent(shapes,$axis),concatImpl($inputs,$axis,backend3)}const concatConfig2={kernelName:Concat,backendName:"webgl",kernelFunc:concat18},COS=CHECK_NAN_SNIPPET_UNARY+`
  return cos(x);
`,cos7=unaryKernelFunc2(COS),cosConfig2={kernelName:Cos,backendName:"webgl",kernelFunc:cos7},DIV=`
if (a == b) {
  return 1.0;
};
return a / b;`,DIV_PACKED=`
  // vec4 one = vec4(equal(a, b));
  // return one + (vec4(1.0) - one) * a / b;
  vec4 result = a / b;
  if(a.x == b.x) {
    result.x = 1.;
  }
  if(a.y == b.y) {
    result.y = 1.;
  }
  if(a.z == b.z) {
    result.z = 1.;
  }
  if(a.w == b.w) {
    result.w = 1.;
  }

  return result;
`,div36=binaryKernelFunc2({opSnippet:DIV,packedOpSnippet:DIV_PACKED,checkOutOfBounds:!0}),divConfig2={kernelName:Div,backendName:"webgl",kernelFunc:div36};class FFTProgram{constructor(component,inputShape,inverse){this.variableNames=["real","imag"];const innerDim=inputShape[1];this.outputShape=inputShape;const exponentMultiplierSnippet=inverse?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,resultDenominator=inverse?`${innerDim}.0`:"1.0";let opString;if(component==="real")opString="return real * expR - imag * expI;";else if(component==="imag")opString="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);this.userCode=`
      const float exponentMultiplier = ${exponentMultiplierSnippet};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${opString}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${innerDim});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${innerDim}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}}function fftImpl2(x,inverse,backend3){const xData=backend3.texData.get(x.dataId),inputSize=util_exports.sizeFromShape(x.shape),innerDimensionSize=x.shape[x.shape.length-1],batch=inputSize/innerDimensionSize,input2D=reshape90({inputs:{x},backend:backend3,attrs:{shape:[batch,innerDimensionSize]}}),xShape=input2D.shape,realProgram=new FFTProgram("real",xShape,inverse),imagProgram=new FFTProgram("imag",xShape,inverse),inputs=[{dataId:xData.complexTensorInfos.real.dataId,dtype:xData.complexTensorInfos.real.dtype,shape:xShape},{dataId:xData.complexTensorInfos.imag.dataId,dtype:xData.complexTensorInfos.imag.dtype,shape:xShape}],realPart=backend3.runWebGLProgram(realProgram,inputs,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart);const complexOutputReshaped=reshape90({inputs:{x:complexOutput},backend:backend3,attrs:{shape:x.shape}});return backend3.disposeIntermediateTensorInfo(complexOutputReshaped),complexOutputReshaped}function fft7(args){const{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!1,backend3)}const fftConfig2={kernelName:FFT,backendName:"webgl",kernelFunc:fft7};class FlipLeftRightProgram{constructor(imageShape){this.variableNames=["Image"],this.outputShape=[];const imageWidth=imageShape[2];this.outputShape=imageShape,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${imageWidth} - x;
          float outputValue;
          if(coordX >= 0 && coordX < ${imageWidth}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}}const flipLeftRightConfig2={kernelName:FlipLeftRight,backendName:"webgl",kernelFunc:({inputs,backend:backend3})=>{const{image:image3}=inputs,webglBackend=backend3,program=new FlipLeftRightProgram(image3.shape),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}};class FromPixelsProgram{constructor(outputShape){this.variableNames=["A"];const glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);

        vec4 values = ${glsl.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}}class FromPixelsPackedProgram{constructor(outputShape){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const glsl=getGlslDifferences(),[height,width]=outputShape;this.outputShape=outputShape,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${width}.0, ${height}.0);
            vec4 values = ${glsl.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${glsl.output} = result;
      }
    `}}const fromPixelsConfig={kernelName:FromPixels,backendName:"webgl",kernelFunc:fromPixels2};let fromPixels2DContext2;function fromPixels2(args){const{inputs,backend:backend3,attrs}=args;let{pixels}=inputs;const{numChannels}=attrs,isVideo=typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement,isImage=typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement,[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],texShape=[height,width],outShape=[height,width,numChannels];(isImage||isVideo)&&(fromPixels2DContext2==null&&(fromPixels2DContext2=document.createElement("canvas").getContext("2d")),fromPixels2DContext2.canvas.width=width,fromPixels2DContext2.canvas.height=height,fromPixels2DContext2.drawImage(pixels,0,0,width,height),pixels=fromPixels2DContext2.canvas);const tempPixelHandle=backend3.makeTensorInfo(texShape,"int32");backend3.texData.get(tempPixelHandle.dataId).usage=TextureUsage.PIXELS,backend3.gpgpu.uploadPixelDataToTexture(backend3.getTexture(tempPixelHandle.dataId),pixels);const program=env().getBool("WEBGL_PACK")?new FromPixelsPackedProgram(outShape):new FromPixelsProgram(outShape),res=backend3.runWebGLProgram(program,[tempPixelHandle],"int32");return backend3.disposeData(tempPixelHandle.dataId),res}function ifft7(args){const{inputs,backend:backend3}=args,{input:input2}=inputs;return fftImpl2(input2,!0,backend3)}const ifftConfig2={kernelName:IFFT,backendName:"webgl",kernelFunc:ifft7};class MeanProgram{constructor(reduceInfo,divisor){this.variableNames=["x"];const{windowSize,batchSize,inSize,outSize}=reduceInfo;this.outputShape=[batchSize,outSize];const windowSizeNearestVec4=Math.floor(windowSize/4)*4,windowSizeVec4Remainder=windowSize%4;let updateSnippet="sumValue += dot(values, ones);";if(divisor!=null){const denominator=1/divisor;updateSnippet=`sumValue += dot(values * ${util_exports.isInt(denominator)?denominator.toPrecision(2):denominator}, ones);`}let checkOutOfBounds="";inSize%windowSize>0&&(checkOutOfBounds=`
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${checkOutOfBounds}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        float sumValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${updateSnippet}
        }
        setOutput(sumValue);
      }
    `}}function getReductionStages(inShape){const stages=[];for(;stages.length===0||stages[stages.length-1].outSize!==1;){const outSize=stages.length?stages[stages.length-1].outSize:inShape[1],windowSize=backend_util_exports.computeOptimalWindowSize(outSize);stages.push({inSize:outSize,windowSize,outSize:Math.ceil(outSize/windowSize)})}return stages}function reduce(x,dtype,reductionType,backend3){const reductionStages=getReductionStages(x.shape);let result=x;for(let i=0;i<reductionStages.length;i++){const{inSize,windowSize,outSize}=reductionStages[i];let program,previousResult;reductionType==="mean"?program=i===0?new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize},inSize):new MeanProgram({windowSize,inSize,batchSize:x.shape[0],outSize}):program=new ReduceProgram({windowSize,inSize,batchSize:x.shape[0],outSize},reductionType),previousResult=result,result=backend3.runWebGLProgram(program,[result],dtype),previousResult.dataId!==x.dataId&&backend3.disposeIntermediateTensorInfo(previousResult)}return result}function maxImpl2(x,reduceShape,outShape,backend3){const inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,x.dtype,"max",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}class TransposeProgram{constructor(aShape,newDim){this.variableNames=["A"];const outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];this.outputShape=outputShape,this.rank=outputShape.length;const dtype=getCoordsDataType(this.rank),switched=getSwitchedCoords(newDim);this.userCode=`
    void main() {
      ${dtype} resRC = getOutputCoords();
      setOutput(getA(${switched}));
    }
    `}}function getSwitchedCoords(newDim){const rank=newDim.length;if(rank>6)throw Error(`Transpose for rank ${rank} is not yet supported`);const originalOrder=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],switchedCoords=new Array(rank);for(let i=0;i<newDim.length;i++)switchedCoords[newDim[i]]=originalOrder[i];return switchedCoords.join()}class TransposePackedProgram{constructor(aShape,newDim){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const outputShape=new Array(aShape.length);for(let i=0;i<outputShape.length;i++)outputShape[i]=aShape[newDim[i]];if(this.outputShape=outputShape,this.rank=outputShape.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const dtype=getCoordsDataType(this.rank),outputOrder=getVecChannels("rc",this.rank),switchedOrder=new Array(this.rank);for(let i=0;i<newDim.length;i++)switchedOrder[newDim[i]]=outputOrder[i];const innerDims=`vec2(${switchedOrder.slice(-2).join()})`,nextColumn=`++${outputOrder[this.rank-1]} < ${outputShape[this.rank-1]}`,getc=`getChannel(getA(${switchedOrder.join()}), ${innerDims})`;this.userCode=`
    void main() {
      ${dtype} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${getc};
      if(${nextColumn}) {
        result[1] = ${getc};
      }
      --${outputOrder[this.rank-1]};
      if(++${outputOrder[this.rank-2]} < ${outputShape[this.rank-2]}) {
        result[2] = ${getc};
        if(${nextColumn}) {
          result[3] = ${getc};
        }
      }
      setOutput(result);
    }
    `}}function transposeImpl2(x,perm,backend3){const program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TransposePackedProgram(x.shape,perm):new TransposeProgram(x.shape,perm);return backend3.runWebGLProgram(program,[x],x.dtype)}const maxConfig2={kernelName:Max,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{const{x}=inputs,{reductionIndices,keepDims}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(reductionIndices,x.shape);let axes=origAxes;const permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),maxInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]);let maxInput=x;if(maxInputIsTransposed){if(shouldExecuteOnCPU){const xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];const maxInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);maxInput=webglBackend.makeTensorInfo(newShape,x.dtype);const maxInputData=webglBackend.texData.get(maxInput.dataId);maxInputData.values=maxInputValues}else maxInput=transposeImpl2(x,permutedAxes,webglBackend);axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("max",axes,xRank);const[maxOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(maxInput.shape,axes);let outShape=maxOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(maxOutShape,origAxes));let out;if(shouldExecuteOnCPU){const xTexData=webglBackend.texData.get(maxInput.dataId),values=xTexData.values,outValues=maxImplCPU(values,util_exports.sizeFromShape(reduceShape),outShape,x.dtype);out=webglBackend.makeTensorInfo(outShape,x.dtype);const outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=maxImpl2(maxInput,reduceShape,outShape,webglBackend);return maxInputIsTransposed&&webglBackend.disposeIntermediateTensorInfo(maxInput),out}};function maxPool3(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs;assertNotComplex2(x,"maxPool");const{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,dilations=1;util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11,dimRoundingMode);if(convInfo.filterWidth===1&&convInfo.filterHeight===1&&util_exports.arraysEqual(convInfo.inShape,convInfo.outShape))return identity3({inputs:{x},backend:backend3});const maxPoolProgram=new Pool2DProgram(convInfo,"max",!1);return backend3.runWebGLProgram(maxPoolProgram,[x],x.dtype)}const maxPoolConfig2={kernelName:MaxPool,backendName:"webgl",kernelFunc:maxPool3};function maxPoolBackprop3(args){const{inputs,backend:backend3,attrs}=args,{dy,input:input2,output}=inputs,x=input2;assertNotComplex2([input2,output],"maxPoolBackprop");const{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),getPositions=!0,maxPoolPositionsProgram=new Pool2DProgram(convInfo,"max",getPositions),maxPoolPositions2=backend3.runWebGLProgram(maxPoolPositionsProgram,[x],x.dtype),maxPoolBackPropProgram=new MaxPool2DBackpropProgram(convInfo),result=backend3.runWebGLProgram(maxPoolBackPropProgram,[dy,maxPoolPositions2],x.dtype);return backend3.disposeIntermediateTensorInfo(maxPoolPositions2),result}const maxPoolBackpropConfig2={kernelName:MaxPoolBackprop,backendName:"webgl",kernelFunc:maxPoolBackprop3};function maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,backend3){let program=new Pool2DProgram(convInfo,"max",!1);const poolOutput=backend3.runWebGLProgram(program,[x],"float32");program=new Pool2DProgram(convInfo,"max",!0,!0,includeBatchInIndex);const indexOutput=backend3.runWebGLProgram(program,[x],"float32");return[poolOutput,indexOutput]}const maxPoolWithArgmaxConfig2={kernelName:MaxPoolWithArgmax,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{const{x}=inputs,{filterSize,strides,pad:pad11,includeBatchInIndex}=attrs,webglBackend=backend3;util_exports.assert(x.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);const dilations=[1,1];util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);const convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,dilations,pad11),[result,indexes]=maxPoolWithArgmaxImpl2(x,includeBatchInIndex,convInfo,webglBackend);return[result,indexes]}};function meanImpl(x,reduceShape,outShape,backend3){const inSize=util_exports.sizeFromShape(reduceShape),xSize=util_exports.sizeFromShape(x.shape),batchSize=xSize/inSize,reshapedInput=reshape90({inputs:{x},attrs:{shape:[batchSize,inSize]},backend:backend3}),reduced=reduce(reshapedInput,"float32","mean",backend3),reshapedOutput=reshape90({inputs:{x:reduced},attrs:{shape:outShape},backend:backend3});return backend3.disposeIntermediateTensorInfo(reshapedInput),backend3.disposeIntermediateTensorInfo(reduced),reshapedOutput}const meanConfig={kernelName:Mean,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{const{x}=inputs,{keepDims,axis}=attrs,webglBackend=backend3,xRank=x.shape.length,origAxes=util_exports.parseAxisParam(axis,x.shape);let axes=origAxes;const permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),meanInputIsTransposed=permutedAxes!=null,shouldExecuteOnCPU=webglBackend.shouldExecuteOnCPU([x]),intermediates=[];let meanInput=x;if(meanInputIsTransposed){if(shouldExecuteOnCPU){const xTexData=webglBackend.texData.get(meanInput.dataId),values=xTexData.values,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[permutedAxes[i]];const meanInputValues=transposeImplCPU(values,x.shape,x.dtype,permutedAxes,newShape);meanInput=webglBackend.makeTensorInfo(newShape,x.dtype);const meanInputData=webglBackend.texData.get(meanInput.dataId);meanInputData.values=meanInputValues}else meanInput=transposeImpl2(x,permutedAxes,webglBackend);intermediates.push(meanInput),axes=backend_util_exports.getInnerMostAxes(axes.length,xRank)}backend_util_exports.assertAxesAreInnerMostDims("sum",axes,xRank);const[meanOutShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(meanInput.shape,axes);let outShape=meanOutShape;keepDims&&(outShape=backend_util_exports.expandShapeToKeepDim(meanOutShape,origAxes));const out=meanImpl(meanInput,reduceShape,outShape,webglBackend);for(const i of intermediates)webglBackend.disposeIntermediateTensorInfo(i);return out}};class MirrorPadProgram{constructor(xShape,paddings,mode){this.variableNames=["x"],this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);const rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),unpackedCoords=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,rank),offset=mode==="reflect"?0:1;if(rank===1){this.userCode=`
        int start = ${start};
        int end = ${end};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${offset};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${offset};
          }
          setOutput(getX(outC - start));
        }
      `;return}this.userCode=`
      ${dtype} start = ${dtype}(${start});
      ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outC = getOutputCoords();
        for (int i = 0; i < ${rank}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${offset};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
          }
        }
        ${dtype} coords = outC - start;
        setOutput(getX(${unpackedCoords}));
      }
    `}}class MirrorPadPackedProgram{constructor(xShape,paddings,mode){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=paddings.map((p2,i)=>p2[0]+xShape[i]+p2[1]);const rank=xShape.length,dtype=getCoordsDataType(rank),start=paddings.map(p2=>p2[0]).join(","),end=paddings.map((p2,i)=>p2[0]+xShape[i]).join(","),coords2=getChannels("rc",rank),source=getChannels("source",rank),cLimit=`${coords2[rank-1]} < ${this.outputShape[rank-1]}`,innerDims=rank===1?"source":`vec2(${source.slice(-2).join()})`,offset=mode==="reflect"?0:1;let mainLoop="";if(rank===1){const padSetup=`
        ${dtype} source = rc;
        if (source < start) {
          source = start * 2 - source - ${offset};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${offset};
        }
        source -= start;
      `;mainLoop=`
        ${dtype} rc = outputLoc;
        ${padSetup}
        result[0] = getChannel(getX(${source.join()}), ${innerDims});
        ${coords2[rank-1]} += 1;
        if(${cLimit}) {
          ${padSetup}
          result[1] = getChannel(getX(${source.join()}), ${innerDims});
        }
      `}else{const padSetup=`
        ${dtype} source = rc;
        ${dtype} lt = ${dtype}(lessThan(source, start));
        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
        ${dtype} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${offset}) +
                gte * ((end - 1) * 2 - source + ${offset});
        source -= start;
      `;mainLoop=`
        ${dtype} rc = outputLoc;
        ${padSetup}
        result[0] = getChannel(getX(${source.join()}), ${innerDims});
        ${coords2[rank-1]} += 1;
        if(${cLimit}) {
          ${padSetup}
          result[1] = getChannel(getX(${source.join()}), ${innerDims});
        }
        rc = outputLoc;
        ${coords2[rank-2]} += 1;
        if(${coords2[rank-2]} < ${this.outputShape[rank-2]}) {
          ${padSetup}
          result[2] = getChannel(getX(${source.join()}), ${innerDims});
          ${coords2[rank-1]} += 1;
          if(${cLimit}) {
            ${padSetup}
            result[3] = getChannel(getX(${source.join()}), ${innerDims});
          }
        }
      `}this.userCode=`
      const ${dtype} start = ${dtype}(${start});
      const ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${mainLoop}
        setOutput(result);
      }
    `}}const mirrorPadKernelFunc=({inputs,backend:backend3,attrs})=>{const{x}=inputs,{paddings,mode}=attrs,program=env().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new MirrorPadPackedProgram(x.shape,paddings,mode):new MirrorPadProgram(x.shape,paddings,mode),output=backend3.runWebGLProgram(program,[x],x.dtype);return output},mirrorPadConfig2={kernelName:MirrorPad,backendName:"webgl",kernelFunc:mirrorPadKernelFunc},COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class BinaryOpComplexProgram{constructor(op2,aShape,bShape){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=backend_util_exports.assertAndGetBroadcastShape(aShape,bShape),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${op2}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}}const MUL="return a * b;";function multiply3(args){const{inputs,backend:backend3}=args,{a,b}=inputs,dtype=backend_util_exports.upcastType(a.dtype,b.dtype);if(a.dtype==="complex64"){const aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),realProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,a.shape,b.shape),imagProgram=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,a.shape,b.shape),inputs2=[{dataId:aData.complexTensorInfos.real.dataId,dtype:aData.complexTensorInfos.real.dtype,shape:a.shape},{dataId:aData.complexTensorInfos.imag.dataId,dtype:aData.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:bData.complexTensorInfos.real.dataId,dtype:bData.complexTensorInfos.real.dtype,shape:b.shape},{dataId:bData.complexTensorInfos.imag.dataId,dtype:bData.complexTensorInfos.imag.dtype,shape:b.shape}],realPart=backend3.runWebGLProgram(realProgram,inputs2,"float32"),imagPart=backend3.runWebGLProgram(imagProgram,inputs2,"float32"),complexOutput=complex10({inputs:{real:realPart,imag:imagPart},backend:backend3});return backend3.disposeIntermediateTensorInfo(realPart),backend3.disposeIntermediateTensorInfo(imagPart),complexOutput}if(backend3.shouldExecuteOnCPU([a,b])){const aData=backend3.texData.get(a.dataId),bData=backend3.texData.get(b.dataId),[outValues,outShape]=multiplyImplCPU(a.shape,b.shape,aData.values,bData.values,dtype),out=backend3.makeTensorInfo(outShape,dtype),outData=backend3.texData.get(out.dataId);return outData.values=outValues,out}let program;return env().getBool("WEBGL_PACK_BINARY_OPERATIONS")?program=new BinaryOpPackedProgram(MUL,a.shape,b.shape):program=new BinaryOpProgram(MUL,a.shape,b.shape),backend3.runWebGLProgram(program,[a,b],dtype)}const multiplyConfig2={kernelName:Multiply,backendName:"webgl",kernelFunc:multiply3},nonMaxSuppressionV3Config={kernelName:NonMaxSuppressionV3,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold;return kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal)}},nonMaxSuppressionV4Impl3=kernel_impls_exports.nonMaxSuppressionV4Impl,nonMaxSuppressionV4Config2={kernelName:NonMaxSuppressionV4,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl3(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[selectedIndices,validOutputs]}},nonMaxSuppressionV5Impl3=kernel_impls_exports.nonMaxSuppressionV5Impl,nonMaxSuppressionV5Config2={kernelName:NonMaxSuppressionV5,backendName:"webgl",kernelFunc:({inputs,backend:backend3,attrs})=>{backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{boxes,scores}=inputs,{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=attrs,gpuBackend=backend3,boxesVals=gpuBackend.readSync(boxes.dataId),scoresVals=gpuBackend.readSync(scores.dataId),maxOutputSizeVal=maxOutputSize,iouThresholdVal=iouThreshold,scoreThresholdVal=scoreThreshold,softNmsSigmaVal=softNmsSigma,{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl3(boxesVals,scoresVals,maxOutputSizeVal,iouThresholdVal,scoreThresholdVal,softNmsSigmaVal);return[selectedIndices,selectedScores]}};class RotateProgram{constructor(imageShape,radians,fillValue,center){this.variableNames=["Image"],this.outputShape=[];const imageHeight=imageShape[1],imageWidth=imageShape[2],sinFactor=Math.sin(radians).toFixed(3),cosFactor=Math.cos(radians).toFixed(3);this.outputShape=imageShape;const[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),centerXString=centerX.toFixed(3),centerYString=centerY.toFixed(3);let fillSnippet="";typeof fillValue=="number"?fillSnippet=`float outputValue = ${fillValue.toFixed(2)};`:fillSnippet=`
        vec3 fill = vec3(${fillValue.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - ${centerXString}) * ${cosFactor} - (float(y) - ${centerYString}) * ${sinFactor};
          float coordYFloat = (float(x) - ${centerXString}) * ${sinFactor} + (float(y) - ${centerYString}) * ${cosFactor};
          int coordX = int(round(coordXFloat + ${centerXString}));
          int coordY = int(round(coordYFloat + ${centerYString}));
          ${fillSnippet}
          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}}const rotateWithOffsetConfig2={kernelName:RotateWithOffset,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{const{image:image3}=inputs,{radians,fillValue,center}=attrs,webglBackend=backend3,program=new RotateProgram(image3.shape,radians,fillValue,center),output=webglBackend.runWebGLProgram(program,[image3],image3.dtype);return output}},SIN=CHECK_NAN_SNIPPET_UNARY+`
  return sin(x);
`,sin6=unaryKernelFunc2(SIN),sinConfig2={kernelName:Sin,backendName:"webgl",kernelFunc:sin6},SQUARE="return x * x;",square25=unaryKernelFunc2(SQUARE),squareConfig2={kernelName:Square,backendName:"webgl",kernelFunc:square25},SQUARED_DIFFERENCE="return (a - b) * (a - b);",squaredDifference3=binaryKernelFunc2({opSnippet:SQUARED_DIFFERENCE,packedOpSnippet:SQUARED_DIFFERENCE}),squaredDifferenceConfig2={kernelName:SquaredDifference,backendName:"webgl",kernelFunc:squaredDifference3},SUB="return a - b;",subKernelFunc=binaryKernelFunc2({opSnippet:SUB,packedOpSnippet:SUB,supportsComplex:!0,cpuKernelImpl:subImplCPU}),subConfig2={kernelName:Sub,backendName:"webgl",kernelFunc:subKernelFunc},TAN="return tan(x);",tan5=unaryKernelFunc2(TAN),tanConfig2={kernelName:Tan,backendName:"webgl",kernelFunc:tan5},transposeConfig2={kernelName:Transpose,backendName:"webgl",kernelFunc:({inputs,attrs,backend:backend3})=>{const{x}=inputs,{perm}=attrs,webglBackend=backend3,xRank=x.shape.length,newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[perm[i]];let out;if(webglBackend.shouldExecuteOnCPU([x])){const xTexData=webglBackend.texData.get(x.dataId),values=xTexData.values,outValues=transposeImplCPU(values,x.shape,x.dtype,perm,newShape);out=webglBackend.makeTensorInfo(newShape,x.dtype);const outData=webglBackend.texData.get(out.dataId);outData.values=outValues}else out=transposeImpl2(x,perm,webglBackend);return out}};function unique7(args){const{inputs,attrs,backend:backend3}=args,{axis}=attrs,{x}=inputs;assertNotComplex2(x,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const values=backend3.readSync(x.dataId),{outputValues,outputShape,indices}=uniqueImplCPU(values,axis,x.shape,x.dtype);return[backend3.makeTensorInfo(outputShape,x.dtype,outputValues),backend3.makeTensorInfo([indices.length],"int32",indices)]}const uniqueConfig2={kernelName:Unique,backendName:"webgl",kernelFunc:unique7},kernelConfigs2=[addConfig2,atan2Config,avgPoolConfig2,avgPoolBackpropConfig2,batchNormConfig2,castConfig2,complexConfig2,concatConfig2,cosConfig2,divConfig2,fftConfig2,flipLeftRightConfig2,fromPixelsConfig,identityConfig2,ifftConfig2,imagConfig2,maxConfig2,maxPoolConfig2,maxPoolBackpropConfig2,maxPoolWithArgmaxConfig2,meanConfig,mirrorPadConfig2,multiplyConfig2,nonMaxSuppressionV3Config,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,realConfig2,reshapeConfig2,rotateWithOffsetConfig2,sinConfig2,squareConfig2,subConfig2,squaredDifferenceConfig2,tanConfig2,transposeConfig2,uniqueConfig2];for(const kernelConfig of kernelConfigs2)registerKernel(kernelConfig);const version14="2.7.0",version16={"tfjs-core":version,"tfjs-backend-cpu":version10,"tfjs-backend-webgl":version12,"tfjs-data":version8,"tfjs-layers":version2,"tfjs-converter":version6,tfjs:version14};var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu"})(FusableActivation||(FusableActivation={}));let wasmFusedMatMul;function setup(backend3){wasmFusedMatMul=backend3.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){const{inputs,backend:backend3,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");const{transposeA,transposeB,activation:activation2}=attrs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id;let biasId=0;if(bias!=null){const biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}const preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);const leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend3.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,outId),out}const fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){const{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc8(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}const absConfig2=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc8;function setupFunc2(backend3){wasmFunc8=backend3.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){const{backend:backend3,inputs}=args,{a,b}=inputs,aId=backend3.dataIdMap.get(a.dataId).id,bId=backend3.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend3.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;const aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend3.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc8(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;const aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:kernelFunc3}}const supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast);let wasmFunc;function setupFunc(backend3){wasmFunc=backend3.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){const{inputs,backend:backend3}=args,out=backend3.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;const inputIds=inputs.map(x=>backend3.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}const addNConfig={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity4(args){const{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}const identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity4};let wasmTranspose;function setup2(backend3){wasmTranspose=backend3.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose19(args){const{inputs,backend:backend3,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm);let permIsNoOp=!0;for(let i=0;i<perm.length;i++)perm[i]!==i&&(permIsNoOp=!1);const outShape=computeOutShape4(inputs.x.shape,attrs.perm),x={dataId:inputs.x.dataId,shape:reducedShape,dtype:inputs.x.dtype};if(permIsNoOp){const cloned=identity4({inputs,backend:backend3});return cloned.shape=outShape,cloned}const out=backend3.makeOutput(outShape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,permBytes=new Uint8Array(new Int32Array(perm).buffer),xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmTranspose(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],outId,permBytes,perm.length),out}function computeOutShape4(inShape,perm){const outShape=new Array(inShape.length);for(let i=0;i<outShape.length;i++)outShape[i]=inShape[perm[i]];return outShape}function removeOneSizeDims(shape,perm){const newShape=[],newPerm=[];for(let i=0;i<shape.length;++i)shape[i]!==1&&newShape.push(shape[i]),shape[perm[i]]!==1&&newPerm.push(perm[i]);for(let i=0;i<newPerm.length;++i){let minValIdx=-1;for(let j=0;j<newPerm.length;++j)newPerm[j]>=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}const transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose19,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend3){const xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape);let axes=originalAxes;const permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank);let xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){const newShape=new Array(xRank);for(let i=0;i<newShape.length;i++)newShape[i]=xShape[permutedAxes[i]];axes=backend_util_exports.getInnerMostAxes(axes.length,xRank),xTransposed=transpose19({inputs:{x},attrs:{perm:permutedAxes},backend:backend3});const xId=backend3.dataIdMap.get(x.dataId).id,transposedId=backend3.dataIdMap.get(xTransposed.dataId).id;transposedId!==xId&&(inputWasTransposed=!0)}return{transposed:xTransposed,originalAxes,axes,inputWasTransposed}}let wasmFunc2;function setup3(backend3){wasmFunc2=backend3.wasm.cwrap(ArgMax,null,["number","number","number","number","number"])}function argmax(args){const{backend:backend3,inputs,attrs}=args,{axis}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id;let inputId=xId,input2=x;const{transposed,axes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){const transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}const outShape=input2.shape.slice(0,-1),out=backend3.makeOutput(outShape,"int32"),outId=backend3.dataIdMap.get(out.dataId).id,outerSize=util_exports.sizeFromShape(out.shape),innerSize=input2.shape[axes[0]];return wasmFunc2(inputId,CppDType[input2.dtype],outerSize,innerSize,outId),inputWasTransposed&&backend3.disposeData(transposed.dataId),out}const argMaxConfig={kernelName:ArgMax,backendName:"wasm",kernelFunc:argmax,setupFunc:setup3};let wasmAvgPool;function setup4(backend3){wasmAvgPool=backend3.wasm.cwrap(AvgPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function avgPool4(args){const{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,channels=convInfo.inChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);if(convInfo.dilationWidth!==1||convInfo.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);const out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmAvgPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,strideHeight,strideWidth,channels,outId),out}const avgPoolConfig3={kernelName:AvgPool,backendName:"wasm",setupFunc:setup4,kernelFunc:avgPool4};function reshape91(args){const{inputs,attrs}=args,{x}=inputs,{shape}=attrs,xSize=util_exports.sizeFromShape(x.shape),$shape=util_exports.inferFromImplicitShape(shape,xSize);return util_exports.assert(xSize===util_exports.sizeFromShape($shape),()=>`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}const reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape91};let wasmBatchMatMul;function setup5(backend3){wasmBatchMatMul=backend3.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul2(args){const{inputs,backend:backend3,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");const aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);const outShapeOuterDims=batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2),outShape=outShapeOuterDims.concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);const a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape91({inputs:{x:a},backend:backend3,attrs:{shape:a3dShape}}),b3d=reshape91({inputs:{x:b},backend:backend3,attrs:{shape:b3dShape}}),a3dId=backend3.dataIdMap.get(a3d.dataId).id,b3dId=backend3.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend3.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend3.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}const batchMatMulConfig2={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul2};function cast51(args){const{inputs:{x},attrs:{dtype},backend:backend3}=args,out=backend3.makeOutput(x.shape,dtype),inVals=backend3.typedArrayFromHeap(x),outVals=backend3.typedArrayFromHeap(out);return outVals.set(inVals),out}const castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast51};let wasmClip;function setup6(backend3){wasmClip=backend3.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}const clipByValueConfig={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat19(args){const{inputs,backend:backend3}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),out=backend3.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;const $inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return $inputs[0];const shapes=$inputs.map(t=>t.shape);backend_util_exports.assertParamsConsistent(shapes,axis);const batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis));let sumInnerDims=0;const innerDims=$inputs.map(input2=>{const innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend3.typedArrayFromHeap(input2)),outVals=backend3.typedArrayFromHeap(out);for(let b=0;b<batchDim;b++){let outOffset=b*sumInnerDims;for(let i=0;i<inVals.length;i++){const innerDim=innerDims[i],inOffset=b*innerDim,vals=inVals[i].subarray(inOffset,inOffset+innerDim);outVals.set(vals,outOffset),outOffset+=innerDim}}return out}const concatConfig3={kernelName:Concat,backendName:"wasm",kernelFunc:concat19};let wasmConv2d;function setup7(backend3){wasmConv2d=backend3.wasm.cwrap(Conv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2d11(args){const{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode,dataFormat}=attrs,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);const out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}const conv2DConfig2={kernelName:Conv2D,backendName:"wasm",setupFunc:setup7,kernelFunc:conv2d11};let wasmConv2DBackpropInput;function setup8(backend3){wasmConv2DBackpropInput=backend3.wasm.cwrap(Conv2DBackpropInput,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function conv2DBackpropInput3(args){const{backend:backend3,inputs,attrs}=args,{dy,filter}=inputs,{strides,pad:pad11,dataFormat,dimRoundingMode,inputShape}=attrs,dilations=1,$dataFormat=backend_util_exports.convertConv2DDataFormat(dataFormat),convInfo=backend_util_exports.computeConv2DInfo(inputShape,filter.shape,strides,dilations,pad11,dimRoundingMode,!1,$dataFormat),{batchSize,filterHeight,filterWidth,inChannels,inHeight,inWidth,outChannels,outHeight,outWidth,strideHeight,strideWidth}=convInfo,topPad=filterHeight-1-convInfo.padInfo.top,leftPad=filterWidth-1-convInfo.padInfo.left,isChannelsLast=convInfo.dataFormat==="channelsLast",dxStrides=util_exports.computeStrides(convInfo.inShape),dyStrides=util_exports.computeStrides(dy.shape),[fltS0,fltS1,fltS2]=util_exports.computeStrides(filter.shape),xBatchStride=dxStrides[0],xRowStride=isChannelsLast?dxStrides[1]:dxStrides[2],xColStride=isChannelsLast?dxStrides[2]:1,xChannelStride=isChannelsLast?1:dxStrides[1],yBatchStride=dyStrides[0],yRowStride=isChannelsLast?dyStrides[1]:dyStrides[2],yColStride=isChannelsLast?dyStrides[2]:1,yChannelStride=isChannelsLast?1:dyStrides[1],out=backend3.makeOutput(convInfo.inShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,dyId=backend3.dataIdMap.get(dy.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id;return wasmConv2DBackpropInput(dyId,filterId,batchSize,filterHeight,filterWidth,inHeight,inWidth,inChannels,outHeight,outWidth,outChannels,strideHeight,strideWidth,topPad,leftPad,fltS0,fltS1,fltS2,xBatchStride,xRowStride,xColStride,xChannelStride,yBatchStride,yRowStride,yColStride,yChannelStride,outId),out}const conv2DBackpropInputConfig2={kernelName:Conv2DBackpropInput,backendName:"wasm",setupFunc:setup8,kernelFunc:conv2DBackpropInput3},cosConfig3=createUnaryKernelConfig(Cos);var InterpolationMethod;(function(InterpolationMethod2){InterpolationMethod2[InterpolationMethod2.bilinear=0]="bilinear",InterpolationMethod2[InterpolationMethod2.nearest=1]="nearest"})(InterpolationMethod||(InterpolationMethod={}));let wasmCropAndResize;function setup9(backend3){wasmCropAndResize=backend3.wasm.cwrap(CropAndResize,null,["number","number","number","number","array","number","number","number","number","number"])}function cropAndResize2(args){const{backend:backend3,inputs,attrs}=args,{method,extrapolationValue,cropSize}=attrs,{image:image3,boxes,boxInd}=inputs,numBoxes=boxes.shape[0],[cropHeight,cropWidth]=cropSize,outShape=[numBoxes,cropHeight,cropWidth,image3.shape[3]];let imagesData=backend3.dataIdMap.get(image3.dataId),castedData;image3.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:image3},attrs:{dtype:"float32"}}),imagesData=backend3.dataIdMap.get(castedData.dataId));const imagesId=imagesData.id,boxesId=backend3.dataIdMap.get(boxes.dataId).id,boxIndId=backend3.dataIdMap.get(boxInd.dataId).id,out=backend3.makeOutput(outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,imagesShapeBytes=new Uint8Array(new Int32Array(image3.shape).buffer);return wasmCropAndResize(imagesId,boxesId,boxIndId,numBoxes,imagesShapeBytes,cropHeight,cropWidth,InterpolationMethod[method],extrapolationValue,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}const cropAndResizeConfig={kernelName:CropAndResize,backendName:"wasm",setupFunc:setup9,kernelFunc:cropAndResize2};let wasmCumsum;function setup10(backend3){wasmCumsum=backend3.wasm.cwrap(Cumsum,null,["number","number","number","number","number","number"])}function cumsum6(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{axis,exclusive,reverse:reverse12}=attrs,xRank=x.shape.length;util_exports.assert(x.dtype==="float32"||x.dtype==="int32",()=>`cumsum does not support ${x.dtype} tensors in the WASM backend`);const permutation=backend_util_exports.getAxesPermutation([axis],xRank);let permutedX=x;permutation!==null&&(permutedX=transpose19({inputs:{x},attrs:{perm:permutation},backend:backend3}));const permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);const permutedOut=backend3.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend3.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend3.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse12?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){const undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose19({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend3}),backend3.disposeData(permutedX.dataId),backend3.disposeData(permutedOut.dataId)}return out}const cumsumConfig={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum6};let wasmDepthToSpace;function setup11(backend3){wasmDepthToSpace=backend3.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace2(args){const{backend:backend3,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);const batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend3.makeOutput(outputShape,"float32"),xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id,channelsLast=dataFormat==="NHWC"?1:0;return wasmDepthToSpace(xId,blockSize,channelsLast,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}const depthToSpaceConfig={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace2};let wasmDepthwiseConv2d;function setup12(backend3){wasmDepthwiseConv2d=backend3.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d5(args){const{inputs,attrs,backend:backend3}=args,{x,filter}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad11,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad11,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);const out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}const depthwiseConv2dNativeConfig2={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d5},supportsFullBroadcast2=!0,divConfig3=createBinaryKernelConfig(Div,supportsFullBroadcast2),supportsFullBroadcast3=!1,equalConfig=createBinaryKernelConfig(Equal,supportsFullBroadcast3,"bool"),expConfig2=createUnaryKernelConfig(Exp);function fill6(args){const{attrs:{shape,value,dtype},backend:backend3}=args,out=backend3.makeOutput(shape,dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(value),out}const fillConfig2={kernelName:Fill,backendName:"wasm",kernelFunc:fill6};let wasmFlipLeftRight;function setup13(backend3){wasmFlipLeftRight=backend3.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){const{inputs,backend:backend3}=args,{image:image3}=inputs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}const flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},supportsFullBroadcast4=!1,floorDivConfig=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast4);let wasmBatchNorm;function setup14(backend3){wasmBatchNorm=backend3.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){const{backend:backend3,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean7,variance,offset,scale:scale2}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,meanId=backend3.dataIdMap.get(mean7.dataId).id,varianceId=backend3.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend3.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend3.dataIdMap.get(scale2.dataId).id:0,out=backend3.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;const outId=backend3.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}const fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm};let wasmFusedConv2d;function setup15(backend3){wasmFusedConv2d=backend3.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d(args){const{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);const xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels;let biasId=0;if(bias!=null){const biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}const filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);const out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}const fusedConv2DConfig2={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d};let wasmFusedDepthwiseConv2d;function setup16(backend3){wasmFusedDepthwiseConv2d=backend3.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){const{inputs,attrs,backend:backend3}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad11,dilations,dataFormat,dimRoundingMode,activation:activation2}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad11,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);const xId=backend3.dataIdMap.get(x.dataId).id,filterId=backend3.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels;let biasId=0;if(bias!=null){const biasData=backend3.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}const filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);const out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend3.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,outId),out}const fusedDepthwiseConv2DConfig2={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d};let wasmGatherNd;function setup17(backend3){wasmGatherNd=backend3.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd(args){const{backend:backend3,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend3.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;const indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xData=backend3.dataIdMap.get(params.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}const gatherNdConfig={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd};let wasmGather;function setup18(backend3){wasmGather=backend3.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV2(args){const{backend:backend3,inputs,attrs}=args,{x,indices}=inputs,{axis}=attrs,newShape=x.shape.slice();newShape[axis]=util_exports.sizeFromShape(indices.shape);const stridesSize=x.shape.length-1,out=backend3.makeOutput(newShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;const xData=backend3.dataIdMap.get(x.dataId),xId=xData.id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,outId=backend3.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(newShape)).buffer);wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,axis,outStridesBytes,outId);const parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis);return out.shape=shapeInfo.outputShape,out}const gatherV2Config={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV2},supportsFullBroadcast5=!1,greaterConfig=createBinaryKernelConfig(Greater,supportsFullBroadcast5,"bool"),supportsFullBroadcast6=!1,greaterEqualConfig=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessConfig=createBinaryKernelConfig(Less,supportsFullBroadcast7,"bool"),supportsFullBroadcast8=!1,lessEqualConfig=createBinaryKernelConfig(LessEqual,supportsFullBroadcast8,"bool"),logConfig2=createUnaryKernelConfig(Log),supportsFullBroadcast9=!1,logicalAndConfig=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast9,"bool");let wasmMax;function setup19(backend3){wasmMax=backend3.wasm.cwrap(Max,null,["number, number, number"])}function max9(args){const{backend:backend3,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id;let inputId=xId,input2=x;const{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){const transposedId=backend3.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}const inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){const outId=backend3.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){const newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}const maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max9},supportsFullBroadcast10=!1,maximumConfig=createBinaryKernelConfig(Maximum,supportsFullBroadcast10);let wasmMaxPool;function setup20(backend3){wasmMaxPool=backend3.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){const{inputs,attrs,backend:backend3}=args,x=inputs.x,xId=backend3.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad11,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad11,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);const out=backend3.makeOutput(convInfo.outShape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}const maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4};let wasmMin;function setup21(backend3){wasmMin=backend3.wasm.cwrap(Min,null,["number, number, number"])}function min7(args){const{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id;let inputId=xId,input2=x;const{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);if(inputWasTransposed){const transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}const inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){const outId=backend3.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){const newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}const minConfig={kernelName:Min,backendName:"wasm",setupFunc:setup21,kernelFunc:min7},supportsFullBroadcast11=!1,minimumConfig=createBinaryKernelConfig(Minimum,supportsFullBroadcast11),supportsFullBroadcast12=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast12),negateConfig=createUnaryKernelConfig(Negate);function parseResultStruct(backend3,resOffset){const result=new Int32Array(backend3.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend3.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}let wasmFunc3;function setup22(backend3){wasmFunc3=backend3.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){const{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc3(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores),backend3.wasm._free(pValidOutputs);const selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices);return selectedIndicesTensor}const nonMaxSuppressionV3Config2={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup22,kernelFunc};let wasmFunc4;function setup23(backend3){wasmFunc4=backend3.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV4(args){const{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pSelectedScores);const selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend3.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}const nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup23,kernelFunc:nonMaxSuppressionV4};let wasmFunc5;function setup24(backend3){wasmFunc5=backend3.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){const{backend:backend3,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend3.dataIdMap.get(boxes.dataId).id,scoresId=backend3.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend3,resOffset);backend3.wasm._free(pValidOutputs);const selectedIndicesTensor=backend3.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend3.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}const nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup24,kernelFunc:kernelFunc2},supportsFullBroadcast13=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast13,"bool");let wasmOneHot;function setup25(backend3){wasmOneHot=backend3.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot2(args){const{inputs,backend:backend3,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend3.makeOutput([...indices.shape,depth],"int32"),outId=backend3.dataIdMap.get(out.dataId).id,indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}const oneHotConfig={kernelName:OneHot,backendName:"wasm",setupFunc:setup25,kernelFunc:oneHot2};function onesLike2(args){const{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(1),out}const onesLikeConfig={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike2};let wasmPadV2;function setup26(backend3){wasmPadV2=backend3.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad10(args){const{inputs:{x},backend:backend3,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(outShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}const padV2Config2={kernelName:PadV2,backendName:"wasm",kernelFunc:pad10,setupFunc:setup26},supportsFullBroadcast14=!1,powConfig=createBinaryKernelConfig(Pow,supportsFullBroadcast14);let wasmPrelu;function setup27(backend3){wasmPrelu=backend3.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu8(args){const{inputs,backend:backend3}=args,{x,alpha}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,weightsId=backend3.dataIdMap.get(alpha.dataId).id,out=backend3.makeOutput(x.shape,"float32"),outId=backend3.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}const preluConfig2={kernelName:Prelu,backendName:"wasm",setupFunc:setup27,kernelFunc:prelu8},reluConfig2=createUnaryKernelConfig(Relu),relu6Config2=createUnaryKernelConfig(Relu6);let wasmResizeBilinear;function setup28(backend3){wasmResizeBilinear=backend3.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number"])}function resizeBilinear2(args){const{backend:backend3,inputs,attrs}=args,{images}=inputs,{alignCorners,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels];let xData=backend3.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast51({backend:backend3,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend3.dataIdMap.get(castedData.dataId));const xId=xData.id,out=backend3.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;const outId=backend3.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,outId),castedData!=null&&backend3.disposeData(castedData.dataId),out}const resizeBilinearConfig={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup28,kernelFunc:resizeBilinear2};let wasmReverse;function setup29(backend3){wasmReverse=backend3.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse11(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity4({inputs:{x},backend:backend3});const out=backend3.makeOutput(x.shape,x.dtype),xId=backend3.dataIdMap.get(x.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape91({inputs:{x:out},attrs:{shape:x.shape},backend:backend3})}const reverseConfig={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse11,setupFunc:setup29};let wasmRotate;function setup30(backend3){wasmRotate=backend3.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){const{inputs,backend:backend3,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend3.makeOutput(image3.shape,image3.dtype),imageId=backend3.dataIdMap.get(image3.dataId).id,outId=backend3.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}const rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup30},rsqrtConfig2=createUnaryKernelConfig(Rsqrt);let wasmScatterNd;function setup31(backend3){wasmScatterNd=backend3.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd(args){const{backend:backend3,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend3.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;const{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesData=backend3.dataIdMap.get(indices.dataId),indicesId=indicesData.id,updatesData=backend3.dataIdMap.get(updates.dataId),updatesId=updatesData.id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend3.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}const scatterNdConfig={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup31,kernelFunc:scatterNd};let wasmSelect;function setup32(backend3){wasmSelect=backend3.wasm.cwrap(SelectV2,null,["number","number","number","number","number"])}function select(args){const{inputs,backend:backend3}=args,{condition,t,e}=inputs,conditionId=backend3.dataIdMap.get(condition.dataId).id,tId=backend3.dataIdMap.get(t.dataId).id,eId=backend3.dataIdMap.get(e.dataId).id,out=backend3.makeOutput(t.shape,t.dtype),outId=backend3.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}const selectV2Config={kernelName:SelectV2,backendName:"wasm",kernelFunc:select,setupFunc:setup32};let wasmFunc6;function setup33(backend3){wasmFunc6=backend3.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid8(args){const{backend:backend3,inputs:{x}}=args,xId=backend3.dataIdMap.get(x.dataId).id,out=backend3.makeOutput(x.shape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc6(xId,outId),out}const sigmoidConfig2={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup33,kernelFunc:sigmoid8},sinConfig3=createUnaryKernelConfig(Sin);function slice20(args){const{inputs:{x},attrs:{begin,size},backend:backend3}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend3.typedArrayFromHeap(x),out=backend3.makeOutput(size_,x.dtype),outVals=backend3.typedArrayFromHeap(out),xStrides=util_exports.computeStrides(x.shape);if(isContinous){const flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return outVals.set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}const rank=x.shape.length;return rank===2?slice2d3(xVals,xStrides[0],outVals,begin_,size_):rank===3?slice3d3(xVals,xStrides[0],xStrides[1],outVals,begin_,size_):rank===4?slice4d3(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_):genericSliceSlow(xVals,x,outVals,begin_,size_),out}function slice2d3(xVals,xStride,outVals,begin,size){let outOffset=0;const beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i<endI;i++){const xOffset=i*xStride+beginJ;outVals.set(xVals.subarray(xOffset,xOffset+size[1]),outOffset),outOffset+=size[1]}}function slice3d3(xVals,xStride1,xStride2,outVals,begin,size){let outOffset=0;const beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++){const xOffset=i*xStride1+j*xStride2+beginK;outVals.set(xVals.subarray(xOffset,xOffset+size[2]),outOffset),outOffset+=size[2]}}function slice4d3(xVals,xStride1,xStride2,xStride3,outVals,begin,size){let outOffset=0;const beginI=begin[0],beginJ=begin[1],beginK=begin[2],endI=beginI+size[0],endJ=beginJ+size[1],endK=beginK+size[2],beginL=begin[3];for(let i=beginI;i<endI;i++)for(let j=beginJ;j<endJ;j++)for(let k=beginK;k<endK;k++){const xOffset=i*xStride1+j*xStride2+k*xStride3+beginL;outVals.set(xVals.subarray(xOffset,xOffset+size[3]),outOffset),outOffset+=size[3]}}function genericSliceSlow(xVals,xInfo,outVals,begin,size){const outBuf=buffer(size,xInfo.dtype,outVals),xBuf=buffer(xInfo.shape,xInfo.dtype,xVals);for(let i=0;i<outBuf.size;++i){const loc=outBuf.indexToLoc(i),xLoc=loc.map((idx,j)=>idx+begin[j]);outVals[i]=xBuf.get(...xLoc)}}const sliceConfig2={kernelName:Slice,backendName:"wasm",kernelFunc:slice20};let wasmFunc7;function setup34(backend3){wasmFunc7=backend3.wasm.cwrap(Softmax,null,["number","number","number","number"])}function softmax5(args){const{backend:backend3,inputs:{logits},attrs:{dim}}=args,xId=backend3.dataIdMap.get(logits.dataId).id,out=backend3.makeOutput(logits.shape,logits.dtype),outId=backend3.dataIdMap.get(out.dataId).id,channels=logits.shape[dim],batch=util_exports.sizeFromShape(logits.shape)/channels;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId,channels,batch),out}const softmaxConfig={kernelName:Softmax,backendName:"wasm",setupFunc:setup34,kernelFunc:softmax5};function split12(args){const{inputs,attrs,backend:backend3}=args,{x}=inputs,{numOrSizeSplits,axis}=attrs,$axis=util_exports.parseAxisParam(axis,x.shape)[0],splitSizes=backend_util_exports.prepareSplitSize(x,numOrSizeSplits,axis),begin=new Array(x.shape.length).fill(0),size=x.shape.slice();return splitSizes.map(s=>{const xSliceSize=[...size];xSliceSize[$axis]=s;const xSlice=slice20({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend3});return begin[$axis]+=s,xSlice})}const splitVConfig={kernelName:SplitV,backendName:"wasm",kernelFunc:split12},sqrtConfig2=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15);let wasmStridedSlice;function setup35(backend3){wasmStridedSlice=backend3.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice2(args){const{backend:backend3,inputs,attrs}=args,{x}=inputs;let{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));const{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");const numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});const xReshaped=reshape91({inputs:{x},attrs:{shape:newShape},backend:backend3}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;const shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});const size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1),nonStrided=strides.every(v=>v===1);if(nonStrided){const xSliced=slice20({inputs:{x},attrs:{begin,size},backend:backend3});return reshape91({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend3})}const out=backend3.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){const xId=backend3.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend3.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape91({inputs:{x:out},attrs:{shape:outShape},backend:backend3})}const stridedSliceConfig={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup35,kernelFunc:stridedSlice2},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16);let wasmSum;function setup36(backend3){wasmSum=backend3.wasm.cwrap(Sum,null,["number, number, number"])}function sum28(args){const{backend:backend3,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id;let inputId=xId,input2=x;const{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend3);let reductionAxes=axes;if(inputWasTransposed){const transposedId=backend3.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);const[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend3.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){const outId=backend3.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend3.disposeData(transposed.dataId),keepDims){const newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}const sumConfig={kernelName:Sum,backendName:"wasm",setupFunc:setup36,kernelFunc:sum28},tanhConfig2=createUnaryKernelConfig(Tanh);let wasmTile;function setup37(backend3){wasmTile=backend3.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile11(args){const{inputs,backend:backend3,attrs}=args,{x}=inputs,xId=backend3.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i<newShape.length;i++)newShape[i]=x.shape[i]*reps[i];const xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),newShapeBytes=new Uint8Array(new Int32Array(newShape).buffer),out=backend3.makeOutput(newShape,x.dtype),outId=backend3.dataIdMap.get(out.dataId).id;return wasmTile(xId,xShapeBytes,x.shape.length,newShapeBytes,newShape.length,CppDType[out.dtype],outId),out}const tileConfig={kernelName:Tile,backendName:"wasm",setupFunc:setup37,kernelFunc:tile11};function unpack(args){const{inputs,backend:backend3,attrs}=args,{value}=inputs,{axis}=attrs,numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1);let outIndex=0;for(let i=0;i<rank;i++)i!==axis&&(outShape[outIndex++]=value.shape[i]);const outs=new Array(numOutputs),begin=new Array(rank).fill(0),size=value.shape.slice();size[axis]=1;for(let i=0;i<outs.length;i++)begin[axis]=i,outs[i]=slice20({inputs:{x:value},attrs:{begin,size},backend:backend3});return outs.map(({dataId,dtype})=>({dataId,dtype,shape:outShape}))}const unpackConfig={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack};function zerosLike2(args){const{inputs:{x},backend:backend3}=args,out=backend3.makeOutput(x.shape,x.dtype),outVals=backend3.typedArrayFromHeap(out);return outVals.fill(0),out}const zerosLikeConfig={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike2},kernelConfigs3=[absConfig2,addConfig3,addNConfig,argMaxConfig,avgPoolConfig3,batchMatMulConfig2,castConfig3,clipByValueConfig,concatConfig3,conv2DConfig2,conv2DBackpropInputConfig2,cosConfig3,cropAndResizeConfig,cumsumConfig,depthToSpaceConfig,depthwiseConv2dNativeConfig2,divConfig3,equalConfig,expConfig2,fillConfig2,flipLeftRightConfig3,floorDivConfig,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig,gatherV2Config,greaterConfig,greaterEqualConfig,identityConfig3,lessConfig,lessEqualConfig,logConfig2,logicalAndConfig,maxConfig3,maximumConfig,maxPoolConfig3,minConfig,minimumConfig,multiplyConfig3,negateConfig,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig,onesLikeConfig,padV2Config2,powConfig,preluConfig2,reluConfig2,relu6Config2,reshapeConfig3,resizeBilinearConfig,reverseConfig,rotateWithOffsetConfig3,rsqrtConfig2,scatterNdConfig,selectV2Config,sigmoidConfig2,sinConfig3,sliceConfig2,softmaxConfig,splitVConfig,sqrtConfig2,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig,subConfig3,sumConfig,tanhConfig2,tileConfig,transposeConfig3,unpackConfig,zerosLikeConfig];for(const kernelConfig of kernelConfigs3)registerKernel(kernelConfig);const ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});const tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),WASM_PRIORITY=2;class BackendWasm extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine15())}write(values,shape,dtype){const dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){const start=util_exports.now();f();const kernelMs=util_exports.now()-start;return{kernelMs}}move(dataId,values,shape,dtype){const id=this.dataIdNextNumber++;if(dtype==="string"){const stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}const size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){const{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;const bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){const data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};const id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});const size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){const buffer11=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer11,memoryOffset,size);case"int32":return new Int32Array(buffer11,memoryOffset,size);case"bool":return new Uint8Array(buffer11,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}}registerBackend("wasm",async()=>{const{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){const[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{const factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){const response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var _scriptDir = undefined; var WasmBackendModuleThreadedSimd = "+tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=tfjs_backend_wasm.default(factoryConfig);const voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{if(initialized)return;if(initAborted)return;initAborted=!0;const rejectMsg="Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";reject({message:rejectMsg})}})}function typedArrayFromBuffer(buffer11,dtype){switch(dtype){case"float32":return new Float32Array(buffer11);case"int32":return new Int32Array(buffer11);case"bool":return new Uint8Array(buffer11);default:throw new Error(`Unknown dtype ${dtype}`)}}const wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"];let wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;const missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}const version17="2.7.0";const facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){const h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){const startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>{const scaledCoord=[coord[0]*factor[0],coord[1]*factor[1]];return scaledCoord});return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){const center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){const centers=getBoxCenter(box),size=getBoxSize(box),maxEdge=Math.max(...size),halfSize=maxEdge/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function shiftBox(box,shiftFactor){const boxSize=[box.endPoint[0]-box.startPoint[0],box.endPoint[1]-box.startPoint[1]],shiftVector=[boxSize[0]*shiftFactor[0],boxSize[1]*shiftFactor[1]],startPoint=[box.startPoint[0]+shiftVector[0],box.startPoint[1]+shiftVector[1]],endPoint=[box.endPoint[0]+shiftVector[0],box.endPoint[1]+shiftVector[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){const radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}const buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot2(v1,v2){let product=0;for(let i=0;i<v1.length;i++)product+=v1[i]*v2[i];return product}function getColumnFrom2DArr(arr,columnIndex){const column=[];for(let i=0;i<arr.length;i++)column.push(arr[i][columnIndex]);return column}function multiplyTransformMatrices(mat1,mat2){const product=[],size=mat1.length;for(let row=0;row<size;row++){product.push([]);for(let col=0;col<size;col++)product[row].push(dot2(mat1[row],getColumnFrom2DArr(mat2,col)))}return product}function buildRotationMatrix(rotation,center){const cosA=Math.cos(rotation),sinA=Math.sin(rotation),rotationMatrix=[[cosA,-sinA,0],[sinA,cosA,0],[0,0,1]],translationMatrix=buildTranslationMatrix(center[0],center[1]),translationTimesRotation=multiplyTransformMatrices(translationMatrix,rotationMatrix),negativeTranslationMatrix=buildTranslationMatrix(-center[0],-center[1]);return multiplyTransformMatrices(translationTimesRotation,negativeTranslationMatrix)}function invertTransformMatrix(matrix){const rotationComponent=[[matrix[0][0],matrix[1][0]],[matrix[0][1],matrix[1][1]]],translationComponent=[matrix[0][2],matrix[1][2]],invertedTranslation=[-dot2(rotationComponent[0],translationComponent),-dot2(rotationComponent[1],translationComponent)];return[rotationComponent[0].concat(invertedTranslation[0]),rotationComponent[1].concat(invertedTranslation[1]),[0,0,1]]}function rotatePoint(homogeneousCoordinate,rotationMatrix){return[dot2(homogeneousCoordinate,rotationMatrix[0]),dot2(homogeneousCoordinate,rotationMatrix[1])]}const handpose=__toModule(require_handpose()),gesture=__toModule(require_gesture()),image2=__toModule(require_image()),profile2=__toModule(require_profile());var config_default={backend:"webgl",wasmPath:"../assets/",console:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:15,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:15},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:15},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:15,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.8,nmsRadius:20},hand:{enabled:!0,inputSize:256,skipFrames:15,minConfidence:.5,iouThreshold:.1,scoreThreshold:.8,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var version3="0.9.7";const now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){const isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{const pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}class Human{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version3,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}log(...msg){msg&&this.config.console&&console.log("Human:",...msg)}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;const current=engine15().state.numTensors,previous=this.numTensors;this.numTensors=current;const leaked=current-previous;leaked!==0&&this.log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";const timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(this.log(`version: ${this.version} TensorFlow/JS version: ${version}`),this.checkBackend(!0),this.log("configuration:",this.config),this.log("flags:",ENV.flags),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));const current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){const timeStamp=now2();if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){if(this.state="backend",this.log("setting backend:",this.config.backend),this.config.backend==="wasm"){this.log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath);const simd=await env().getAsync("WASM_HAS_SIMD_SUPPORT");simd||this.log("warning: wasm simd support is not enabled")}await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"&&(this.config.deallocate&&(this.log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)),await ready()}const current=Math.trunc(now2()-timeStamp);current>(this.perf.backend||0)&&(this.perf.backend=current)}async detectFace(input2){let timeStamp,ageRes,genderRes,emotionRes,embeddingRes;const faceRes=[];this.state="run:face",timeStamp=now2();const faces=await this.models.facemesh.estimateFaces(input2,this.config);this.perf.face=Math.trunc(now2()-timeStamp);for(const face2 of faces){if(this.analyze("Get Face"),!face2.image||face2.image.isDisposedInternal){this.log("Face object is disposed:",face2.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face2.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face2.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face2.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face2.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face2.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face2.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face2.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face2.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face2.image.dispose();const irisSize=face2.annotations.leftEyeIris&&face2.annotations.rightEyeIris?11.7*Math.max(Math.abs(face2.annotations.leftEyeIris[3][0]-face2.annotations.leftEyeIris[1][0]),Math.abs(face2.annotations.rightEyeIris[4][1]-face2.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face2.confidence,box:face2.box,mesh:face2.mesh,annotations:face2.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);const process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";const error=this.sanity(input2);error&&(this.log(error,input2),resolve({error}));let poseRes,handRes,faceRes;const timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine15().startScope(),this.analyze("Start Scope:"),timeStamp=now2();const process3=image2.process(input2,this.config);this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?this.models.posenet.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await this.models.posenet.estimatePoses(process3.tensor,this.config):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?this.models.handpose.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await this.models.handpose.estimateHands(process3.tensor,this.config):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine15().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes={face:gesture.face(faceRes),body:gesture.body(poseRes),hand:gesture.hand(handRes)},this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig,sample){sample||(sample=new ImageData(255,255));const warmup=await this.detect(sample,userConfig);return this.log("warmed up"),warmup}}export{Human as default};
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/** @license See the LICENSE file. */
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