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face-api
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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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Buffer shape=${this.shape}`;throw new Error(s)}t++}let o=e[e.length-1];for(let n=0;n<e.length-1;++n)o+=this.strides[n]*e[n];return this.values[o]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let o=0;o<e.length-1;++o)t+=this.strides[o]*e[o];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let o=0;o<t.length-1;++o)t[o]=Math.floor(e/this.strides[o]),e-=t[o]*this.strides[o];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Fi().makeTensor(this.values,this.shape,this.dtype)}},Fi=null,zc=null,_3=null;function AI(r){Fi=r}function DI(r){zc=r}function $I(r){_3=r}var Ve=class{constructor(e,t,o,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=ft(e),this.strides=Hs(e),this.dataId=o,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return zc.buffer(this.shape,this.dtype,e)}bufferSync(){return zc.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Bl(this.shape,e)}arraySync(){return Bl(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Fi().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(o=>Lc(o))}catch(o){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Fi().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Lc(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Fi().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Fi().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return zc.print(this,e)}clone(){return this.throwIfDisposed(),zc.clone(this)}toString(e=!1){let t=this.dataSync();return TI(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),zc.cast(this,e)}variable(e=!0,t,o){return this.throwIfDisposed(),Fi().makeVariable(this,e,t,o)}};Object.defineProperty(Ve,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function F(){return hm("Tensor",()=>Ve)}F();var tl=class extends Ve{constructor(e,t,o,n){super(e.shape,e.dtype,e.dataId,n);this.trainable=t,this.name=o}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Vr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Fi().disposeTensor(this),this.dataId=e.dataId,Fi().incRef(this,null)}dispose(){Fi().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(tl,Symbol.hasInstance,{value:r=>r instanceof Ve&&r.assign!=null&&r.assign instanceof Function});var Fn={};et(Fn,{assertTypesMatch:()=>Ab,getTensorsInContainer:()=>wm,isTensorInList:()=>k3,makeTypesMatch:()=>Ge});var Ib;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(Ib||(Ib={}));var Nb;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(Nb||(Nb={}));var Sb;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(Sb||(Sb={}));var Tb;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(Tb||(Tb={}));var Eb;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(Eb||(Eb={}));var w3={float32:Tb,int32:Nb,bool:Sb,complex64:Eb};function mr(r,e){if(r==="string"||e==="string"){if(r==="string"&&e==="string")return"string";throw new Error(`Can not upcast ${r} with ${e}`)}return w3[r][e]}function hu(r){return mr(r,"int32")}function Ge(r,e){if(r.dtype===e.dtype)return[r,e];let t=mr(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function Ab(r,e){T(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function k3(r,e){return e.some(t=>t.id===r.id)}function wm(r){let e=[],t=new Set;return RI(r,e,t),e}function RI(r,e,t){if(r==null)return;if(r instanceof Ve){e.push(r);return}if(!v3(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),RI(s,e,t))}}function v3(r){return Array.isArray(r)||typeof r=="object"}function Db(r){return r.kernelName!=null}var $b=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},gu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new $b}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let o=e[t];if(await this.initializeBackend(o).success){await this.setBackend(o);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:o},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:o}=this.initializeBackend(e);if(!(o?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new vb(this.backendInstance),!0}setupRegisteredKernels(){xm(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){xm(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Ws)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=o,{success:!0,asyncInit:!1}}catch(o){return console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(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((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let o=e[t],{success:n,asyncInit:s}=this.initializeBackend(o);if(s||n)return{name:o,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let o=this.state.tensorInfo.get(t),n=o.backend,s=this.readSync(t);n.disposeData(t),o.backend=e,e.move(t,s,o.shape,o.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let o=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to 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this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=Db(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Db(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Pc(d,this.backendName);T(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();l=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let _=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,_);let w=_.map(v=>{if(v.rank!=null)return v;let{dataId:$,shape:A,dtype:R}=v;return this.makeTensorFromDataId($,A,R)});if(n){let v=this.getTensorsForGradient(d,h,w);o=this.saveTensorsForBackwardMode(v)}return w}}else{let{forwardFunc:d}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let x=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:p}=e,m=Db(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),n&&this.addTapeNode(u,c,t,m,o,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Bh(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(T(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let l=o.filter((u,c)=>a[c]);return i.concat(l)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");o=o||"float32",n=n||this.backend;let s=e;o==="string"&&ns(e[0])&&(s=e.map(l=>el(l)));let a=n.write(s,t,o),i=new Ve(t,o,a,this.nextTensorId());if(this.incRef(i,n),o==="string"){let l=this.state.tensorInfo.get(a),u=hb(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||"float32";let s=new Ve(t,o,e,this.nextTensorId());return this.incRef(s,n),s}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new tl(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}incRef(e,t){let o=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,o===0){this.state.numDataBuffers++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*db(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.state.numBytes+=n}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof tl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of 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n.data(),[l,u]=[a.length/s,s],c=cb("bool",l);for(let p=0;p<l;p++){let m=p*u,f=a.subarray(m,m+u),d=[];for(let h=0;h<f.length;h++)d.push({value:f[h],index:h});d.sort((h,g)=>g.value-h.value),c[p]=0;for(let h=0;h<t;h++)if(d[h].index===i[p]){c[p]=1;break}}return r!==o&&o.dispose(),e!==n&&n.dispose(),Dr(c,n.shape,"bool")}var NU=IU;var zn={};et(zn,{conv2d:()=>EU,depthwiseConv2d:()=>RU,matMul:()=>OU});function SU(r,e,t,o,n,s="NHWC",a){let i=r;r.rank===3&&(i=L(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let l=e;l.rank===3&&(l=L(e,[1,e.shape[0],e.shape[1],e.shape[2]])),T(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),T(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),T(t.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${t}.`);let u=s==="NHWC"?i.shape[3]:i.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];T(u===t[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth 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a=k(r,"boxes","nonMaxSuppression"),i=k(e,"scores","nonMaxSuppression"),l=Bn(a,i,t,o,n,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,m={boxes:a,scores:i},f={maxOutputSize:u,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=E.runKernel(wi,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var u0=S({nonMaxSuppressionPadded_:JU});async function QU(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=k(r,"boxes","nonMaxSuppressionAsync"),i=k(e,"scores","nonMaxSuppressionAsync"),l=Bn(a,i,t,o,n,null),u=l.maxOutputSize,c=l.iouThreshold,p=l.scoreThreshold,[m,f]=await Promise.all([a.data(),i.data()]),{selectedIndices:d,validOutputs:h}=U_(m,f,u,c,p,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Vt(d,"int32"),validOutputs:le(h,"int32")}}var c0=QU;function ej(r,e,t=!1,o=!1){let n=k(r,"images","resizeBilinear");T(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),T(e.length===2,()=>`Error in resizeBilinear: new 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gj={fft:Ra,ifft:zi,rfft:Fa,irfft:zu},xj={hammingWindow:JN,hannWindow:fg,frame:dg,stft:QN},Ds={flipLeftRight:t0,resizeNearestNeighbor:gg,resizeBilinear:hg,rotateWithOffset:r0,cropAndResize:e0,nonMaxSuppression:o0,nonMaxSuppressionAsync:i0,nonMaxSuppressionWithScore:a0,nonMaxSuppressionWithScoreAsync:l0,nonMaxSuppressionPadded:u0,nonMaxSuppressionPaddedAsync:c0},H_={bandPart:p0,gramSchmidt:m0,qr:d0},yj={absoluteDifference:h0,computeWeightedLoss:Nr,cosineDistance:g0,hingeLoss:x0,huberLoss:y0,logLoss:b0,meanSquaredError:_0,sigmoidCrossEntropy:w0,softmaxCrossEntropy:k0};var Fr=class extends tg{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Te(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){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(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};np.className="Adamax";eo(np);var ll=class extends Fr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=E.registeredVariables[o];V(()=>{let i=Q(O(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=At(le(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let _=b.sourceLayer,w=b.nodeIndex,v=b.tensorIndex;this.outputLayers.push(_),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(v)}for(let b of this.inputs){let _=b.sourceLayer,w=b.nodeIndex,v=b.tensorIndex;Po(w===0,"input layer has >1 nodes"),Po(v===0,"input layer has >1 tensors"),this.inputLayers.push(_),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let _=this.inputLayers[b];if(!(_ instanceof Wi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${_.getClassName()}.`);this.inputNames.push(_.name),this.feedInputShapes.push(_.batchInputShape),this.feedInputNames.push(_.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,_,w,v,$,A)=>{(v==null||$==null||A==null)&&(v=b.sourceLayer,$=b.nodeIndex,A=b.tensorIndex);let R=v.inboundNodes[$];if(w.indexOf(R)!==-1)throw new Or(`The tensor ${b.name} at layer "${v.name}" is part of a cycle.`);if(_.indexOf(R)!==-1)return;this.containerNodes.add(Mo.nodeKey(v,$)),v.id in a||(a[v.id]=Object.keys(a).length),w.indexOf(R)===-1&&w.push(R);let M=R.inboundLayers.length;for(let z=0;z<M;z++){let W=R.inputTensors[z],U=R.inboundLayers[z],q=R.nodeIndices[z],Z=R.tensorIndices[z];l(W,_,w,U,q,Z)}for(_.push(R);w.indexOf(R)>=0;)w.splice(w.indexOf(R),1);i.push(R)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){o[b.id]=b,b.id in t||(t[b.id]=0);let _=t[b.id],w=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];_=Math.max(_,w),n[b.outboundLayer.id]=_,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=_;for(let v=0;v<b.inboundLayers.length;v++){let $=b.inboundLayers[v],A=b.nodeIndices[v],R=$.inboundNodes[A],M=t[R.id]==null?0:t[R.id];t[R.id]=Math.max(_+1,M),o[R.id]=R}}let m={};for(let b in t){let _=t[b];_ in m||(m[_]=[]),m[_].push(o[b])}let f={};for(let b in n){let _=n[b];_ in f||(f[_]=[]),f[_].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(lf);this.layers=[];for(let b of d){let _=f[b];_.sort((w,v)=>{let $=a[w.id],A=a[v.id];return $<A?-1:$>A?1:0});for(let w of _)w instanceof Mo&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(lf);let h=this.inputs.slice(),g=[];for(let b of d)for(let _ of m[b]){let w=_.outboundLayer;if(w!=null){for(let v of _.inputTensors)if(h.indexOf(v)===-1)throw new Or(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${w.name}". The following previous layers were accessed without issue: ${g}`);for(let v of _.outputTensors)h.push(v);g.push(w.name)}}this.nodesByDepth=m;let x=this.layers.map(b=>b.name);for(let b of x){let _=x.filter(w=>w===b).length;if(_!==1)throw new Or(`The name "${b}" is used ${_} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(x))}this.outboundNodes=[],this.inboundNodes=[],new ml({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(o=>o.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("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 e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new B(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new B(`${a.length} of ${n} weights are not set: ${a}`)}_p(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${hl}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Gg(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return V(()=>{e=yt(e);let o=new Fs;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return Ju(this.outputs,o,t)})}computeMask(e,t){return V(()=>{e=yt(e);let o;return t==null?o=Vn(null,e.length):o=yt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=yp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(lf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],x=u.nodeIndices[h],b=u.tensorIndices[h],_=`${g.name}_${x}_${b}`,w=o[_];p.push(w)}let m=c.computeOutputShape(dr(p)),f=yp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Po(l in o),s.push(o[l])}return dr(s)}runInternalGraph(e,t){t==null&&(t=Vn(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(lf);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,x,b,_;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[w,v]=d[0];h.mask==null&&(h.mask=v),b=yt(p.call(w,h)),_=yt(p.computeMask(w,v)),g=[w],x=[v]}else g=d.map(w=>w[0]),x=d.map(w=>w[1]),h.mask==null&&(h.mask=x),b=yt(p.call(g,h)),_=yt(p.computeMask(g,x));if(p.activityRegularizer)throw new Ne("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w<f.length;++w){let v=f[w],$=b[w],A=_[w];o[v.id]=[$,A]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Po(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Mo?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Mo.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let o of this.layers)if(o.name===e)return o;throw new B(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Mo.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Mo.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],b=m.nodeIndices[g],_=m.tensorIndices[g],w=Mo.nodeKey(x,b),v=t[w];v==null&&(v=0),h.push([x.name,v,_,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,o.push(c)}e.layers=o;let n=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=Mo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];n.push([i.name,c,p])}e.inputLayers=n;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=Mo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,o={},n=!1){let s={},a={};function i(g,x){g.name in a?a[g.name].push(x):a[g.name]=[x]}function l(g,x){let b=[],_;for(let w of x){let v=w[0],$=w[1],A=w[2];if(_=w[3]==null?{}:w[3],!(v in s)){i(g,x);return}let R=s[v];if(R.inboundNodes.length<=$){i(g,x);return}let M=R.inboundNodes[$];b.push(M.outputTensors[A])}b.length>0&&g.apply(dr(b),_)}function u(g){let x=g.name,b=Yr(g,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(n),s[x]=b,g.inboundNodes.forEach(w=>{if(!(w instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${w}`);i(b,w)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!bT(a);)for(let g of p){let x=s[g.name];if(x.name in a){let b=a[x.name];delete a[x.name];for(let _ of b)l(x,_)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let x=g[0],b=g[1],_=g[2];Po(x in s);let v=s[x].inboundNodes[b].outputTensors;m.push(v[_])}let h=t.outputLayers;for(let g of h){let x=g[0],b=g[1],_=g[2];Po(x in s);let v=s[x].inboundNodes[b].outputTensors;f.push(v[_])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function lq(r,e,t){let o=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(n=>null);if(o===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==o)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${o} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let n=[];return e.forEach(s=>{s in r?n.push(r[s]):n.push(null)}),n}else throw new Error(`The model has multiple (${o}) outputs, so ${t} must be either an array with ${o} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function Wg(r,e){return lq(r,e,"classWeight")}async function Ug(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Te(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l<s.length;l++)y.assert(s[l].shape[0]===i,()=>`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l<a.length;l++)y.assert(a[l].shape[0]===i,()=>`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function o1(r,e,t){if(t instanceof Ve)return[t];if(Array.isArray(t))return y.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let o=[];for(let n of e){if(t[n]==null)throw new B(`The feature data generated by the dataset lacks the required ${r} key '${n}'.`);o.push(t[n])}return o}}function cq(r){if(r.length===3)throw new Ne("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function i1(r,e,t){let o=t.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(t!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(t.epochs!=null&&t.epochs>0&&Number.isInteger(t.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${t.epochs}`),y.assert(!o||t.batchesPerEpoch>0&&Number.isInteger(t.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${t.batchesPerEpoch}`),y.assert(t.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(s1(t.validationData))y.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=cq(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Og(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=Pg(c,p,t.epochs,null,null,pq(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(o||(h=await e.iterator());o?x<t.batchesPerEpoch:!0;){let _=await h.next();if(o&&_.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. 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e.metrics)s[a]=Pa(e.metrics[a])}this.compile({loss:n,metrics:s,optimizer:o})}async save(e,t){if(typeof e=="string"){let u=vr.getSaveHandlers(e);if(u.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new B(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let o=await vr.encodeWeights(this.getNamedWeights(t)),n=!1,s=null,i={modelTopology:this.toJSON(s,n),format:yq,generatedBy:`TensorFlow.js tfjs-layers v${hl}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:p}=await vr.encodeWeights(await this.optimizer.getWeights(),u);o.specs.push(...p),o.data=vr.concatenateArrayBuffers([o.data,c])}if(this.userDefinedMetadata!=null){let 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,o={},n=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");s=t}else y.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Hi))throw new Ne(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=Yr(l,void 0,n);n&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let o={};o.className=t.getClassName(),o.config=t.getConfig(),e.push(o)}return{name:this.name,layers:e}}};Hi.className="Sequential";J.registerClass(Hi);function d1(r){return new vo(r)}function h1(r){return new Hi(r)}function g1(r,e){return e==null&&(e={}),f1(r,e)}function Kg(r){return Ag(r)}function x1(r,e){no.registerCallbackConstructor(r,e)}var so=class extends J.Serializable{getConfig(){return{}}},bw=class extends so{apply(e,t=1){return MT(e,t)}};bw.className="elu";J.registerClass(bw);var _w=class extends so{apply(e){return Pu(e)}};_w.className="selu";J.registerClass(_w);var ww=class extends so{apply(e){return Ir(e)}};ww.className="relu";J.registerClass(ww);var kw=class extends so{apply(e){return V(()=>Ts(6,Ir(e)))}};kw.className="relu6";J.registerClass(kw);var vw=class extends so{apply(e){return e}};vw.className="linear";J.registerClass(vw);var Cw=class extends so{apply(e){return 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Received: ${r.length} elements.`);for(let o=0;o<e;++o){let n=r[o];if(!$T(n))throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. 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instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=vu(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ro(i,t)),i})}function k1(r,e,t,o=[1,1],n="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Kr()),$t(s),r.rank!==3&&r.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=zf(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function kq(r,e,t,o=[1,1,1],n="valid",s,a){return V(()=>{if(s==null&&(s=Kr()),$t(s),r.rank!==4&&r.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=Fw(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Rm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ro(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Ip=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Ip.verifyArgs(t),this.rank=e,Wt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=xl(t.kernelSize,e,"kernelSize"),this.strides=xl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Xr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=Ps(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=dt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=xl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`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 B(`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 B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Po("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!_g(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Os(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ec=class extends Ip{constructor(e,t){super(e,t);this.kernel=null,ec.verifyArgs(t),this.filters=t.filters,Wt(this.filters,"filters"),this.kernelInitializer=dt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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:{[t]:o}}],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o,n=this.bias==null?null:this.bias.read(),s=wg(this.activation.getClassName());if(s!=null&&this.rank===2)o=k1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=wq(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=k1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=kq(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ne("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Qe(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=io(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:It(this.kernelInitializer),kernelRegularizer:lt(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},yl=class extends ec{constructor(e){super(2,e);yl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_g(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};yl.className="Conv2D";J.registerClass(yl);var tc=class extends ec{constructor(e){super(3,e);tc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};tc.className="Conv3D";J.registerClass(tc);var Bf=class extends yl{constructor(e){super(e);if(this.inputSpec=[new Et({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Et({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{let o=Oe(e);if(o.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Lf(l,m,c,this.padding),h=Lf(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=Ue(o,[0,2,3,1]));let x=Cu(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ue(x,[0,3,1,2])),this.bias!=null&&(x=ro(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Qe(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=Lf(t[n],l,a,this.padding),t[s]=Lf(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Bf.className="Conv2DTranspose";J.registerClass(Bf);var Ow=class extends ec{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Et({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),o=Km(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=Ue(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};Ow.className="SeparableConv";var Vf=class extends Ow{constructor(e){super(2,e)}};Vf.className="SeparableConv2D";J.registerClass(Vf);var rc=class extends ec{constructor(e){super(1,e);rc.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_g(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};rc.className="Conv1D";J.registerClass(rc);var Gf=class extends Me{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let o=df(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return df(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=df(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return df(o,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Gf.className="Cropping2D";J.registerClass(Gf);var Wf=class extends Me{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ET(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return V(()=>{let o=Oe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=Ue(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Wf.className="UpSampling2D";J.registerClass(Wf);function vq(r,e,t=[1,1],o="valid",n,s){return V(()=>{n==null&&(n=Kr()),$t(n);let a=zf(r,n);if(r.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Cs(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var Uf=class extends Ip{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=dt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Qe(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o=vq(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ro(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=io(t,this.kernelSize[0],this.padding,this.strides[0]),a=io(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=It(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};Uf.className="DepthwiseConv2D";J.registerClass(Uf);function Pw(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function Mw(r,e,t,o=!1,n,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Pr(2,l));if(e=Ue(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=sr(n,-1)),n=Ue(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=ur(e),h;n!=null&&(h=ur(n));for(let x=0;x<f;++x){let b=d[x],_=V(()=>r(b,m));if(n==null)p=_[0],m=_[1];else{let w=V(()=>{let v=h[x],$=er(v).sub(v),A=_[0].mul(v).add(m[0].mul($)),R=m.map((M,z)=>_[1][z].mul(v).add(M.mul($)));return{output:A,newStates:R}});p=w.output,m=w.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var ao=class extends Me{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Np({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Et({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Eg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Ne("Constants support is not implemented in RNN yet.");Eg(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Et({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new B(`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=a.map(i=>new Et({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new B("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(n=>gt([o,n])):this.states_=[gt([o,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>gt([o,n])):this.states_[0]=gt([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!y.arraysEqual(s.shape,i))throw new B(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>At(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Pw(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new Et({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof Lr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Oe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new B(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=Mw((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=gt(e.shape);return t=ye(t,[1,2]),t=La(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?Cg(t,[1,o]):t):this.cell.stateSize>1?[Cg(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let o=this.cell.getConfig();return this.getClassName()===ao.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=Yr(n,o);return new e(Object.assign(t,{cell:s}))}};ao.className="RNN";J.registerClass(ao);var bl=class extends Me{},Sp=class extends bl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Wt(this.units,"units"),this.activation=Ps(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=qu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(e),this.kernel=this.addWeight("kernel",[e[e.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(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Hn(O(e,a),this.kernel.read()):s=Hn(e,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),i!=null&&(o=O(o,i));let l=Q(s,Hn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Sp.className="SimpleRNNCell";J.registerClass(Sp);var jf=class extends ao{constructor(e){e.cell=new Sp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};jf.className="SimpleRNN";J.registerClass(jf);var Tp=class extends bl{constructor(e){super(e);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",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Wt(this.units,"units"),this.activation=Ps(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ps(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=qu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,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(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let c=Hn(e,this.kernel.read());this.useBias&&(c=ro(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=lr(p,[2*this.units,this.units],p.rank-1),d=Hn(n,m),[h,g,x]=lr(c,3,c.rank-1),[b,_]=lr(d,2,d.rank-1);i=this.recurrentActivation.apply(Q(h,b)),l=this.recurrentActivation.apply(Q(g,_));let w=Hn(O(l,n),f);u=this.activation.apply(Q(x,w));let v=Q(O(i,n),O(Q(1,je(i)),u));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Tp.className="GRUCell";J.registerClass(Tp);var Hf=class extends ao{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Tp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Hf.className="GRU";J.registerClass(Hf);var _l=class extends bl{constructor(e){super(e);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=e.units,Wt(this.units,"units"),this.activation=Ps(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ps(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=qu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Qe(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends oo{apply(l,u){let c=s.apply([a]),p=new Xu().apply([a]),m=s.apply([a*2]);return aw(aw(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let m=Hn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,i[0])),m=Q(m,Hn(n,this.recurrentKernel.read())),this.useBias&&(m=ro(m,this.bias.read()));let[f,d,h,g]=lr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Q(O(u,s),O(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=O(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};_l.className="LSTMCell";J.registerClass(_l);var qf=class extends ao{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new _l(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};qf.className="LSTM";J.registerClass(qf);var Np=class extends bl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Eg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{$s(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(Yr(s,o));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vf(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}_p(t)}};Np.className="StackedRNNCells";J.registerClass(Np);function Ba(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Ng(e(),t),a=()=>cl(s,e,o);return!n||n<=1?At(a().clone()):Array(n).fill(void 0).map(a).map(l=>At(l.clone()))}var Cq=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var Lw=class extends ao{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Et({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=gt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new ko("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new B("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(()=>gt(s)):this.states_=[gt(s)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>gt(s)):this.states_[0]=gt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new B(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>At(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=io(u,n[0],s,a[0],i[0]),m=io(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};Lw.className="ConvRNN2D";var Ep=class extends _l{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Wt(this.filters,"filters"),this.kernelSize=xl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Wt(l,"kernelSize")),this.strides=xl(n||1,2,"strides"),this.strides.forEach(l=>Wt(l,"strides")),this.padding=s||"valid",Xr(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=xl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Wt(l,"dilationRate"))}build(e){var t;e=Qe(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends oo{apply(m,f){let d=u.apply([c]),h=Cr([c]),g=u.apply([c*2]);return cp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ba({ones:()=>er(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(ie,se,pe)=>!se||!se[pe]?ie:O(se[pe],ie),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ba({ones:()=>er(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),_=3,[w,v,$,A]=lr(this.kernel.read(),i,_),[R,M,z,W]=this.useBias?lr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,R,this.padding),p=this.inputConv(p,v,M,this.padding),m=this.inputConv(m,$,z,this.padding),f=this.inputConv(f,A,W,this.padding);let[U,q,Z,X]=lr(this.recurrentKernel.read(),i,_);h=this.recurrentConv(h,U),g=this.recurrentConv(g,q),x=this.recurrentConv(x,Z),b=this.recurrentConv(b,X);let Y=this.recurrentActivation.apply(Q(c,h)),te=this.recurrentActivation.apply(Q(p,g)),K=Q(O(te,a),O(Y,this.activation.apply(Q(m,x)))),re=O(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(K));return[re,re,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=Cq(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=jr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ro(s,o,this.dataFormat):s}recurrentConv(e,t){return jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ep.className="ConvLSTM2DCell";J.registerClass(Ep);var Kf=class extends Lw{constructor(e){let t=new Ep(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Kf.className="ConvLSTM2D";J.registerClass(Kf);var Ap=class extends Me{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return cl(()=>Ng(o,this.rate,s,this.seed),()=>o,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Ap.className="Dropout";J.registerClass(Ap);var Xf=class extends Ap{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Xf.className="SpatialDropout1D";J.registerClass(Xf);var Yf=class extends Me{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Wt(this.units,"units"),this.activation=Ps(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Qe(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,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]:t}}],this.built=!0}computeOutputShape(e){e=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=wg(this.activation.getClassName()),s;return n!=null?s=Hn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Hn(o,this.kernel.read()),this.bias!=null&&(s=ro(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Yf.className="Dense";J.registerClass(Yf);var Zf=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],jn(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return PT(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Zf.className="Flatten";J.registerClass(Zf);var Jf=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ps(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Jf.className="Activation";J.registerClass(Jf);var Qf=class extends Me{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Oe(e),FT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Qf.className="RepeatVector";J.registerClass(Qf);var ed=class extends Me{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new B("Can only specifiy one unknown dimension.");else s*=u}let i=jn(e);if(a!==null){if(s===0||i%s!=0)throw new B(o);n[a]=i/s}else if(i!==s)throw new B(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ed.className="Reshape";J.registerClass(ed);var td=class extends Me{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Pr(1,e.dims.length+1);if(!y.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Et({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return Ue(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};td.className="Permute";J.registerClass(td);var rd=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let o=Oe(e),n=-1;return nl(Ln(o,this.maskValue),n)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=-1,s=!0,a=nl(Ln(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};rd.className="Masking";J.registerClass(rd);var od=class extends Me{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Wt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Wt(this.outputDim,"outputDim"),this.embeddingsInitializer=dt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Oe(e),Ln(e,Ie(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return o.dtype!=="int32"&&(o=Ma(o,"int32")),Ig(this.embeddings.read(),o.as1D()).reshape(Qe(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:It(this.embeddingsInitializer),embeddingsRegularizer:lt(this.embeddingsRegularizer),activityRegularizer:lt(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};od.className="Embedding";J.registerClass(od);var wl=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Qe(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=Un(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&Un(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Rs(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=La(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(jn(c.slice(1))));f=Ue(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Pr(1,u).concat([0]);o.push(Ue(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ue(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Pr(0,i-1));a=Ue(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=Un(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:sr(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=fr(o,t[n]);return o})}},nd=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return t})}};nd.className="Add";J.registerClass(nd);var sd=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=O(t,e[o]);return t})}};sd.className="Multiply";J.registerClass(sd);var id=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return O(1/e.length,t)})}};id.className="Average";J.registerClass(id);var ad=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=qr(t,e[o]);return t})}};ad.className="Maximum";J.registerClass(ad);var ld=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Ts(t,e[o]);return t})}};ld.className="Minimum";J.registerClass(ld);var ud=class extends wl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(y.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>cp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,o=t[0].slice(),n=this.axis<0?o.length+this.axis:this.axis;for(let s of t.slice(1)){if(o[n]==null||s[n]==null){o[n]=null;break}o[n]+=s[n]}return o}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let o=!0;if(t.forEach(a=>{if(a!=null){o=!1;return}}),o)return null;let n=[];for(let a=0;a<e.length;++a)t[a]==null?n.push(er(e[a]).asType("bool")):t[a].rank<e[a].rank?n.push(sr(t[a],-1)):n.push(t[a]);let s=Je(n,this.axis);return _u(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ud.className="Concatenate";J.registerClass(ud);function cd(r,e){for(;r<0;)r+=e;return r}function Iq(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return V(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var pd=class extends wl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],o=e[1];if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new B(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>cd(s,e[a].shape.length)):n=[cd(this.axes,t.shape.length),cd(this.axes,o.shape.length)],this.normalize&&(t=Cf(t,n[0]),o=Cf(o,n[1])),Iq(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[cd(this.axes,e.length),cd(this.axes,t.length)],o}computeOutputShape(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),o=e[1].slice();if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};pd.className="Dot";J.registerClass(pd);var md=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return cl(()=>pp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};md.className="GaussianNoise";J.registerClass(md);var fd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.rate>0&&this.rate<1?cl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(pp(o.shape,1,s))},()=>o,t.training||!1):o})}};fd.className="GaussianDropout";J.registerClass(fd);var dd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return cl(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=to(Es(o),this.rate);u=Ma(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Oe(e),t.training||!1)}return e})}};dd.className="AlphaDropout";J.registerClass(dd);function hd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=s_(r,e,t,o,n,s);else if(r.rank===3)a=i_(r,e,t,o,n,s);else if(r.rank===4)a=a_(r,e,t,o,n,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function Nq(r,e,t,o,n=.001){return V(()=>{let s=Xc(r,o),a=s.mean,i=s.variance;return[hd(r,a,i,t,e,n),a,i]})}function Sq(r,e,t,o,n=.001){return V(()=>{let s=Xc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Pr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[hd(r,u,c,m,p,n),a,i]})}function Tq(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Pr(0,r.rank-1))?Nq(r,e,t,o,n):Sq(r,e,t,o,n)}var gd=class extends Me{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=dt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Qe(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Et({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training,n=Oe(e),s=n.shape,a=s.length,i=Pr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Vn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Pr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),_=this.movingVariance.read().reshape(u),w=this.center?this.beta.read().reshape(u):null,v=this.scale?this.gamma.read().reshape(u):null;return hd(n,b,_,w,v,this.epsilon)}else return hd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=Tq(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,_,w)=>{V(()=>{let v=1-w,$=b.read(),A=$.sub(_).mul(v);b.write($.sub(A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),movingMeanInitializer:It(this.movingMeanInitializer),movingVarianceInitializer:It(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};gd.className="BatchNormalization";J.registerClass(gd);var xd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))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=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=dt(e.betaInitializer||"zeros"),this.gammaInitializer=dt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Un(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Oe(e),n=o.shape,s=n.length;return V(()=>{let a=!0,{mean:i,variance:l}=Xc(o,this.axis,a),u=Vn(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),hd(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};xd.className="LayerNormalization";J.registerClass(xd);function Eq(r,e,t){return V(()=>{if(r.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Kr()),t!=="channelsLast"&&t!=="channelsFirst")throw new B(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let o;return t==="channelsFirst"?o=[[0,0],[0,0],e[0],e[1]]:o=[[0,0],e[0],e[1],[0,0]],$r(r,o)})}var yd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Kr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,o;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],o=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,o;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return V(()=>Eq(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};yd.className="ZeroPadding2D";J.registerClass(yd);function Xg(r,e,t,o,n,s){return V(()=>{$t(n),sw(s),Xr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=zf(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Ea(r,e,t,i):a=va(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function v1(r,e,t,o,n,s){return V(()=>{$t(n),sw(s),Xr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Kr()),s==null&&(s="max"),r=Fw(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Gm(r,e,t,i):a=Dm(r,e,t,i),n==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var zw=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Wt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Xr(this.padding),this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=io(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=La(Oe(e),2);let o=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return wo(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},bd=class extends zw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"max")}};bd.className="MaxPooling1D";J.registerClass(bd);var _d=class extends zw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"avg")}};_d.className="AveragePooling1D";J.registerClass(_d);var Bw=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},wd=class extends Bw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"max")}};wd.className="MaxPooling2D";J.registerClass(wd);var kd=class extends Bw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),Xg(e,t,o,n,s,"avg")}};kd.className="AveragePooling2D";J.registerClass(kd);var Vw=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),Xr(this.padding),this.inputSpec=[new Et({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=io(t,this.poolSize[0],this.padding,this.strides[0]),o=io(o,this.poolSize[1],this.padding,this.strides[1]),n=io(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},vd=class extends Vw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),v1(e,t,o,n,s,"max")}};vd.className="MaxPooling3D";J.registerClass(vd);var Cd=class extends Vw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Xr(n),v1(e,t,o,n,s,"avg")}};Cd.className="AveragePooling3D";J.registerClass(Cd);var Gw=class extends Me{constructor(e){super(e);this.inputSpec=[new Et({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},Id=class extends Gw{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Oe(e);return ht(o,1)})}};Id.className="GlobalAveragePooling1D";J.registerClass(Id);var Nd=class extends Gw{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Oe(e);return ar(o,1)})}};Nd.className="GlobalMaxPooling1D";J.registerClass(Nd);var Ww=class extends Me{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.inputSpec=[new Et({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Sd=class extends Ww{call(e,t){return V(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ht(o,[1,2]):ht(o,[2,3])})}};Sd.className="GlobalAveragePooling2D";J.registerClass(Sd);var Td=class extends Ww{call(e,t){return V(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?ar(o,[1,2]):ar(o,[2,3])})}};Td.className="GlobalMaxPooling2D";J.registerClass(Td);var Uw=class extends Me{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}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(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,o={}){let n=t.layer,s=Yr(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Ed=class extends Uw{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Qe(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Qe(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return V(()=>(e=Oe(e),Mw((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ed.className="TimeDistributed";J.registerClass(Ed);function Aq(r){Gi(TT,"BidirectionalMergeMode",r)}var Dq="concat",Ad=class extends Uw{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=Yr(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Yr(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Dq:e.mergeMode,Aq(this.mergeMode),e.weights)throw new Ne("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):dr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Pw(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.length;if(u%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=o,a.push(...o);let c=o.map(p=>new Et({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Ne("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof Lr;for(let u of a)if(u instanceof Lr!==l)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=qt(s,1));let i;return this.mergeMode==="concat"?i=cp([n,s]):this.mergeMode==="sum"?i=Q(n,s):this.mergeMode==="ave"?i=O(.5,Q(n,s)):this.mergeMode==="mul"?i=O(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){$s(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),$s(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let o=Yr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ne("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let n=t;return n.layer=o,new e(n)}};Ad.className="Bidirectional";J.registerClass(Ad);function $q(r){return new Wi(r)}function Rq(r){return new Of(r)}function Fq(r){return new $f(r)}function Oq(r){return new Rf(r)}function Pq(r){return new Ff(r)}function Mq(r){return new Mf(r)}function Lq(r){return new Pf(r)}function zq(r){return new rc(r)}function Bq(r){return new yl(r)}function Vq(r){return new Bf(r)}function 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Missing built-in atob() or Buffer()")}function O1(r,e){let t=Array.isArray(r)?String.fromCharCode.apply(null,r):v6(r);return e?t:t.toLowerCase()}function rx(r,e,t,o=!1){let n=r[e];return n!=null?O1(n.s,o):t}function ox(r,e,t){let o=r[e];return o?o.b:t}function nx(r,e,t){let o=r[e]||{},n=o.i!=null?o.i:o.f!=null?o.f:t;return typeof n=="number"?n:parseInt(n,10)}function gk(r){switch(typeof r=="string"&&(r=qn[r]),r){case qn.DT_FLOAT:return"float32";case qn.DT_INT32:case qn.DT_INT64:case qn.DT_INT8:case qn.DT_UINT8:return"int32";case qn.DT_BOOL:return"bool";case qn.DT_DOUBLE:return"float32";case qn.DT_STRING:return"string";default:return null}}function F1(r,e,t){let o=r[e];return o&&o.func?o.func.name:t}function sx(r,e,t){let o=r[e];return o&&o.type?gk(o.type):t}function ix(r,e,t){let o=r[e];return o&&o.list&&o.list.type?o.list.type.map(n=>gk(n)):t}function P1(r){if(!r.unknownRank)return r.dim!=null?r.dim.map(e=>typeof e.size=="number"?e.size:parseInt(e.size,10)):[]}function ax(r,e,t){let 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M1=(r,e,t)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[Q(C("a",r,e,t),C("b",r,e,t))];case"AddN":return[t_(C("tensors",r,e,t))];case"FloorMod":case"Mod":return[Um(C("a",r,e,t),C("b",r,e,t))];case"Mul":return[O(C("a",r,e,t),C("b",r,e,t))];case"RealDiv":case"Div":return[de(C("a",r,e,t),C("b",r,e,t))];case"DivNoNan":return[Pm(C("a",r,e,t),C("b",r,e,t))];case"FloorDiv":return[bu(C("a",r,e,t),C("b",r,e,t))];case"Sub":return[ue(C("a",r,e,t),C("b",r,e,t))];case"Minimum":return[Ts(C("a",r,e,t),C("b",r,e,t))];case"Maximum":return[qr(C("a",r,e,t),C("b",r,e,t))];case"Pow":return[Rr(C("a",r,e,t),C("b",r,e,t))];case"SquaredDifference":return[Bu(C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var L1=(r,e,t)=>{switch(r.op){case"Abs":case"ComplexAbs":return[Tt(C("x",r,e,t))];case"Acos":return[vm(C("x",r,e,t))];case"Acosh":return[Cm(C("x",r,e,t))];case"Asin":return[Nm(C("x",r,e,t))];case"Asinh":return[Sm(C("x",r,e,t))];case"Atan":return[Tm(C("x",r,e,t))];case"Atan2":return[Em(C("x",r,e,t),C("y",r,e,t))];case"Atanh":return[Am(C("x",r,e,t))];case"Ceil":return[$m(C("x",r,e,t))];case"Complex":return[bo(C("real",r,e,t),C("imag",r,e,t))];case"Cos":return[Ia(C("x",r,e,t))];case"Cosh":return[Iu(C("x",r,e,t))];case"Elu":return[Is(C("x",r,e,t))];case"Erf":return[Mm(C("x",r,e,t))];case"Exp":return[Xt(C("x",r,e,t))];case"Expm1":return[Lm(C("x",r,e,t))];case"Floor":return[Ns(C("x",r,e,t))];case"Log":return[ir(C("x",r,e,t))];case"Log1p":return[Eu(C("x",r,e,t))];case"Imag":return[Su(C("x",r,e,t))];case"Neg":return[je(C("x",r,e,t))];case"Reciprocal":return[Hm(C("x",r,e,t))];case"Real":return[al(C("x",r,e,t))];case"Relu":return[Ir(C("x",r,e,t))];case"Round":return[qm(C("x",r,e,t))];case"Selu":return[Pu(C("x",r,e,t))];case"Sigmoid":return[Ur(C("x",r,e,t))];case"Sin":return[Mu(C("x",r,e,t))];case"Sign":return[Xm(C("x",r,e,t))];case"Sinh":return[Lu(C("x",r,e,t))];case"Softplus":return[Ss(C("x",r,e,t))];case"Sqrt":return[xt(C("x",r,e,t))];case"Square":return[Pe(C("x",r,e,t))];case"Tanh":return[Mi(C("x",r,e,t))];case"Tan":return[Qm(C("x",r,e,t))];case"ClipByValue":return[nr(C("x",r,e,t),C("clipValueMin",r,e,t),C("clipValueMax",r,e,t))];case"Relu6":return[Fu(C("x",r,e,t))];case"Rsqrt":return[Ou(hr(r.inputNames[0],e,t))];case"Prod":return[$u(C("x",r,e,t),C("axes",r,e,t))];case"LeakyRelu":return[Sa(C("x",r,e,t),C("alpha",r,e,t))];case"Prelu":return[Da(C("x",r,e,t),C("alpha",r,e,t))];default:throw 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J1=(r,e,t)=>{switch(r.op){case"Equal":return[_o(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[Ln(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[Qt(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[to(C("a",r,e,t),C("b",r,e,t))];case"Less":return[Tu(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[Oo(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[fr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[Ta(C("a",r,e,t))];case"LogicalOr":return[Du(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[Dt(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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s=s.slice(0,o),[Je(s,n)]}case"Gather":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[Pn(o,oe(n,"int32"),0)]}case"GatherV2":{let o=C("axis",r,e,t),n=C("batchDims",r,e,t),s=C("x",r,e,t),a=C("indices",r,e,t);return[Pn(s,oe(a,"int32"),o,n)]}case"Reverse":{let o=C("dims",r,e,t),n=[];for(let a=0;a<o.length;a++)o[a]&&n.push(a);let s=C("x",r,e,t);return[qt(s,n)]}case"ReverseV2":{let o=C("axis",r,e,t),n=C("x",r,e,t);return[qt(n,o)]}case"Slice":{let o=C("begin",r,e,t),n=C("size",r,e,t);return[Fe(C("x",r,e,t),o,n)]}case"StridedSlice":{let o=C("begin",r,e,t),n=C("end",r,e,t),s=C("strides",r,e,t),a=C("beginMask",r,e,t),i=C("endMask",r,e,t),l=C("ellipsisMask",r,e,t),u=C("newAxisMask",r,e,t),c=C("shrinkAxisMask",r,e,t),p=C("x",r,e,t);return[Jm(p,o,n,s,a,i,l,u,c)]}case"Pack":return V(()=>{let o=C("axis",r,e,t),n=C("tensors",r,e,t),s=n[0].shape,a=wo(n[0]).shape,i=n.map(l=>{let u=y.arraysEqual(l.shape,s);if(!u&&!y.arraysEqual(wo(l).shape,a))throw new Error("the input tensors shape does not match");return u?l:L(l,s)});return[Bt(i,o)]});case"Unpack":{let o=C("axis",r,e,t),n=C("tensor",r,e,t);return ur(n,o)}case"Tile":{let o=C("reps",r,e,t);return[Fo(C("x",r,e,t),o)]}case"Split":case"SplitV":{let o=C("axis",r,e,t),n=C("numOrSizeSplits",r,e,t),s=C("x",r,e,t);return lr(s,n,o)}case"ScatterNd":{let o=C("indices",r,e,t),n=C("values",r,e,t),s=C("shape",r,e,t);return[L_(o,n,s)]}case"GatherNd":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[z_(o,n)]}case"SparseToDense":{let o=C("sparseIndices",r,e,t),n=C("outputShape",r,e,t),s=C("sparseValues",r,e,t),a=C("defaultValue",r,e,t);return[of(o,s,n,s.dtype===a.dtype?a:oe(a,s.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var oE=(r,e,t)=>{switch(r.op){case"FFT":return[Ra(C("x",r,e,t))];case"IFFT":return[zi(C("x",r,e,t))];case"RFFT":return[Fa(C("x",r,e,t))];case"IRFFT":return[zu(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var nE=(r,e,t)=>{switch(r.op){case"Cast":return[oe(C("x",r,e,t),C("dtype",r,e,t))];case"ExpandDims":{let o=C("axis",r,e,t);return[sr(C("x",r,e,t),o)]}case"Squeeze":{let o=C("axis",r,e,t);return[wo(C("x",r,e,t),o)]}case"Reshape":return[L(C("x",r,e,t),C("shape",r,e,t))];case"MirrorPad":return[Wm(C("x",r,e,t),C("padding",r,e,t),C("mode",r,e,t))];case"PadV2":case"Pad":return[$r(C("x",r,e,t),C("padding",r,e,t),C("constantValue",r,e,t))];case"SpaceToBatchND":{let o=C("blockShape",r,e,t),n=C("paddings",r,e,t);return[Aa(C("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=C("blockShape",r,e,t),n=C("crops",r,e,t);return[Ca(C("x",r,e,t),o,n)]}case"DepthToSpace":{let o=C("blockSize",r,e,t),n=C("dataFormat",r,e,t).toUpperCase();return[Fm(C("x",r,e,t),o,n)]}case"BroadcastTo":return[il(C("x",r,e,t),C("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function wk(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return V(()=>M1(s,a,i));case"basic_math":return V(()=>L1(s,a,i));case"control":return W1(s,a,i);case"convolution":return V(()=>j1(s,a,i));case"creation":return V(()=>H1(s,a,i));case"dynamic":return q1(s,a,i);case"evaluation":return V(()=>K1(s,a,i));case"image":return V(()=>Z1(s,a,i));case"graph":return V(()=>X1(s,a,i));case"logical":return V(()=>J1(s,a,i));case"matrices":return V(()=>Q1(s,a,i));case"normalization":return V(()=>eE(s,a,i));case"reduction":return V(()=>tE(s,a,i));case"slice_join":return V(()=>rE(s,a,i));case"spectral":return V(()=>oE(s,a,i));case"transformation":return V(()=>nE(s,a,i));case"hash_table":return Y1(s,a,i,o);case"custom":let l=Qg(s.op);if(l&&l.customExecutor)return l.customExecutor(new xk(s,a,i));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(n)?n.then(s=>[].concat(s)):[].concat(n)}var mx=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let o=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(o))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function vk(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>Zr(m)[0]),c=[];o!=null&&(c=o.map(m=>Zr(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((kk(m)||I6(m)||N6(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function sE(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>Zr(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var S6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],T6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],E6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function kk(r){return S6.indexOf(r.op)>=0}function I6(r){return T6.indexOf(r.op)>=0}function N6(r){return E6.indexOf(r.op)>=0}var Dp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new Dp(e.functions[o],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(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=vk(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${l}]. Missing the following inputs: [${n}]`)}return sE(this.graph,this.weightMap,o)}execute(e,t){e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(p=>this.graph.nodes[Zr(p)[0]]),s=t.map(p=>Zr(p)[0]),a=s.map(p=>this.graph.nodes[p]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),l=this.compiledMap.get(i);l==null&&(l=this.compile(e,a),this.compiledMap.set(i,l));let u={},c={};return V(()=>{let p=new mx(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(e).forEach(h=>{let[g,x]=Zr(h),b=[];b[x]=e[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<l.length;h++){let g=l[h];if(!m[g.name]){let x=wk(g,m,p,this._resourceManager);if(y.isPromise(x))throw new Error(`The execution of the op '${g.op}' returned a promise. 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You can use model.execute() instead.");let b=l.filter(_=>!kk(_)&&!hr(_.name,d,t)).map(_=>_.name);if(b.length>0){let _="";throw p!=null&&(_=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${_}`)}return d}processStack(e,t,o,n,s,a,i,l,u){let c=[];for(;t.length>0;){let p=t.pop();o.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&C("isConstant",p.node,n,o)&&([m]=Ms(p.node.name,o)),n[p.node.name]==null){let f=wk(p.node,n,o,this._resourceManager);m||([m]=Ms(p.node.name,o));let d=o.currentContext;y.isPromise(f)?c.push(f.then(h=>(n[m]=h,o.currentContext=d,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u),h))):(n[m]=f,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u))}else this.processChildNodes(p.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=Ms(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!hr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!hr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Zr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((l,u)=>a[u]===-1||a[u]===l);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[o]!=null){let n=this._signature.inputs[o];t[n.name]=e[o]}else t[o]=e[o];return t}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Zr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[o]=Zr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Ck=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var A6="?tfjs-format=file",D6="model.json",fx=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Ck}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=vr.browserHTTPRequest(e,this.loadOptions);else{let 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Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var Md=class extends Kt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))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=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.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(e={}){if(G().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Md(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(o){throw new Error(`Error thrown while initializing video stream: ${o.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({freqDataQueue:e,timeDataQueue:t}))},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(e){let t=e[0].length,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Dr(o,t)}};var Ld=class extends Kt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=Bi([a,s,l,i],[1,4])}else this.cropBox=Bi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(G().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let o=new Ld(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.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(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Yh.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=sr(oe(e,"float32"),0),o;o=Ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return L(o,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var zd=class{};var bx=class extends Kt{split(e){return new $E(this,e)}},$E=class extends bx{constructor(e,t){super();this.upstream=e,this.impl=new RE(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},RE=class extends Rp{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var Dk=class extends Kt{decodeUTF8(){return new OE(this)}},OE=class extends bx{constructor(e){super();this.upstream=e,this.impl=new PE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},PE=class extends Rp{constructor(e){super();if(this.upstream=e,G().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=FE();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let o;return G().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var Bd=class extends Dk{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(G().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof 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extends zd{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return _x(this.url)?new Vd(this.url,this.fileOptions).iterator():ME(this.url,this.fileOptions)}};function LE(r,e={}){return new Pd(new Gd(r),e)}function zE(r){let e=Rd(r);return lo(async()=>e)}function BE(r){return lo(async()=>{let e=await r();return Rd(()=>e.next())})}async function VE(r,e){return Ld.create(r,e)}async function GE(r){return Md.create(r)}var wx="3.0.0";function ee(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var M6=Sr.whereImpl,Rk=class extends Ws{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ya(this,vs())}write(e,t,o){this.firstUse&&(this.firstUse=!1,G().get("IS_NODE")&&N.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.
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`),a=r.map(d=>O8(d,e,o)).join(`
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float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${r.texture2D}(textureSampler, uv).r;
}
`}function z8(r){return`
void setOutput(float val) {
${r.output} = vec4(val, 0, 0, 0);
}
`}function B8(r){return`
void setOutput(vec4 val) {
${r.output} = val;
}
`}function V8(r){return`${r.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${r.varyingFs} vec2 resultUV;
${r.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;
${r.defineSpecialNaN}
${r.defineSpecialInf}
${r.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;
}
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//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);
}
${fY}
${dY}
${hY}
`}var fY=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
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}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
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}
`,dY=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
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}
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vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,G8=`
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;function W$(){return`
int getOutputCoords() {
return 0;
}
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}
`:t[1]===1?`
int getOutputCoords() {
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}
`:`
int getOutputCoords() {
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int getOutputCoords() {
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int b = index / ${n};
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${a}
int b = index / ${n};
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${t}
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int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
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}
`}function pY(r,e){let t=zs(["r","c","d","d2","d3","d4"],r);return`
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int index = resTexRC.x * ${e[1]} + resTexRC.y;
${t}
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}
`}function iY(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
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}
`;let o=Math.ceil(r[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = 2 * (index / ${o});
int c = imod(index, ${o}) * 2;
return ivec2(r, c);
}
`}function mY(r,e){return y.arraysEqual(r,e)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
}
`:r[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:r[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.x * ${e[1]} + resTexRC.y;
int r = index / ${r[1]};
int c = index - r * ${r[1]};
return ivec2(r, c);
}
`}function lc(r){return`offset${r}`}function Y8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Mt();return`
vec4 ${t}() {
return ${o.texture2D}(${e}, halfCR);
}
`}function W8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
float ${t}() {
return sampleTexture(${e}, halfCR);
}
`;let[s,a]=r.shapeInfo.texShape,i=lc(e);return`
float ${t}() {
vec2 uv = uvFromFlat(${s}, ${a}, ${i});
return sampleTexture(${e}, uv);
}
`}function Z8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Mt();return`
vec4 ${t}(int index) {
vec2 uv = packedUVfrom1D(
${n[0]}, ${n[1]}, index);
return ${s.texture2D}(${e}, uv);
}
`}function U8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
float ${t}(int index) {
${Vp(r)}
}
`;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
float ${t}(int index) {
return sampleTexture(${e}, halfCR);
}
`;let a=lc(e);return s===1?`
float ${t}(int index) {
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
return sampleTexture(${e}, uv);
}
`:n===1?`
float ${t}(int index) {
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${e}, uv);
}
`:`
float ${t}(int index) {
vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
return sampleTexture(${e}, uv);
}
`}function J8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Mt();if(n!=null&&y.arraysEqual(e,n))return`
vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
return ${i.texture2D}(${t}, uv);
}
`;let l=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],u=Math.ceil(e[1]/2);return`
vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
return ${i.texture2D}(${t}, uv);
}
`}function j8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&y.arraysEqual(e,n)){let p=n[0],m=n[1];return`
float ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=Gp(r,i),m=["row","col"];return`
${Bp(p)}
float ${o}(int row, int col) {
return ${o}(${Wp(m,a)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
${Vp(r)}
}
`;let l=n[0],u=n[1],c=lc(t);return u===1?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${t}, uv);
}
`:l===1?`
float ${o}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${o}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${e[1]} + col + ${c};
vec2 uv = uvFromFlat(${l}, ${u}, index);
return sampleTexture(${t}, uv);
}
`}function Q8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Gp(r,p),d=["b","row","col"];return`
${G$(f)}
vec4 ${o}(int b, int row, int col) {
return ${o}(${Wp(d,m)});
}
`}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Mt();return`
vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${a}, ${i}, ${u}, ${l}, b, row, col);
return ${c.texture2D}(${t}, uv);
}
`}function H8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=Gp(r,l),h=["row","col","depth"];return`
${Bp(d)}
float ${o}(int row, int col, int depth) {
return ${o}(${Wp(h,i)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${n}, ${s}, 1)));
${Vp(r)}
}
`;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
float ${o}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${c}.0);
return sampleTexture(${t}, uv);
}
`;if(p===s&&m==null)return`
float ${o}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
return sampleTexture(${t}, uv);
}
`;let f=lc(t);return`
float ${o}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n} + col * ${s} + depth + ${f};
vec2 uv = uvFromFlat(${c}, ${p}, index);
return sampleTexture(${t}, uv);
}
`}function eY(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Mt();return`
vec4 ${n}(${p}) {
int index = ${m};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
return ${f.texture2D}(${o}, uv);
}
`}function q8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=Gp(r,i),h=["row","col","depth","depth2"];return`
${Bp(d)}
float ${o}(int row, int col, int depth, int depth2) {
return ${o}(${Wp(h,l)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${a}, ${s}, ${n}, 1)));
${Vp(r)}
}
`;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
float ${o}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${n}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`;if(m===n&&u==null)return`
float ${o}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${e[1]*e[2]}, ${e[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${p}.0);
return sampleTexture(${t}, uv);
}
`;let f=lc(t);return`
float ${o}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${s} +
depth * ${n} + depth2;
vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
return sampleTexture(${t}, uv);
}
`}function K8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=Gp(r,l),g=["row","col","depth","depth2","depth3"];return`
${Bp(h)}
float ${o}(int row, int col, int depth, int depth2, int depth3) {
return ${o}(${Wp(g,u)});
}
`}if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${a}, ${s}, ${n})) +
depth3;
${Vp(r)}
}
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${a}, ${s}, ${n}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;if(f===n&&c==null)return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]},
${e[2]*e[3]}, ${e[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${m}.0);
return sampleTexture(${t}, uv);
}
`;let d=lc(t);return`
float ${o}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${a} + depth * ${s} +
depth2 * ${n} + depth3 + ${d};
vec2 uv = uvFromFlat(${m}, ${f}, index);
return sampleTexture(${t}, uv);
}
`}function X8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=Gp(r,n),x=["row","col","depth","depth2","depth3","depth4"];return`
${Bp(g)}
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${o}(${Wp(x,s)});
}
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${u}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${a}, 1)));
${Vp(r)}
}
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${i}, ${a})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;if(d===a&&p==null)return`
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${e[1]*e[2]*e[3]*e[4]},
${e[2]*e[3]*e[4]},
${e[3]*e[4]},
${e[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${f}.0);
return sampleTexture(${t}, uv);
}
`;let h=lc(t);return`
float ${o}(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 * ${c} + col * ${u} + depth * ${l} +
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
vec2 uv = uvFromFlat(${f}, ${d}, index);
return sampleTexture(${t}, uv);
}
`}function Vp(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
for (int i = 0; i < ${t}; i++) {
if (i == index) {
return ${e}[i];
}
}
`}function tY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=B$(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,_)=>`coords.${p[_+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(h&&!x)a===1?f=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
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return vec4(outputValue.x);
`;else if(i.length){let b=s-2,_=s-1;i.indexOf(b)>-1&&i.indexOf(_)>-1?f="return vec4(outputValue.x);":i.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(_)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${n}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${o}(${m});
${f}
}
`}function rY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
float ${n}() {
return sampleTexture(${t}, resultUV);
}
`;let u=Le(l),c=B$(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
float ${n}() {
${u} coords = getOutputCoords();
${m}
return get${o}(${d});
}
`}function Le(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Gp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Wp(r,e){return e.map(t=>r[t]).join(", ")}function U$(r,e,t,o){let n=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},l=V$(s,i,n,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);G().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function j$(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function H$(r,e,t,o,n){j$(e.inShapeInfos,t),j$([e.outShapeInfo],[o]);let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),G().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(y.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),n!=null&&n(r,e.webGLProgram),r.executeProgram()}function q$(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${l}_${i}`});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n,s}var{addImpl:K$,bincountImpl:Rx,bincountReduceImpl:X$,ceilImpl:Y$,concatImpl:Z$,expImpl:J$,expm1Impl:Q$,floorImpl:eR,gatherV2Impl:tR,greaterImpl:rR,lessImpl:oR,linSpaceImpl:nR,logImpl:sR,maxImpl:iR,maximumImpl:aR,minimumImpl:lR,multiplyImpl:uR,negImpl:cR,prodImpl:pR,rangeImpl:mR,rsqrtImpl:fR,simpleAbsImpl:Fx,sliceImpl:dR,stridedSliceImpl:hR,subImpl:gR,tileImpl:xR,topKImpl:yR,transposeImpl:Up,uniqueImpl:bR}=Wk;function kv(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Ut(r,e){return e===1?[r]:kv(r,e)}function _R(r,e){if(r===1)return"rc";let t="";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=",");return t}var vv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let o=Ut("rc",t),n=Le(t),s=gY(t,e,o),a=xY(t,e[e.length-1],e[e.length-2],o),i=yY(e,o);this.userCode=`
void main() {
${n} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${i}));
}
}
`}}};function bY(r,e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function gY(r,e,t){if(r===1)return`rc > ${e[0]}`;let o="";for(let n=r-2;n<r;n++)o+=`${t[n]} >= ${e[n]}`,n<r-1&&(o+="||");return o}function xY(r,e,t,o){if(r===1)return"";let n=o.slice(-2);return`
int r = ${n[0]};
int c = ${n[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${e};
bool rEdge = rp1 >= ${t};
`}function yY(r,e){let t=r.length,o=bY(t,e);return t===1?`getA(rc),
rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${o[0]}),
cEdge ? 0. : getA(${o[1]}),
rEdge ? 0. : getA(${o[2]}),
rEdge || cEdge ? 0. : getA(${o[3]})`}var nh=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=`
${s}
${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${n}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${n>0?"}":""}
`}this.userCode=`
${_Y(t)}
${zp(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${o}
setOutput(result);
}
`}};function _Y(r){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${zs(["r","c","d"],r)}
return ivec3(r, c, d);
}
`}var Cv=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=kR(t,o),s=vR(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=wR(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===br.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===br.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===br.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===br.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=kR(o,n),a=vR(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=wR(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=G().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function wY(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function wR(r,e,t,o,n){let s=kY(e,o),a;if(n){let[l,u]=Xi(r[0],r[1]);a=l*u}else{let[l,u]=ic(r[0],r[1]);a=l*u}let i=wY(t,s);return a*i}function kY(r,e){switch(r){case br.PACKED_2X2_FLOAT32:return bv(e);case br.PACKED_2X2_FLOAT16:return _v(e);case br.UNPACKED_FLOAT32:return gv(e);case br.UNPACKED_FLOAT16:return xv(e);case br.PACKED_4X1_UNSIGNED_BYTE:return yv(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function vY(r){return G().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?br.PACKED_2X2_FLOAT32:br.UNPACKED_FLOAT32:r?br.PACKED_2X2_FLOAT16:br.UNPACKED_FLOAT16}function kR(r,e){if(r===Er.UPLOAD)return br.PACKED_2X2_FLOAT32;if(r===Er.RENDER||r==null)return vY(e);if(r===Er.DOWNLOAD||r===Er.PIXELS)return br.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function vR(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var uo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},gr="if (isnan(x)) return x;",CR="return x;",Iv="return abs(x);";var IR="return (x >= 0.0) ? x : (exp(x) - 1.0);",NR=gr+`
return (x < 0.0) ? 0.0 : x;
`,SR=gr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,sh="return x;";var TR="return x;",ER=`
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;
`,AR=`
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;
`,DR=`
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;
`,Bs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}};var Nv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Ut("rc",t),n=Le(t),s=_R(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${i}));
}
`}};var CY=Sr.whereImpl,IY=1e-7,NY=1e-4,Ox={};function SY(r){return r in Ox||(Ox[r]={}),Ox[r]}var TY=128,EY=600;function AY(){return G().global.screen==null?1024:G().global.screen.height*G().global.screen.width*window.devicePixelRatio*EY/1024/1024}var Sv=class extends Ws{constructor(e){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,!G().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Lo(G().getNumber("WEBGL_VERSION"));this.binaryCache=SY(G().getNumber("WEBGL_VERSION")),this.gpgpu=new wv(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Cv(this.gpgpu),this.numMBBeforeWarning=AY(),this.texData=new Ya(this,vs())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((G().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||G().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Er.UPLOAD,refCount:1,complexParentRefCount:0}),n}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}decComplexRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.complexParentRefCount>0&&t.refCount--}}move(e,t,o,n){if(G().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Er.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let o=this.texData.get(t);o.refCount--,o.refCount<1&&this.disposeData(t)}}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Bs(i,sh):m=new uo(i,sh);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Bs(n,sh):d=new uo(n,sh);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&G().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&G().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...Il(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!n$(o))throw G().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(G().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...Il(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=G().getBool("WEBGL_PACK")&&n===!0,i=a?Ax(t):t,l=a?new fv(i):new mv(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(G().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return G().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=vs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=TY){let o=this.getCPUBackend();return!G().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==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),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return CY(e.shape,t)}packedUnaryOp(e,t,o){let n=new Bs(e.shape,t);return this.compileAndRun(n,[e],o)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=Fx(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(G().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Iv,e.dtype);let t=new uo(e.shape,Iv);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return vs().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Nv(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vv(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Nl(e.shape),...Sl(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Nl(t),...Sl(t)],a=new nh(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Ax(n),i;o?i=new pv(a):i=new cv(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Cl.DENSE){let h=Il(e.outputShape);i.texShape=h.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(h=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(h.dataId);if(g.texture==null){if(!e.packedInputs&&y.sizeFromShape(h.shape)<=G().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:h.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=h.shape)}else if(!!g.isPacked!=!!e.packedInputs)h=g.isPacked?this.unpackTensor(h):this.packTensor(h),l.push(h),g=this.texData.get(h.dataId);else if(g.isPacked&&!ac(g.shape,h.shape)){let x=h,b=h.shape;h.shape=g.shape,h=this.packedReshape(h,b),l.push(h),g=this.texData.get(h.dataId),x.shape=b}return this.uploadToGPU(h.dataId),{shape:h.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=q$(e,u,c),m=this.getAndSaveBinary(p,()=>U$(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;if(f&&(d=this.startTimer()),H$(this.gpgpu,m,u,c,n),l.forEach(h=>this.disposeIntermediateTensorInfo(h)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!G().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let h=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),h}return a}compileAndRun(e,t,o,n,s=!1){o=o||t[0].dtype;let a=this.runWebGLProgram(e,t,o,n,s);return vs().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(G().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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=V(()=>{if(!G().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=G().getBool("DEBUG");G().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(G().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?IY:NY}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=x$(o,l),t.texShape=p),s!=null){let m=Ax(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Xi(p[0],p[1]),f=new hv(m,[h,d],g)):f=new dv(m,[h,d],g);let x=this.makeTensorInfo([h,d],n);g?this.texData.get(x.dataId).usage=Er.PIXELS:this.texData.get(x.dataId).usage=Er.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,_=this.runWebGLProgram(f,[x],n,null,b),w=this.texData.get(_.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(_.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=DY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};function DY(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var $R="3.0.0";Bc.isBrowser()&&yu("webgl",()=>new Sv,2);var Px=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`;var Qn=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}};var Tl=`
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;
`;var Vs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${Le(s)} coords = getOutputCoords();
`,s===1)a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let l=Ut("coords",s);a+=`
bool nextRowOutOfBounds =
(${l[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${l[s-1]} + 1) >= ${this.outputShape[s-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) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var RR={kernelName:us,backendName:"webgl",kernelFunc:jt};function co(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=t.texData.get(i.dataId);l.complexParentRefCount++;let u=jt({inputs:{x:n},backend:t}),c=t.texData.get(u.dataId);return c.complexParentRefCount++,a.complexTensorInfos={real:i,imag:u},s}var FR={kernelName:Hl,backendName:"webgl",kernelFunc:co};var Tv="return (a < 0.) ? b * a : a;",Ev=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function $Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(Ev,n.shape,a.shape):new Qn(Tv,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var OR={kernelName:sn,backendName:"webgl",kernelFunc:$Y};var Av="return (a < 0.) ? b * a : a;",Dv=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function RY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(Dv,o.shape,n.shape):new Qn(Av,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var PR={kernelName:yn,backendName:"webgl",kernelFunc:RY};var Mx="if (isnan(x)) return x;",MR=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,LR=`
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 ve({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=G().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Bs(a.shape,e):c=new uo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function it({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(_=>{let[w,v]=_,$={dataId:w.dataId,dtype:w.dtype,shape:l.shape},A={dataId:v.dataId,dtype:v.dtype,shape:u.shape},R=new Qn(r,l.shape,u.shape);return c.runWebGLProgram(R,[$,A],mr(w.dtype,v.dtype))}),b=co({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||mr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),_=c.texData.get(b.dataId);return _.values=g,b}let m=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Vs(e,l.shape,u.shape,t):f=new Qn(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function El(r,e=!1){if(r==="linear")return e?TR:CR;if(r==="relu")return e?AR:NR;if(r==="elu")return e?ER:IR;if(r==="relu6")return e?DR:SR;if(r==="prelu")return e?Dv:Av;if(r==="leakyrelu")return e?Ev:Tv;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var ih=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:u?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:g=`vec4 activation(vec4 x) {
${i}
}`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let _="rc.x",w="rc.x";e[0]<t[0]?_=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${g}
const float sharedDimension = ${p}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${p}; i++) {
int batchA = ${_};
int batchB = ${w};
vec4 a = getMatrixA(batchA, ${m});
vec4 b = getMatrixB(batchB, ${f});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${d[0]} * ${h[0]});
result += (${d[1]} * ${h[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${x}
setOutput(result);
}
`}};var $v={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Lx=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}};var zR="return a * b;";function Rv(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=N.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new Lx($v.REAL,o.shape,n.shape),c=new Lx($v.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=uR(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Vs(zR,o.shape,n.shape):a=new Qn(zR,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var BR={kernelName:dn,backendName:"webgl",kernelFunc:Rv};function VR(r,e,t){let o=[Nl(r.shape),...Sl(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Nl(e),...Sl(e)],a=new nh(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function ce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!ac(n.shape,l)&&!(c.texture!==null&&ac(c.shape,l))?VR(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var GR={kernelName:fs,backendName:"webgl",kernelFunc:ce};var zx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
if (inIdx < 0 || inIdx >= ${s}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${l===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${l===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${l===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}};var Fv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${l}(values, minMaxValue);
}
`,f="vec4";t==="all"?(i="1.0",m=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,f="bvec4"):t==="any"&&(i="0.0",m=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,f="bvec4");let d="";s%o>0&&(d=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${o};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===1}) {
${f} values = ${f}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${m}
} else if (${p===2}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${m}
} else if (${p===3}) {
${f} values = ${f}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${m}
}
setOutput(${u});
}
`}};function FY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Io(r,e,t,o){let n=FY(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new zx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new zx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Fv({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var Ov=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=OY(t);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function OY(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var Pv=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=kv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${l}) {
result[1] = ${u};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${o[this.rank-2]}) {
result[2] = ${u};
if(${l}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Al(r,e,t){let o=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pv(r.shape,e):new Ov(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function WR(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Al(r,l,o),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=ce({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=hu(r.dtype),_=Io(x,b,"sum",o),w=ce({inputs:{x:_},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(_),u&&o.disposeIntermediateTensorInfo(c),w}function ah(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return WR(n,s,a,t)}var UR={kernelName:Tn,backendName:"webgl",kernelFunc:ah};function Lt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=Up(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Al(n,s,a);return u}var jR={kernelName:Rn,backendName:"webgl",kernelFunc:Lt};var Mv=1e3;function uc({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),_=x===b||x===1||b===1;y.assert(u>=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let v=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let $=t?[x,p,f]:[x,f,p],A=o?[b,d,m]:[b,m,d],R=ce({inputs:{x:r},backend:n,attrs:{shape:$}}),M=ce({inputs:{x:e},backend:n,attrs:{shape:A}}),z=[R,M],W=Math.max(x,b),U=t?R.shape[1]:R.shape[2],q=s!=null,Z=a!=null,X=l==="leakyrelu",Y=l!=null?El(l,!0):null,te=q||Z||X||Y!=null,K;if((f===1||d===1)&&U>Mv&&te===!1){let ie=R,se=M;t&&(ie=Lt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),z.push(ie)),o&&(se=Lt({inputs:{x:M},backend:n,attrs:{perm:[0,2,1]}}),z.push(se));let pe=d!==1,ae=d===1,xe=ie;pe&&(xe=ce({inputs:{x:ie},backend:n,attrs:{shape:[W,U,1]}}),z.push(xe));let ge=d===1?2:1,_e=se;ae&&(_e=ce({inputs:{x:se},backend:n,attrs:{shape:[W,1,U]}}),z.push(_e));let ke=Rv({inputs:{a:xe,b:_e},backend:n});K=ah({inputs:{x:ke},backend:n,attrs:{axis:ge,keepDims:!0}}),z.push(ke)}else{let ie=mr(r.dtype,e.dtype),se=new ih($,A,[W,f,d],t,o,q,Y,Z,X),pe=[R,M];if(s!=null&&pe.push(s),Z&&pe.push(a),X){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));pe.push(ae),z.push(ae)}K=n.runWebGLProgram(se,pe,ie)}let re=ce({inputs:{x:K},backend:n,attrs:{shape:v}});z.push(K);for(let ie of z)n.disposeIntermediateTensorInfo(ie);return re}function PY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return uc({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var HR={kernelName:bs,backendName:"webgl",kernelFunc:PY};var qR="return abs(x);";function MY(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Fx(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Bs(o.shape,qR):n=new uo(o.shape,qR),t.runWebGLProgram(n,[o],o.dtype)}var KR={kernelName:ss,backendName:"webgl",kernelFunc:MY};var LY=gr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,zY=ve({opSnippet:LY}),XR={kernelName:qs,backendName:"webgl",kernelFunc:zY};var BY=gr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,VY=ve({opSnippet:BY}),YR={kernelName:Ks,backendName:"webgl",kernelFunc:VY};var ZR="return a + b;",GY=it({opSnippet:ZR,packedOpSnippet:ZR,supportsComplex:!0,cpuKernelImpl:K$}),JR={kernelName:xo,backendName:"webgl",kernelFunc:GY};var Lv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${o.join(`
`)}
float result = ${n};
setOutput(result);
}
`}};var zv=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${o.join(`
`)}
vec4 result = ${n};
setOutput(result);
}
`}};function Bx(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>G().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=Bx({inputs:o.slice(0,l),backend:t}),c=Bx({inputs:o.slice(l),backend:t});return Bx({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>mr(l,u)),s=o.map(l=>l.shape),i=G().getBool("WEBGL_PACK")?new zv(o[0].shape,s):new Lv(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var QR={kernelName:Uo,backendName:"webgl",kernelFunc:Bx};function WY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var eF={kernelName:Vl,backendName:"webgl",kernelFunc:WY};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var tF={kernelName:Gl,backendName:"webgl",kernelFunc:UY};var Bv=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"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 * ${n};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${n}; i++) {
int inIdx = ${l};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}};var Vv=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Ut("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=`
${R} sourceLocR = ${R}(${c.join()}, 0);
++${c[l-1]};
${R} sourceLocG = ${R}(${c.join()}, 0);
++${c[l-2]};
${R} sourceLocA = ${R}(${c.join()}, 0);
--${c[l-1]};
${R} sourceLocB = ${R}(${c.join()}, 0);
--${c[l-2]};`}else m=l,p=`
${u} sourceLocR = coords;
++${c[l-1]};
${u} sourceLocG = coords;
++${c[l-2]};
${u} sourceLocA = coords;
--${c[l-1]};
${u} sourceLocB = coords;
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Ut("sourceLocR",m-1).concat("inIdx.r"),x=Ut("sourceLocG",m-1).concat("inIdx.g"),b=Ut("sourceLocB",m-1).concat("inIdx.b"),_=Ut("sourceLocA",m-1).concat("inIdx.a"),w=o==="max"?"greaterThan":"lessThan",v=n?"":`
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${x.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${_.join()})));`,$=`vec4(
getAChannel(${g.join()}),
hasNextCol ? getAChannel(${x.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${_.join()}) : 0.)`,A=n?"":`
float getBestIndicesAChannel(${h.join()}) {
return getChannel(getBestIndicesA(${f.join()}),
vec2(${f.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${h.join()}) {
return getChannel(getA(${f.join()}),
vec2(${f.slice(-2).join()}));
}
${A}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
${p}
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
sourceLocB${d}, sourceLocA${d}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${$};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${v}
vec4 candidate = ${$};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${w}(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);
}
`}};function rF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Bv(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=rF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function oF(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Vv(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=oF(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Vx(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!G().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=ce({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=rF(r,u,o);s.push(c);let p=ce({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return oF(r,e,o)}function jY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Vx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var nF={kernelName:jo,backendName:"webgl",kernelFunc:jY};function HY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Lt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Vx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var sF={kernelName:oa,backendName:"webgl",kernelFunc:HY};var qY=gr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,KY=ve({opSnippet:qY}),iF={kernelName:Xs,backendName:"webgl",kernelFunc:KY};var XY=gr+"return log(x + sqrt(x * x + 1.0));",YY=ve({opSnippet:XY}),aF={kernelName:Ys,backendName:"webgl",kernelFunc:YY};var ZY=gr+`
return atan(x);
`,JY=ve({opSnippet:ZY}),lF={kernelName:Zs,backendName:"webgl",kernelFunc:JY};var QY=MR+`
return atan(a, b);
`,e7=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+LR+`
return result;
`,t7=it({opSnippet:QY,packedOpSnippet:e7}),uF={kernelName:Qs,backendName:"webgl",kernelFunc:t7};var r7=gr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,o7=ve({opSnippet:r7}),cF={kernelName:Js,backendName:"webgl",kernelFunc:o7};var Zi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${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
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.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 ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let _="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let v=Math.floor(a/4)*4,$=a%4,A=`
if (${h}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${_}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${f}, ${d});
const float initializationValue = ${b};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${b});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${p};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${A}
}
int xC = xCCorner + ${v};
if (${$===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${A}
} else if (${$===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${A}
} else if (${$===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${A}
}
}
setOutput(${w});
}
`}},cc=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let _=t==="avg",w="0.0";if(_||(w="-1.0 / 1e-20"),o){let z=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${b});
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 < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${m}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.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 ${z} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
wR * ${h} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let v="max",$=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&($="avgValue / count");let A=Math.floor(a/4)*4,R=a%4,M=`
if (${_}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${v}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${l}, ${u});
const ivec3 pads = ivec3(${g}, ${x}, ${b});
const float initializationValue = ${w};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${w});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${f};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d};
wR += ${p}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${A}; wC += 4) {
int xC = xCCorner + wC * ${m};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
);
${M}
}
int xC = xCCorner + ${A};
if (${R===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${M}
} else if (${R===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
initializationValue,
initializationValue
);
${M}
} else if (${R===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${m}, ch),
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
initializationValue
);
${M}
}
}
setOutput(${$});
}
}
`}};function n7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var pF={kernelName:Ho,backendName:"webgl",kernelFunc:n7};function s7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new cc(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var mF={kernelName:na,backendName:"webgl",kernelFunc:s7};var Gv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
const ivec2 pads = ivec2(${c}, ${p});
const float avgMultiplier = float(${m});
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 < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Wv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${g});
const float avgMultiplier = float(${x});
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 < ${p};
wD += ${l}) {
float dyD = float(dyDCorner + wD) / ${s}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${m};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${f};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function i7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Wv(m);return t.runWebGLProgram(f,[n],a.dtype)}var fF={kernelName:Ul,backendName:"webgl",kernelFunc:i7};function a7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Yi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new Gv(c);return t.runWebGLProgram(p,[n],a.dtype)}var dF={kernelName:Wl,backendName:"webgl",kernelFunc:a7};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return uc({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var hF={kernelName:qo,backendName:"webgl",kernelFunc:l7};var Uv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${l};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}};var jv=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${l};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}};var u7=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=G().getBool("WEBGL_PACK_NORMALIZATION")?new jv(o.shape,n.shape,s.shape,c,p,l):new Uv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},gF={kernelName:on,backendName:"webgl",kernelFunc:u7};var Hv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=c7(this.rank),s,a=e.map((i,l)=>`sourceLoc.${qv[l]} = start[${l}] + coords.${qv[l]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${a.join(`
`)}
`,this.userCode=`
${o}
void main() {
${s}
setOutput(getSource(${n}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},qv=["x","y","z","w","u","v"];function c7(r){if(r===1)return"sourceLoc";if(r<=6)return qv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Kv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Ut("coords",this.rank),n=Ut("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
result.x = ${a};
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.y = ${a};
--${n[this.rank-1]};
}
`,l=this.rank===1?"":`
--${o[this.rank-1]};
if (++${o[this.rank-2]} < ${e[this.rank-2]}) {
++${n[this.rank-2]};
result.z = ${a};
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.w = ${a};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${p}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${l}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function p7(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.complexParentRefCount=0,a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=or.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Ua(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=or.parseSliceParams(n,s,a);if(or.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=dR(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=or.isSliceContinous(n.shape,i,l);if(u||!c){let p=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kv(l):new Hv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),p7(n,i,l,t)}var xF={kernelName:hs,backendName:"webgl",kernelFunc:Ua};var m7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,_)=>b*_),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=ce({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Lt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ce({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Ua({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},yF={kernelName:sa,backendName:"webgl",kernelFunc:m7};function f7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=Rx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var bF={kernelName:jl,backendName:"webgl",kernelFunc:f7};var d7="return float(a != b);",Xv=it({opSnippet:d7,dtype:"bool"}),_F={kernelName:bi,backendName:"webgl",kernelFunc:Xv};function ja(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var wF={kernelName:cu,backendName:"webgl",kernelFunc:ja};var h7="return float(int(x));";function kF(r,e){let t=new uo(r.shape,h7),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Yv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=gt(n.shape),i=Yv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=co({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=ja({inputs:{input:n},backend:t}),i=Yv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return kF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=Xv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var vF={kernelName:Eo,backendName:"webgl",kernelFunc:Yv};var CF="return ceil(x);",g7=ve({opSnippet:CF,packedOpSnippet:CF,cpuKernelImpl:Y$}),IF={kernelName:ei,backendName:"webgl",kernelFunc:g7};var Zv=class{constructor(e){this.variableNames=["A"],this.outputShape=e,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(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var Jv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,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(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;G().getBool("WEBGL_PACK_CLIP")?i=new Jv(n.shape):i=new Zv(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var NF={kernelName:Ao,backendName:"webgl",kernelFunc:x7};var Qv=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,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))
);
}
`}};function SF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function y7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Qv(o.shape),a=[SF(o,n.complexTensorInfos.real),SF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var TF={kernelName:ia,backendName:"webgl",kernelFunc:y7};var eC=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${o.join(`
`)}
}
`}};var tC=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=Ut("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
return getChannel(
getT0(${p}), vec2(${c.join()}));
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
return getChannel(
getT${h}(${Gx(i,u,g)}),
vec2(${Gx(c,u,g)}));
}`}let f=l.length,d=l[l.length-1];m+=`
return getChannel(
getT${f}(${Gx(i,u,d)}),
vec2(${Gx(c,u,d)}));`,this.userCode=`
float getValue(${i.map(h=>"int "+h)}) {
${m}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[n-1]} = ${a[n-1]} + 1;
if (${a[n-1]} < ${o[n-1]}) {
result.g = getValue(${a});
}
${a[n-2]} = ${a[n-2]} + 1;
if (${a[n-2]} < ${o[n-2]}) {
result.a = getValue(${a});
}
${a[n-1]} = ${a[n-1]} - 1;
if (${a[n-2]} < ${o[n-2]} &&
${a[n-1]} < ${o[n-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Gx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function pc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var EF={kernelName:ou,backendName:"webgl",kernelFunc:pc};function mc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>ja({inputs:{input:d},backend:t})),c=r.map(d=>pc({inputs:{input:d},backend:t})),p=mc(u,e,t),m=mc(c,e,t),f=co({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=AF(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=Z$(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>G().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=mc(r.slice(0,u),e,t),p=mc(r.slice(u),e,t),m=mc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new tC(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=AF(r,e,t),a=new eC(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=ce({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function AF(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ce({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function rC(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),mc(i,s,t)}var DF={kernelName:is,backendName:"webgl",kernelFunc:rC};var lh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,_=g?3:1,w="",v="";o&&(n?w=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${o}
}`:s?w=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${o}
}`:w=`
float activation(float x) {
${o}
}
`,v="result = activation(result);");let $=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${w}
const ivec2 strides = ivec2(${l}, ${u});
const ivec2 pads = ivec2(${a}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${_}];
ivec2 xRCCorner =
ivec2(coords[${x}], coords[${b}]) * 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 < ${m}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${p};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${g}) {
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 (${h===1}) {
if (${g}) {
dotProd +=
getX(batch, xR, xC, ${d}) *
getW(wR, wC, ${d}, d2);
} else {
dotProd +=
getX(batch, ${d}, xR, xC) *
getW(wR, wC, ${d}, d2);
}
} else if (${h===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2)
);
if (${g}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${h===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${d}, d2),
getW(wR, wC, ${d} + 1, d2),
getW(wR, wC, ${d} + 2, d2)
);
if (${g}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${d}),
getX(batch, xR, xC, ${d} + 1),
getX(batch, xR, xC, ${d} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${d}, xR, xC),
getX(batch, ${d} + 1, xR, xC),
getX(batch, ${d} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${$}
${v}
setOutput(result);
}
`}},oC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${i});
const ivec3 pads = ivec3(${t}, ${o}, ${n});
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 < ${p}; wF++) {
int xF = xFCorner + wF * ${l};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${m}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${d}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${h===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${d}) *
getW(wF, wR, wC, ${d}, d2);
} else if (${h===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${h===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${d}),
getX(batch, xF, xR, xC, ${d} + 1),
getX(batch, xF, xR, xC, ${d} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${d}, d2),
getW(wF, wR, wC, ${d} + 1, d2),
getW(wF, wR, wC, ${d} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}};var nC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Mt(),x=m==="channelsLast",b=x?0:1,_=x?1:2,w="";for(let v=0;v<=1;v++)for(let $=0;$<=1;$++)w+=`
blockIndex = rc.y + ${$};
pos = rc.x + ${v};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${u})) * ${i} - ${d};
d0 = offsetY + ${p} * (pos / ${h});
if(d0 < ${t[b]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));
if(d1 < ${t[_]} && d1 >= 0) {
ch = int(mod(float(pos), ${s}.));
if (${x}) {
innerDims = vec2(d1, ch);
result[${v*2+$}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${v*2+$}] = 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;
${w}
${g.output} = result;
}
`}};function Wx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Mv,_=l[2]%2!=0&&!!u.isPacked;if(b||!G().getBool("WEBGL_LAZILY_UNPACK")||!G().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let w=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=ce({inputs:{x:r},backend:o,attrs:{shape:[1,w,t.inChannels]}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),A=uc({a:v,b:$,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ce({inputs:{x:A},backend:o,attrs:{shape:t.outShape}}),x.push(v),x.push($),x.push(A)}else{let w=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},$=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(ac(u.shape,v.shape),()=>`packed reshape ${u.shape} to ${v.shape} isn't free`);let A=ce({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(A);let R=uc({a:v,b:A,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),M=o.texData.get(R.dataId);y.assert(M.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=$,M.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}for(let w of x)o.disposeIntermediateTensorInfo(w);return g}function Ux({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,_=!1,w=[],v=ce({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),$=ce({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});w.push(v),w.push($);let A=new nC(x,v.shape,t),R=o.runWebGLProgram(A,[v],"float32"),M=ce({inputs:{x:R},backend:o,attrs:{shape:[1,x[0],x[1]]}});w.push(R),w.push(M);let z=n!=null,W=s!=null,U=i==="leakyrelu",q=i?El(i,!0):null,Z=new ih(M.shape,$.shape,[1,g,t.outChannels],b,_,z,q,W,U),X=[M,$];if(n&&X.push(n),W&&X.push(s),U){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));X.push(re),w.push(re)}let Y=o.runWebGLProgram(Z,X,"float32"),te=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],K=ce({inputs:{x:Y},backend:o,attrs:{shape:te}});w.push(Y);for(let re of w)o.disposeIntermediateTensorInfo(re);return K}function b7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Wx({x:n,filter:s,convInfo:m,backend:t});else if(G().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=Ux({x:n,filter:s,convInfo:m,backend:t});else{let h=new lh(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=ce({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var $F={kernelName:Ko,backendName:"webgl",kernelFunc:b7};var sC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.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 < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
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);
}
`}},iC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${p}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - 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 < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
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);
}
`}},aC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.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 < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${s};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${o} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},lC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${l}, ${u}, ${c});
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 < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${s}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${o}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${o} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function _7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new sC(m);return t.runWebGLProgram(f,[n,s],"float32")}var RF={kernelName:ql,backendName:"webgl",kernelFunc:_7};function w7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new iC(m);return t.runWebGLProgram(f,[n,s],"float32")}var FF={kernelName:Xo,backendName:"webgl",kernelFunc:w7};function k7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new oC(u);return t.runWebGLProgram(c,[n,s],"float32")}var OF={kernelName:aa,backendName:"webgl",kernelFunc:k7};function v7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new aC(u);return t.runWebGLProgram(c,[n,s],"float32")}var PF={kernelName:Kl,backendName:"webgl",kernelFunc:v7};function C7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new lC(u);return t.runWebGLProgram(c,[n,s],"float32")}var MF={kernelName:Xl,backendName:"webgl",kernelFunc:C7};var I7=Mx+`
return cos(x);
`,N7=ve({opSnippet:I7}),LF={kernelName:Yo,backendName:"webgl",kernelFunc:N7};var S7=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,T7=ve({opSnippet:S7}),zF={kernelName:ti,backendName:"webgl",kernelFunc:T7};var uC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[_,w,v]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
const float height_ratio = float(${g});
const float width_ratio = float(${_});
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 >= ${a}) {
return;
}
float height_scale = ${x};
float width_scale = ${w};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${d} ) {
setOutput(float(${s}));
return;
}
float in_x = ${v};
if( in_x < 0.0 || in_x > ${h} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${f} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}};var E7=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new uC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},BF={kernelName:ri,backendName:"webgl",kernelFunc:E7};var jx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${VF(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${Le(n)} coords = getOutputCoords();
int end = ${GF(n,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${l};
${GF(n,"coords")} = idx;
val += getX(${VF(n,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function VF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function GF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function A7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Lt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=n.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new jx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new jx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Lt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var WF={kernelName:Zo,backendName:"webgl",kernelFunc:A7};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Rx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=X$(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var UF={kernelName:Yl,backendName:"webgl",kernelFunc:D7};var cC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + 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)"}};function $7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new cC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var jF={kernelName:oi,backendName:"webgl",kernelFunc:$7};var uh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";o&&(n?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${o}
}`:s?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${o}
}`:x=`
float activation(float x) {
${o}
}
`,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${c}, ${p});
const ivec2 pads = ivec2(${l}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${g};
int q = d2 - d1 * ${g};
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 < ${d}; wR++) {
int xR = xRCorner + wR * ${m};
if (xR < 0 || xR >= ${a}) {
continue;
}
for (int wC = 0; wC < ${h}; wC++) {
int xC = xCCorner + wC * ${f};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${_}
${b}
setOutput(result);
}
`}};var ch=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let v=0;v<d;v++)for(let $=0;$<h;$++)x+=`
vec4 xTexelR${v}C${$*2} = vec4(0.);
vec4 wR${v}C${$} = vec4(0.);
vec4 xR${v}C${$} = vec4(0.);`;for(let v=0;v<d;v++)for(let $=0;$<g;$++){let A=$*2;if(x+=`
xR = xRCorner + ${v*m};
xC = xCCorner + ${A*f};
`,p===1){if(A<h&&(u%2==1?x+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${v}C${A}.zw = vec2(0.);
}
} else {
xTexelR${v}C${A} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${v}C${A} = vec4(previous.zw, xTexelR${v}C${A}.xy);
} else {
xR${v}C${A} = vec4(0, 0, xTexelR${v}C${A}.xy);
}
`:x+=`
if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
xTexelR${v}C${A} = getX(batch, xR, xC, d1);
} else {
xTexelR${v}C${A} = vec4(0.);
}
xR${v}C${A} = xTexelR${v}C${A};
`,A+1<h)){let R=u%2==0?y.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(x+=`
xCOffset = xC + ${u%2} + ${R};
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${A+2} = getX(batch, xR, xCOffset, d1);
}
`,f>1&&(x+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${A} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${A} = vec4(0.);
}
`),x+=`
xR${v}C${A+1} = vec4(
xTexelR${v}C${A}.zw, xTexelR${v}C${A+2}.xy);
`):x+=`
xCOffset = xC + ${R};
if(xR >= 0 && xR < ${a} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${A+2} = getX(batch, xR, xCOffset, d1);
}
xR${v}C${A+1} = xTexelR${v}C${A+2};
`}}else A<h&&(x+=`
if(xR >= 0 && xR < ${a}) {
`,u%2==1?(x+=`
xCOffset = xC + 1 - ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${A} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${A} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${v}C${A+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${v}C${A+2} = vec4(0.);
}
xR${v}C${A} = vec4(
xTexelR${v}C${A}.zw, xTexelR${v}C${A+2}.zw);
`,A+1<h&&(x+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${v}C${A+1} = vec4(xTexelR${v}C${A+2}.xy, final.xy);
`)):(x+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${v}C${A} = getX(batch, xR, xC, d1);
} else {
xTexelR${v}C${A} = vec4(0.);
}
xCOffset = xC + ${p};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${A+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${A+2} = vec4(0.);
}
xR${v}C${A} = vec4(
xTexelR${v}C${A}.xy, xTexelR${v}C${A+2}.xy);
`,A+1<h&&(x+=`
xR${v}C${A+1} = vec4(
xTexelR${v}C${A}.zw, xTexelR${v}C${A+2}.zw);
`)),x+="}");A<h&&(x+=`
vec4 wTexelR${v}C${A} = getW(${v}, ${A}, d1, q);
wR${v}C${A} = vec4(wTexelR${v}C${A}.xz, wTexelR${v}C${A}.xz);
`,A+1<h&&(x+=`
vec4 wTexelR${v}C${A+1} = getW(${v}, ${A+1}, d1, q);
wR${v}C${A+1} =
vec4(wTexelR${v}C${A+1}.xz, wTexelR${v}C${A+1}.xz);`))}for(let v=0;v<d;v++)for(let $=0;$<h;$++)x+=`dotProd += xR${v}C${$} * wR${v}C${$};`;let b="",_="";o&&(n?b=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:s?b=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:b=`vec4 activation(vec4 x) {
${o}
}`,_="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${b}
const ivec2 strides = ivec2(${c}, ${p});
const ivec2 pads = ivec2(${l}, ${u});
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.);
${x}
vec4 result = dotProd;
${w}
${_}
setOutput(result);
}
`}};function R7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return G().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new ch(p):m=new uh(p),t.runWebGLProgram(m,[n,s],"float32")}var HF={kernelName:Jo,backendName:"webgl",kernelFunc:R7};var pC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.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 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${o} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},mC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${i});
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 < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${o}; wC++) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${o} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${l}; dm++) {
int d2 = d1 * ${l} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new pC(p);return t.runWebGLProgram(m,[n,s],"float32")}var qF={kernelName:Zl,backendName:"webgl",kernelFunc:F7};function O7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new mC(p);return t.runWebGLProgram(m,[n,s],"float32")}var KF={kernelName:Jl,backendName:"webgl",kernelFunc:O7};var fC=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function P7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=ce({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new fC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ce({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var XF={kernelName:Ql,backendName:"webgl",kernelFunc:P7};var dC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${p}, ${m});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${l}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${o}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function M7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new dC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=ce({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var YF={kernelName:la,backendName:"webgl",kernelFunc:M7};var L7="return (x >= 0.0) ? x : (exp(x) - 1.0);",z7=`
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;
`,B7=ve({opSnippet:L7,packedOpSnippet:z7}),ZF={kernelName:ni,backendName:"webgl",kernelFunc:B7};var V7="return (b >= 1.0) ? a : a * (b + 1.0);",G7=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,W7=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=G().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(G7,o.shape,n.shape):new Qn(V7,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},JF={kernelName:eu,backendName:"webgl",kernelFunc:W7};var U7=`
return vec4(equal(a, b));
`,j7="return float(a == b);",H7=it({opSnippet:j7,packedOpSnippet:U7,dtype:"bool"}),QF={kernelName:ii,backendName:"webgl",kernelFunc:H7};var q7=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,K7=ve({opSnippet:q7}),eO={kernelName:si,backendName:"webgl",kernelFunc:K7};var tO="return exp(x);",hC=ve({opSnippet:tO,packedOpSnippet:tO,cpuKernelImpl:J$}),rO={kernelName:en,backendName:"webgl",kernelFunc:hC};function Hx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),ce({inputs:{x:s},backend:o,attrs:{shape:i}})}var oO={kernelName:as,backendName:"webgl",kernelFunc:Hx};var nO="return exp(x) - 1.0;",X7=ve({opSnippet:nO,packedOpSnippet:nO,cpuKernelImpl:Q$}),sO={kernelName:ai,backendName:"webgl",kernelFunc:X7};var qx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${s};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${n});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${n}; 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) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function Kx(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=ce({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new qx("real",l,e),c=new qx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=co({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ce({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function Y7(r){let{inputs:e,backend:t}=r,{input:o}=e;return Kx(o,!1,t)}var iO={kernelName:tu,backendName:"webgl",kernelFunc:Y7};var gC=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function ph(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new gC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var aO={kernelName:ua,backendName:"webgl",kernelFunc:ph};var xC=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}};var lO={kernelName:li,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new xC(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var uO="return floor(x);",Z7=ve({opSnippet:uO,packedOpSnippet:uO,cpuKernelImpl:eR}),cO={kernelName:tn,backendName:"webgl",kernelFunc:Z7};var J7=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,Q7=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,eZ=it({opSnippet:J7,packedOpSnippet:Q7,dtype:"int32"}),pO={kernelName:rn,backendName:"webgl",kernelFunc:eZ};var yC=class{constructor(e){this.variableNames=["A"];let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}};var bC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Mt(),[o,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}.0, ${o}.0);
vec4 values = ${t.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);
}
}
${t.output} = result;
}
`}};var mO={kernelName:Fc,backendName:"webgl",kernelFunc:tZ},jp;function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[u,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],p=[c,u],m=[c,u,s];(i||a||l)&&(jp==null&&(jp=document.createElement("canvas").getContext("2d")),jp.canvas.width=u,jp.canvas.height=c,jp.drawImage(n,0,0,u,c),n=jp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Er.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),n);let d=G().getBool("WEBGL_PACK")?new bC(m):new yC(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function rZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=Wx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(G().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=Ux({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let w=a!=null,v=i!=null,$=f==="leakyrelu",A=f?El(f,!1):null,R=new lh(g,w,A,v,$),M=[n,s];if(a&&M.push(a),i&&M.push(i),$){let z=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));M.push(z),b.push(z)}x=t.runWebGLProgram(R,M,"float32")}let _=ce({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var fO={kernelName:_s,backendName:"webgl",kernelFunc:rZ};function oZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=G().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?El(m,x):null,_=[n,s],w=a!=null,v=i!=null,$=m==="leakyrelu";if(w&&_.push(a),v&&_.push(i),$){let M=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));_.push(M),d.push(M)}let A;x?A=new ch(g,w,b,v,$):A=new uh(g,w,b,v,$);let R=t.runWebGLProgram(A,_,"float32");return d.forEach(M=>t.disposeIntermediateTensorInfo(M)),R}var dO={kernelName:ws,backendName:"webgl",kernelFunc:oZ};var _C=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${n} strides = ${n}(${this.strides});
void main() {
${s} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function nZ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=ce({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=ce({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new _C(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=ce({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var hO={kernelName:ui,backendName:"webgl",kernelFunc:nZ};var wC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=sZ(e,2);this.userCode=`
void main() {
${o} resRC = getOutputCoords();
setOutput(getA(${n}));
}
`}};function sZ(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function iZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=ce({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=ce({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(f),_=t.bufferSync(m),w=tR(_,b,d);return p.forEach(v=>t.disposeIntermediateTensorInfo(v)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new wC(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=ce({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var gO={kernelName:ls,backendName:"webgl",kernelFunc:iZ};var aZ="return float(a > b);",lZ=`
return vec4(greaterThan(a, b));
`,uZ=it({opSnippet:aZ,packedOpSnippet:lZ,cpuKernelImpl:rR,dtype:"bool"}),xO={kernelName:ci,backendName:"webgl",kernelFunc:uZ};var cZ="return float(a >= b);",pZ=`
return vec4(greaterThanEqual(a, b));
`,mZ=it({opSnippet:cZ,packedOpSnippet:pZ,dtype:"bool"}),yO={kernelName:nn,backendName:"webgl",kernelFunc:mZ};function fZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Kx(o,!0,t)}var bO={kernelName:ru,backendName:"webgl",kernelFunc:fZ};var dZ="return float(!isnan(x) && !isinf(x));",hZ=ve({opSnippet:dZ,dtype:"bool"}),_O={kernelName:pi,backendName:"webgl",kernelFunc:hZ};var gZ="return float(isinf(x));",xZ=ve({opSnippet:gZ,dtype:"bool"}),wO={kernelName:mi,backendName:"webgl",kernelFunc:xZ};var yZ="return float(isnan(x));",bZ=ve({opSnippet:yZ,dtype:"bool"}),kO={kernelName:fi,backendName:"webgl",kernelFunc:bZ};var _Z="return float(a < b);",wZ=`
return vec4(lessThan(a, b));
`,kZ=it({opSnippet:_Z,packedOpSnippet:wZ,cpuKernelImpl:oR,dtype:"bool"}),vO={kernelName:di,backendName:"webgl",kernelFunc:kZ};var vZ="return float(a <= b);",CZ=`
return vec4(lessThanEqual(a, b));
`,IZ=it({opSnippet:vZ,packedOpSnippet:CZ,dtype:"bool"}),CO={kernelName:hi,backendName:"webgl",kernelFunc:IZ};function NZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=nR(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var IO={kernelName:nu,backendName:"webgl",kernelFunc:NZ};var SZ=`if (x < 0.0) return NAN;
return log(x);`,TZ=`
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;
`,EZ=ve({opSnippet:SZ,packedOpSnippet:TZ,cpuKernelImpl:sR}),NO={kernelName:an,backendName:"webgl",kernelFunc:EZ};var AZ="return log(1.0 + x);",DZ=ve({opSnippet:AZ}),SO={kernelName:gi,backendName:"webgl",kernelFunc:DZ};var $Z="return float(a >= 1.0 && b >= 1.0);",RZ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,FZ=it({opSnippet:$Z,packedOpSnippet:RZ,dtype:"bool"}),TO={kernelName:xi,backendName:"webgl",kernelFunc:FZ};var OZ="return float(!(x >= 1.0));",PZ=ve({opSnippet:OZ}),EO={kernelName:Za,backendName:"webgl",kernelFunc:PZ};var MZ="return float(a >= 1.0 || b >= 1.0);",LZ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,zZ=it({opSnippet:MZ,packedOpSnippet:LZ,dtype:"bool"}),AO={kernelName:Ja,backendName:"webgl",kernelFunc:zZ};var kC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,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 = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${l};
setOutput(val);
}
`}};var vC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,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 - ${a};
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 = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
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 * ${l};
setOutput(result);
}
`}};var BZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=G().getBool("WEBGL_PACK_NORMALIZATION")?new vC(n.shape,s,a,i,l):new kC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},DO={kernelName:ca,backendName:"webgl",kernelFunc:BZ};var CC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,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 - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${n}) * norm + float(${o});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${n})
* float(${s})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${s});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}};var VZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new CC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},$O={kernelName:su,backendName:"webgl",kernelFunc:VZ};function RO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,r.dtype,"max",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function IC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let _=t.texData.get(f.dataId).values,w=new Array(i);for(let A=0;A<w.length;A++)w[A]=n.shape[c[A]];let v=Up(_,n.shape,n.dtype,c,w);f=t.makeTensorInfo(w,n.dtype);let $=t.texData.get(f.dataId);$.values=v}else f=Al(n,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let _=t.texData.get(f.dataId).values,w=iR(_,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let v=t.texData.get(x.dataId);v.values=w}else x=RO(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var FO={kernelName:ln,backendName:"webgl",kernelFunc:IC};var GZ=Px+`
return max(a, b);
`,WZ=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Tl+`
return result;
`,UZ=it({opSnippet:GZ,packedOpSnippet:WZ,cpuKernelImpl:aR}),OO={kernelName:un,backendName:"webgl",kernelFunc:UZ};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Yi(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Zi(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var PO={kernelName:cn,backendName:"webgl",kernelFunc:jZ};function HZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new cc(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var MO={kernelName:pa,backendName:"webgl",kernelFunc:HZ};var NC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${l});
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 < ${s};
wR += ${n}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},SC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
const ivec3 pads = ivec3(${p}, ${m}, ${f});
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 < ${l};
wD += ${s}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${o}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${d} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function qZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new cc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new SC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var LO={kernelName:au,backendName:"webgl",kernelFunc:qZ};function KZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Yi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Zi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new NC(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var zO={kernelName:iu,backendName:"webgl",kernelFunc:KZ};function BO(r,e,t,o){let n=new Zi(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Zi(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var VO={kernelName:lu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=BO(o,i,c,l);return[p,m]}};function GO(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=ce({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=Io(i,"float32","mean",o),u=ce({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var WO={kernelName:pn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let w=a.texData.get(d.dataId).values,v=new Array(i);for(let R=0;R<v.length;R++)v[R]=o.shape[c[R]];let $=Up(w,o.shape,o.dtype,c,v);d=a.makeTensorInfo(v,o.dtype);let A=a.texData.get(d.dataId);A.values=$}else d=Al(o,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),x=h;n&&(x=N.expandShapeToKeepDim(h,l));let b=GO(d,g,x,a);for(let _ of f)a.disposeIntermediateTensorInfo(_);return b}};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=Io(h,h.dtype,"min",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=ce({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=ce({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var UO={kernelName:mn,backendName:"webgl",kernelFunc:XZ};var YZ=Px+`
return min(a, b);
`,ZZ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Tl+`
return result;
`,JZ=it({opSnippet:YZ,packedOpSnippet:ZZ,cpuKernelImpl:lR}),jO={kernelName:fn,backendName:"webgl",kernelFunc:JZ};var TC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${n}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${s} coords = outC - start;
setOutput(getX(${l}));
}
`}};var EC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${m};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${m};
}
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[n-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
`}else{let d=`
${s} source = rc;
${s} lt = ${s}(lessThan(source, start));
${s} gte = ${s}(greaterThanEqual(source, end));
${s} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${m}) +
gte * ((end - 1) * 2 - source + ${m});
source -= start;
`;f=`
${s} rc = outputLoc;
${d}
result[0] = getChannel(getX(${u.join()}), ${p});
${l[n-1]} += 1;
if(${c}) {
${d}
result[1] = getChannel(getX(${u.join()}), ${p});
}
rc = outputLoc;
${l[n-2]} += 1;
if(${l[n-2]} < ${this.outputShape[n-2]}) {
${d}
result[2] = getChannel(getX(${u.join()}), ${p});
${l[n-1]} += 1;
if(${c}) {
${d}
result[3] = getChannel(getX(${u.join()}), ${p});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${f}
setOutput(result);
}
`}};var QZ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new EC(o.shape,n,s):new TC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},HO={kernelName:ma,backendName:"webgl",kernelFunc:QZ};var e9=`if (b == 0.0) return NAN;
return mod(a, b);`,t9=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Tl+`
return result;
`,r9=it({opSnippet:e9,packedOpSnippet:t9}),qO={kernelName:yi,backendName:"webgl",kernelFunc:r9};var AC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],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 < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var o9=`
if (a == b) {
return 1.0;
};
return a / b;`,n9=`
// 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;
`,DC=it({opSnippet:o9,packedOpSnippet:n9,checkOutOfBounds:!0}),KO={kernelName:Qo,backendName:"webgl",kernelFunc:DC};var XO="return a - b;",$C=it({opSnippet:XO,packedOpSnippet:XO,supportsComplex:!0,cpuKernelImpl:gR}),YO={kernelName:Dn,backendName:"webgl",kernelFunc:$C};function RC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=IC({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=ce({inputs:{x:i},backend:t,attrs:{shape:l}}),c=$C({inputs:{a:n,b:u},backend:t}),p=hC({inputs:{x:c},backend:t}),m=ah({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ce({inputs:{x:m},backend:t,attrs:{shape:l}}),d=DC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var ZO={kernelName:En,backendName:"webgl",kernelFunc:RC};function s9(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:RC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new AC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var JO={kernelName:uu,backendName:"webgl",kernelFunc:s9};var QO="return -x;";function i9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=cR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return G().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Bs(o.shape,QO):n=new uo(o.shape,QO),t.runWebGLProgram(n,[o],o.dtype)}var eP={kernelName:cs,backendName:"webgl",kernelFunc:i9};var a9=Sr.nonMaxSuppressionV3Impl;function l9(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=a9(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var tP={kernelName:_i,backendName:"webgl",kernelFunc:l9};var u9=Sr.nonMaxSuppressionV4Impl;function c9(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=u9(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var rP={kernelName:wi,backendName:"webgl",kernelFunc:c9};var p9=Sr.nonMaxSuppressionV5Impl;function m9(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=p9(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var oP={kernelName:ki,backendName:"webgl",kernelFunc:m9};var FC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${n}), float(${o}),
float(index == coords.y)));
}
`}};var f9=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new FC(l,s,a,i),c=ce({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=ce({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},nP={kernelName:hn,backendName:"webgl",kernelFunc:f9};function mh(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=mh({inputs:{x:n},backend:t}),a=pc({inputs:{input:o},backend:t}),i=mh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return ph({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var sP={kernelName:ys,backendName:"webgl",kernelFunc:mh};function iP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=ja({inputs:{input:o},backend:t}),s=iP({inputs:{x:n},backend:t}),a=pc({inputs:{input:o},backend:t}),i=mh({inputs:{x:a},backend:t}),l=co({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return ph({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var aP={kernelName:ps,backendName:"webgl",kernelFunc:iP};function d9(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Hx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Hx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=rC({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var lP={kernelName:ms,backendName:"webgl",kernelFunc:d9};var OC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
int start = ${a};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${o}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${i});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${o}));
} else {
${s} coords = outC - start;
setOutput(getX(${l}));
}
}
`}};var PC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
if(${c}) {
`,n===1?"":`}
rc = outputLoc;
${l[n-2]} += 1;
if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${l[n-1]} += 1;
if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h<g;h++)d+=`
${m[h]}
if (${f}) {
result[${h}] = float(${o});
} else {
${s} source = rc - start;
result[${h}] = getChannel(getX(${u.join()}), ${p});
}
`;d+=n===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${i});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}};var MC=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PC(n.shape,s,a):new OC(n.shape,s,a);return t.runWebGLProgram(i,[n],n.dtype)},uP={kernelName:gn,backendName:"webgl",kernelFunc:MC};var h9=`
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);
`,g9=`
// 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));
`+Tl+`
return result;
`,x9=it({opSnippet:h9,packedOpSnippet:g9}),cP={kernelName:xn,backendName:"webgl",kernelFunc:x9};function y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Lt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=pR(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=ce({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=hu(n.dtype),_=Io(x,b,"prod",t);f=ce({inputs:{x:_},backend:t,attrs:{shape:d}}),l.push(x),l.push(_)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=ce({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var pP={kernelName:vi,backendName:"webgl",kernelFunc:y9};var LC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=mR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},mP={kernelName:fa,backendName:"webgl",kernelFunc:LC};var b9="return 1.0 / x;",_9=ve({opSnippet:b9}),fP={kernelName:Ci,backendName:"webgl",kernelFunc:_9};var w9=gr+`
return (x < 0.0) ? 0.0 : x;
`,k9=`
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;
`,v9=ve({opSnippet:w9,packedOpSnippet:k9}),dP={kernelName:bn,backendName:"webgl",kernelFunc:v9};var C9=gr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,I9=`
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;
`,N9=ve({opSnippet:C9,packedOpSnippet:I9}),hP={kernelName:wn,backendName:"webgl",kernelFunc:N9};var zC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${m};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
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);
}
`}};var BC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/p[0]},
${c[1]/p[1]},
${c[1]/p[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
${l}.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 = ${m};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${o-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);
}
`}};function S9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=G().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new BC(n.shape,l,u,s,a):new zC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var gP={kernelName:_n,backendName:"webgl",kernelFunc:S9};var VC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*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(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// 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 >= ${a}) {
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 >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${n-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), ${s-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);
}
`}};function T9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new VC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var xP={kernelName:mu,backendName:"webgl",kernelFunc:T9};var GC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/p[0]},
${c[1]/p[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${f};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function E9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new GC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var yP={kernelName:da,backendName:"webgl",kernelFunc:E9};var WC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${p});
const float invHeightScale = float(${m});
const float invWidthScale = float(${f});
const int winHeight = int(${d});
const int winWidth = int(${h});
// 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 >= ${a}) {
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 >= ${i}) {
continue;
}
float sourceFracRow =
float(${l[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${l[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${n}) - 1),
${o} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${o} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function A9(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new WC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var bP={kernelName:pu,backendName:"webgl",kernelFunc:A9};var UC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Le(o);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}};var jC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Ut("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${s}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${l(n.slice())};
if(${s}){
result.g = ${u(n.slice())};
}
if(${a}) {
result.b = ${c(n.slice())};
if(${s}) {
result.a = ${p(n.slice())};
}
}
setOutput(result);
}
`;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,_)=>f(_,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function D9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=G().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jC(n.shape,i):new UC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var _P={kernelName:kn,backendName:"webgl",kernelFunc:D9};var HC=class{constructor(e,t,o,n){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=N.getImageCenter(n,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof o=="number"?f=`float outputValue = ${o.toFixed(2)};`:f=`
vec3 fill = vec3(${o.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) - ${p}) * ${l} - (float(y) - ${m}) * ${i};
float coordYFloat = (float(x) - ${p}) * ${i} + (float(y) - ${m}) * ${l};
int coordX = int(round(coordXFloat + ${p}));
int coordY = int(round(coordYFloat + ${m}));
${f}
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${s}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}};var wP={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new HC(o.shape,n,s,a);return i.runWebGLProgram(l,[o],o.dtype)}};var $9=`
// 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;
}
}
`,R9=ve({opSnippet:$9}),kP={kernelName:vn,backendName:"webgl",kernelFunc:R9};var F9="return inversesqrt(x);",O9=ve({opSnippet:F9,cpuKernelImpl:fR}),vP={kernelName:Cn,backendName:"webgl",kernelFunc:O9};var fh=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
${l} strides = ${l}(${s});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${p});
flattenedIndex += index * ${d};
}
if (flattenedIndex == coords[0]) {
sum += ${f};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function P9(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=ce({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=ce({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new fh(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=ce({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var CP={kernelName:Ii,backendName:"webgl",kernelFunc:P9};var qC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${n});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function M9(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new qC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],mr(n.dtype,s.dtype))}var IP={kernelName:ds,backendName:"webgl",kernelFunc:M9};var L9=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,z9=ve({opSnippet:L9}),NP={kernelName:Ni,backendName:"webgl",kernelFunc:z9};var B9="return 1.0 / (1.0 + exp(-1.0 * x));",V9=ve({opSnippet:B9}),SP={kernelName:Nn,backendName:"webgl",kernelFunc:V9};var G9=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,W9=ve({opSnippet:G9}),TP={kernelName:Ti,backendName:"webgl",kernelFunc:W9};var U9=Mx+`
return sin(x);
`,j9=ve({opSnippet:U9}),EP={kernelName:In,backendName:"webgl",kernelFunc:j9};var H9=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,q9=ve({opSnippet:H9}),AP={kernelName:Si,backendName:"webgl",kernelFunc:q9};var K9=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
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return x > 0.0 ? 1.0 : float(${e.alpha});
`,s=new uo(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var zP={kernelName:Do,backendName:"webgl",kernelFunc:nJ};var KC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Le(o.length),a=Le(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
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setOutput(getX(${i}));
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float e2x = exp(-2.0 * abs(x));
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setOutput(getA(${s}));
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sumValue += dot(values, segFilter);
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if (inIdx < 0 || inIdx >= ${s}) {
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if (inIdx < 0 || inIdx >= ${s}) {
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const float initializationValue = ${l};
float getValue(int batch, int inIdx) {
${f}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${d}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${o}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; 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
);
${m}
}
int inIdx = inOffset + ${c};
if (${p===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
);
${m}
} else if (${p===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
);
${m}
} else if (${p===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
);
${m}
}
setOutput(${u});
}
`}};function dJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=N.getAxesPermutation([u],i),p=n;c!=null&&(p=Lt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=N.getInnerMostAxes(1,i)[0]);let m=N.segment_util.computeOutShape(p.shape,u,a),f=y.sizeFromShape([p.shape[u]]),d=ce({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=hu(n.dtype),g=(w,v,$,A,R)=>{let M=w.shape[0],z=w.shape[1],W=N.segment_util.segOpComputeOptimalWindowSize(z,R),U={windowSize:W,inSize:z,batchSize:M,numSegments:R},q=new ZC(U,v),Z=t.compileAndRun(q,[w,$],A);if(l.push(Z),Z.shape[1]===R)return Z;let X=LC({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),Y=YC({inputs:{x:X},backend:t,attrs:{reps:[z/W]}});return l.push(X),l.push(Y),g(Z,v,Y,A,R)},x=g(d,"unsortedSegmentSum",s,h,a),b=ce({inputs:{x},backend:t,attrs:{shape:m}}),_=b;if(c!=null){l.push(b);let w=N.getUndoAxesPermutation(c);_=Lt({inputs:{x:_},backend:t,attrs:{perm:w}})}return 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Please use 'channelsLast'.`);let W=o.makeOutput(f.outShape,"float32"),U=o.dataIdMap.get(W.dataId).id;return NM(a,n.shape[0],n.shape[1],n.shape[2],i,d,h,g,x,b,_,z,w,v,$,A,R,M,U),W}var SM={kernelName:Jo,backendName:"wasm",setupFunc:WJ,kernelFunc:UJ};var jJ=!1,TM=_t(ii,jJ,"bool");var EM=Rt(en);function Xx(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),l=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+s+1),i.splice(l,0,1),zr({inputs:{x:n},backend:o,attrs:{shape:i}})}var AM={kernelName:as,backendName:"wasm",kernelFunc:Xx};function HJ(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var DM={kernelName:ua,backendName:"wasm",kernelFunc:HJ};var $M;function qJ(r){$M=r.wasm.cwrap(li,null,["number","number","number","number","number","number"])}function KJ(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,l,u,c]=o.shape;return $M(s,i,l,u,c,a),n}var RM={kernelName:li,backendName:"wasm",kernelFunc:KJ,setupFunc:qJ};var FM=Rt(tn);var XJ=!1,OM=_t(rn,XJ);var PM;function YJ(r){PM=r.wasm.cwrap(on,null,["number","number","number","number","number","number","number"])}function ZJ(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:l,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,p=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,f=l!=null?e.dataIdMap.get(l.dataId).id:0,d=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return PM(c,p,m,f,d,n,g),h}var MM={kernelName:on,backendName:"wasm",setupFunc:YJ,kernelFunc:ZJ};var LM;function JJ(r){LM=r.wasm.cwrap(_s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function QJ(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=N.computeConv2DInfo(n.shape,s.shape,l,c,u,m),g=Dl[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,_=h.outChannels,w=0;if(a!=null){let ae=o.dataIdMap.get(a.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${_})`);w=ae.id}let v=h.filterHeight,$=h.filterWidth,A=h.padInfo.top,R=h.padInfo.right,M=h.padInfo.bottom,z=h.padInfo.left,W=h.dilationHeight,U=h.dilationWidth,q=h.strideHeight,Z=h.strideWidth,X=h.inChannels,Y=h.padInfo.type==="SAME"?1:0,te=h.batchSize,K=h.inHeight,re=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. 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xL={kernelName:ki,backendName:"wasm",setupFunc:TQ,kernelFunc:EQ};var AQ=!1,yL=_t(bi,AQ,"bool");var bL;function DQ(r){bL=r.wasm.cwrap(hn,null,["number","number","number","number","number"])}function $Q(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=t.makeOutput([...n.shape,s],"int32"),u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(n.dataId).id;return bL(p,s,a,i,u),l}var _L={kernelName:hn,backendName:"wasm",setupFunc:DQ,kernelFunc:$Q};function RQ(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var wL={kernelName:ps,backendName:"wasm",kernelFunc:RQ};function FQ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Xx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(l=>{y.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===l.dtype,()=>"All tensors passed to stack must have matching 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EL={kernelName:vi,backendName:"wasm",setupFunc:BQ,kernelFunc:VQ};var GQ=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=qd(o,n,s,a),l=e.makeOutput([i.length],a);return e.typedArrayFromHeap(l).set(i),l},AL={kernelName:fa,backendName:"wasm",kernelFunc:GQ};var WQ=!0,DL=_t(Qo,WQ);var $L=Rt(bn);var RL=Rt(wn);var FL;function UQ(r){FL=r.wasm.cwrap(_n,null,["number","number","number","number","number","number","number","number","number","number"])}function jQ(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,[c,p,m,f]=n.shape,d=[c,l,u,f],h=e.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=dc({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let x=h.id,b=e.makeOutput(d,"float32");if(y.sizeFromShape(n.shape)===0)return b;let _=e.dataIdMap.get(b.dataId).id;return FL(x,c,p,m,f,l,u,s?1:0,a?1:0,_),g!=null&&e.disposeData(g.dataId),b}var 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For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})}})}function vee(r,e){switch(e){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${e}`)}}var Iee=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Qx=null,hh=null,dh={},gh=!1,iI=!1;function Nee(r,e=!1){if(rg("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),gh)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Qx=r,iI=e}function See(r,e=!1){if(gh)throw new Error("The WASM backend was already initialized. 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Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}iI=e}var Tee="3.0.0";var Eee=2;yu("wasm",async()=>{let{wasm:r}=await vz();return new Jx(r)},Eee);export{ss as Abs,qs as Acos,Ks as Acosh,tp as AdadeltaOptimizer,rp as AdagradOptimizer,op as AdamOptimizer,np as AdamaxOptimizer,xo as Add,Uo as AddN,Vl as All,Gl as Any,jo as ArgMax,oa as ArgMin,Xs as Asin,Ys as Asinh,Zs as Atan,Qs as Atan2,Js as Atanh,Ho as AvgPool,na as AvgPool3D,Ul as AvgPool3DGrad,Wl as AvgPoolGrad,Jx as BackendWasm,qo as BatchMatMul,sa as BatchToSpaceND,jl as Bincount,bb as BroadcastTo,Yg as Callback,$g as CallbackList,Eo as Cast,ei as Ceil,Ao as ClipByValue,Hl as Complex,ia as ComplexAbs,is as Concat,Ko as Conv2D,ql as Conv2DBackpropFilter,Xo as Conv2DBackpropInput,aa as Conv3D,Kl as Conv3DBackpropFilterV2,Xl as Conv3DBackpropInputV2,Yo as Cos,ti as Cosh,ri as CropAndResize,Zo as Cumsum,Fg as CustomCallback,Ya as DataStorage,Yl as DenseBincount,oi as DepthToSpace,Jo as DepthwiseConv2dNative,Zl as DepthwiseConv2dNativeBackpropFilter,Jl as DepthwiseConv2dNativeBackpropInput,Ql as Diag,la as Dilation2D,Rc as Dilation2DBackpropFilter,$c as Dilation2DBackpropInput,gb as ENV,Jg as EarlyStopping,ni as Elu,eu as EluGrad,zh as Environment,ii as Equal,si as Erf,en as Exp,as as ExpandDims,ai as Expm1,tu as FFT,ua as Fill,li as FlipLeftRight,tn as Floor,rn as FloorDiv,Fc as FromPixels,on as FusedBatchNorm,_s as FusedConv2D,ws as FusedDepthwiseConv2D,ui as GatherNd,ls as GatherV2,fx as GraphModel,ci as Greater,nn as GreaterEqual,Rg as History,ru as IFFT,us as Identity,ou as Imag,Et as InputSpec,pi as IsFinite,mi as IsInf,fi as IsNan,Ws as KernelBackend,ca as LRN,su as LRNGrad,kf as LayerVariable,vo as LayersModel,sn as LeakyRelu,di as Less,hi as LessEqual,nu as LinSpace,an as Log,gi as Log1p,_b as LogSoftmax,xi as LogicalAnd,Za as LogicalNot,Ja as LogicalOr,ln as Max,cn as MaxPool,pa as MaxPool3D,au as MaxPool3DGrad,iu as MaxPoolGrad,lu as MaxPoolWithArgmax,un as Maximum,pn as Mean,mn as Min,fn as Minimum,ma as MirrorPad,yi as Mod,sp as MomentumOptimizer,uu as Multinomial,dn as Multiply,cs as Neg,_i as NonMaxSuppressionV3,wi as NonMaxSuppressionV4,ki as NonMaxSuppressionV5,bi as NotEqual,PI as OP_SCOPE_SUFFIX,hn as OneHot,ps as OnesLike,Fr as Optimizer,ms as Pack,gn as PadV2,p3 as Pool,xn as Pow,yn as Prelu,vi as Prod,ip as RMSPropOptimizer,ao as RNN,fa as Range,Ib as Rank,cu as Real,Qo as RealDiv,Ci as Reciprocal,Gt as Reduction,bn as Relu,wn as Relu6,fs as Reshape,_n as ResizeBilinear,mu as ResizeBilinearGrad,da as ResizeNearestNeighbor,pu as ResizeNearestNeighborGrad,kn as Reverse,Ri as RotateWithOffset,vn as Round,Cn as Rsqrt,ll as SGDOptimizer,Ii as ScatterNd,ds as Select,Ni as Selu,Hi as Sequential,Nn as Sigmoid,Ti as Sign,In as Sin,Si as Sinh,hs as Slice,En as Softmax,Ei as Softplus,ha as SpaceToBatchND,fu as SparseToDense,gs as SplitV,Sn as Sqrt,ga as Square,An as SquaredDifference,Do as Step,Ai as StridedSlice,Dn as Sub,Tn as Sum,Lr as SymbolicTensor,Di as Tan,$n as Tanh,Ve as Tensor,ct as TensorBuffer,yo as Tile,$i as TopK,Rn as Transpose,du as Unique,xs as Unpack,xa as UnsortedSegmentSum,tl as Variable,ys as ZerosLike,bs as _FusedMatMul,Tt as abs,vm as acos,Cm as acosh,Q as add,t_ as addN,_u as all,nl as any,sl as argMax,Im as argMin,Nm as asin,Sm as asinh,Tm as atan,Em as atan2,Am as atanh,va as avgPool,Dm as avgPool3d,e_ as backend,N as backend_util,uG as basicLSTMCell,On as batchNorm,s_ as batchNorm2d,i_ as batchNorm3d,a_ as batchNorm4d,Ca as batchToSpaceND,l_ as bincount,xU as booleanMaskAsync,il as broadcastTo,Yh as browser,Ce as buffer,E1 as callbacks,oe as cast,$m as ceil,nr as clipByValue,$o as clone,bo as complex,Je as concat,u_ as concat1d,c_ as concat2d,p_ as concat3d,m_ as concat4d,nw as constraints,vu as conv1d,jr as conv2d,Cu as conv2dTranspose,Rm as conv3d,AG as conv3dTranspose,d3 as copyRegisteredKernels,Ia as cos,Iu as cosh,nf as cosineWindow,Nu as cumsum,Hr as customGrad,$k as data,f_ as denseBincount,rg as deprecationWarn,Fm as depthToSpace,Cs as depthwiseConv2d,$1 as deregisterOp,Bc as device_util,LG as diag,Om as dilation2d,kV as disableDeprecationWarnings,Te as dispose,vV as disposeVariables,de as div,Pm as divNoNan,d_ as dot,B_ as dropout,Is as elu,wV as enableDebugMode,_V as enableProdMode,V_ as enclosingPowerOfTwo,vs as engine,G as env,_o as equal,Mm as erf,Xt as exp,sr as expandDims,Lm as expm1,Kc as eye,Ra as fft,Na as fill,EV as findBackend,AV as findBackendFactory,Ns as floor,bu as floorDiv,zn as fused,Pn as gather,z_ as gatherND,Zh as gather_util,SV as getBackend,Bh as getGradient,Pc as getKernel,xm as getKernelsForBackend,fW as grad,dW as grads,Qt as greater,to as greaterEqual,zi as ifft,Su as imag,Ds as image,NU as inTopKAsync,uw as initializers,Kg as input,vr as io,zu as irfft,h_ as isFinite,g_ as isInf,x_ as isNaN,At as keep,Sr as kernel_impls,jw as layers,Sa as leakyRelu,Tu as less,Oo as lessEqual,H_ as linalg,y_ as linspace,iE as loadGraphModel,g1 as loadLayersModel,zm as localResponseNormalization,ir as log,Eu as log1p,b_ as logSigmoid,Au as logSoftmax,Vm as logSumExp,fr as logicalAnd,Ta as logicalNot,Du as logicalOr,v_ as logicalXor,yj as losses,We as matMul,fN as math,ar as max,Ea as maxPool,Gm as maxPool3d,C_ as maxPoolWithArgmax,qr as maximum,ht as mean,jc as memory,Xw as metrics,Li as min,Ts as minimum,Wm as mirrorPad,Um as mod,d1 as model,Yw as models,Xc as moments,_U as movingAverage,O as mul,WW as multiRNNCell,I_ as multinomial,je as neg,sf as nextFrame,Gu as norm,Ln as notEqual,ks as oneHot,Cr as ones,er as onesLike,S as op,KW as outerProduct,$r as pad,ZW as pad1d,QW as pad2d,t4 as pad3d,o4 as pad4d,N_ as pool,Rr as pow,Da as prelu,Wb as print,$u as prod,CV as profile,m4 as rand,_4 as randomGamma,ug as randomNormal,Es as randomUniform,Zc as range,NV as ready,al as real,Hm as reciprocal,yu as registerBackend,x1 as registerCallbackConstructor,kb as registerGradient,Qa as registerKernel,D1 as registerOp,Zw as regularizers,Ir as relu,Fu as relu6,TV as removeBackend,L as reshape,qt as reverse,E4 as reverse1d,D4 as reverse2d,R4 as reverse3d,O4 as reverse4d,Fa as rfft,qm as round,Ou as rsqrt,le as scalar,L_ as scatterND,Jh as scatter_util,Pu as selu,Km as separableConv2d,h1 as sequential,J as serialization,EN as setBackend,DV as setPlatform,Nee as setWasmPath,See as setWasmPaths,O_ as setdiff1dAsync,Ur as sigmoid,Xm as sign,xj as signal,Mu as sin,Lu as sinh,Fe as slice,Ym as slice1d,cg as slice2d,Zm as slice3d,Jc as slice4d,or as slice_util,$a as softmax,Ss as softplus,Aa as spaceToBatchND,of as sparseToDense,gj as spectral,lr as split,xt as sqrt,Pe as square,Bu as squaredDifference,wo as squeeze,Bt as stack,As as step,Jm as stridedSlice,ue as sub,ye as sum,hu as sumOutType,Qm as tan,Mi as tanh,Dr as tensor,Vt as tensor1d,Bi as tensor2d,qb as tensor3d,aU as tensor4d,lU as tensor5d,uU as tensor6d,Fn as tensor_util,NN as test_util,V as tidy,Fo as tile,IV as time,ef as topk,ul as train,Ue as transpose,Vu as truncatedNormal,Qc as unique,f3 as unregisterGradient,m3 as unregisterKernel,tf as unsortedSegmentSum,ur as unstack,mr as upcastType,y as util,hW as valueAndGrad,gW as valueAndGrads,P_ as variable,ig as variableGrads,gJ as version,dx as version_converter,Qb as version_core,hl as version_layers,Tee as version_wasm,Dt as where,rf as whereAsync,gt as zeros,Ie as zerosLike};
/**
* @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
*
* 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 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=tfjs.esm.js.map
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