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face-api
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face-api/dist/face-api.js

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/*
Face-API
homepage: <https://github.com/vladmandic/face-api>
author: <https://github.com/vladmandic>'
*/
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Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Rl.nextTensorId++}nextVariableId(){return Rl.nextVariableId++}clone(e){let t=B.runKernel(Ja,{x:e}),n={x:e},r=a=>({x:()=>{let o="float32",i={x:a},c={dtype:o};return B.runKernel(Ma,i,c)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,s,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(ih(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),s=0;n.forEach(i=>{s+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-s-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,c=_b(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(_b(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=ih(h,this.backendName);D(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let b=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:C,dtype:N}=x;return this.makeTensorFromDataId(k,C,N)});if(r){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!r||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,m,g),g}}let{inputs:u,attrs:l}=e,d=_b(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(c,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),r&&this.addTapeNode(c,u,t,d,n,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:c,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let r=yb(e);if(r!=null){let s=r.inputsToSave||[],a=r.outputsToSave||[],o;r.saveAllInputs?(D(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(c=>t[c])):o=s.map(c=>t[c]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*db(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Qs||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*db(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}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,n=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(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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a=this.state.activeScope.track[s];!a.kept&&!n.has(a.id)&&a.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===r.id&&this.track(s)})}gradients(e,t,n,r=!1){if(D(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));D(s instanceof Ee,()=>"The result y returned by f() must be a tensor.");let a=Z$(this.state.activeTape,t,s);if(!r&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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The output of every math call will be downloaded to CPU and checked for NaNs. 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r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(r==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;let s=this.LS.getItem(this.keys.modelMetadata);if(s!=null){let o=JSON.parse(s);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer),o.trainingConfig!=null&&(t.trainingConfig=o.trainingConfig)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=bF(a),t}};Mo.URL_SCHEME="localstorage://";var h1=e=>Q().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Mo.URL_SCHEME)?PF(e.slice(Mo.URL_SCHEME.length)):null;Et.registerSaveRouter(h1);Et.registerLoadRouter(h1);function PF(e){return new 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TL=W({stringToHashBucketFast_:CL}),NL={fft:Rh,ifft:jl,rfft:Ph,irfft:Sy},_L={hammingWindow:nM,hannWindow:jk,frame:qk,stft:oM},qn={flipLeftRight:lM,grayscaleToRGB:pM,resizeNearestNeighbor:OM,resizeBilinear:RM,rotateWithOffset:fM,cropAndResize:cM,nonMaxSuppression:gM,nonMaxSuppressionAsync:SM,nonMaxSuppressionWithScore:TM,nonMaxSuppressionWithScoreAsync:_M,nonMaxSuppressionPadded:AM,nonMaxSuppressionPaddedAsync:FM,threshold:BM,transform:WM},Qk={bandPart:UM,gramSchmidt:HM,qr:qM},EL={absoluteDifference:YM,computeWeightedLoss:Ss,cosineDistance:JM,hingeLoss:eL,huberLoss:nL,logLoss:sL,meanSquaredError:oL,sigmoidCrossEntropy:uL,softmaxCrossEntropy:pL},Kl={sparseFillEmptyRows:fL,sparseReshape:gL,sparseSegmentMean:yL,sparseSegmentSum:xL},Wh={stringNGrams:kL,stringSplit:SL,stringToHashBucketFast:TL},Cs=class extends O1{minimize(e,t=!1,n){let{value:r,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:s[o.name]}));this.applyGradients(a)}else 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Gh.className="Adam";ra(Gh);var Hh=class extends Cs{constructor(e,t,n,r=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],M(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);M(()=>{let n=he(1,this.accBeta1),r=ge(-this.learningRate,Y(V(this.iteration,this.decay),1));t.forEach((s,a)=>{let 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Ot=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Br=class{constructor(e,t,n,r,s,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=s,this.outputTensorIndex=o,this.id=FI(),a!=null&&(this.originalName=II(a),this.name=SI(this.originalName)),this.rank=t.length}},sW=0,df=class{constructor(e,t){this.callArgs=t,this.id=sW++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},aW=0,je=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=aW++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ns(n)+"_"+cf(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let s=null;e.batchSize!=null&&(s=e.batchSize),n=[s].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Or(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new H(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Fn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Fn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Ts(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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je{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=cf(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ca(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. 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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 y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;ns(v===0,"input layer has >1 nodes"),ns(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof cu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.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={},n={},r={},s={},a={},o=[],i=(b,y,v,x,k,C)=>{(x==null||k==null||C==null)&&(x=b.sourceLayer,k=b.nodeIndex,C=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new Or(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(ss.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let F=N.inboundLayers.length;for(let R=0;R<F;R++){let O=N.inputTensors[R],$=N.inboundLayers[R],P=N.nodeIndices[R],T=N.tensorIndices[R];i(O,y,v,$,P,T)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);o.push(N)},c=[],u=[];for(let b of this.outputs)i(b,c,u);let l=o.slice().reverse();for(let b of l){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=r[b.outboundLayer.id]==null?0:r[b.outboundLayer.id];y=Math.max(y,v),r[b.outboundLayer.id]=y,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],C=b.nodeIndices[x],N=k.inboundNodes[C],F=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,F),n[N.id]=N}}let d={};for(let b in t){let y=t[b];y in d||(d[y]=[]),d[y].push(n[b])}let p={};for(let b in r){let y=r[b];y in p||(p[y]=[]),p[y].push(s[b])}let h=Object.keys(p).map(b=>parseInt(b,10)).sort(Xh);this.layers=[];for(let b of h){let y=p[b];y.sort((v,x)=>{let k=a[v.id],C=a[x.id];return k<C?-1:k>C?1:0});for(let v of y)v instanceof ss&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=p,h=Object.keys(d).map(b=>parseInt(b,10)).sort(Xh);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of d[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new Or(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=d;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new Or(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new df({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new H("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 n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new H(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,r++}let s=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)s.push([n[o],e[a]]);else if(t)throw new H(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new H(`${a.length} of ${r} weights are not set: ${a}`)}rv(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${dv}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=lv(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return M(()=>{e=gt(e);let n=new Qo;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return sd(this.outputs,n,t)})}computeMask(e,t){return M(()=>{e=gt(e);let n;return t==null?n=qo(null,e.length):n=gt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=uf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],c=t[o],u=i.name+"_0_0";n[u]=c}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Xh);if(r.length>1)for(let o of r){let i=this.nodesByDepth[o];for(let c of i){let u=c.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let l=[];for(let f=0;f<c.inboundLayers.length;f++){let m=c.inboundLayers[f],g=c.nodeIndices[f],b=c.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];l.push(v)}let d=u.computeOutputShape(Fn(l)),p=uf(d),h=u.inboundNodes.indexOf(c);for(let f=0;f<p.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=p[f]}}}let s=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],c=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],l=`${i.name}_${c}_${u}`;a.push(l)}for(let o=0;o<a.length;o++){let i=a[o];ns(i in n),s.push(n[i])}return Fn(s)}runInternalGraph(e,t){t==null&&(t=qo(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let c=this.inputs[i],u=e[i],l=t[i];n[c.id]=[u,l]}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Xh);for(let i of r){let c=this.nodesByDepth[i];for(let u of c){let l=u.outboundLayer,d=u.inputTensors,p=u.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,b,y;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=gt(l.call(v,f)),y=gt(l.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=gt(l.call(m,f)),y=gt(l.computeMask(m,g));if(l.activityRegularizer)throw new $e("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<p.length;++v){let x=p[v],k=b[v],C=y[v];n[x.id]=[k,C]}}}}let s=[],a=[],o=[];for(let i of this.outputs){ns(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[c,u]=n[i.id];o.push(c.shape),s.push(c),a.push(u)}return[s,a,o]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof ss?1:0;for(let s=0;s<r.inboundNodes.length;s++){let a=ss.nodeKey(r,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new H(`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 H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return M(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=ss.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),c=[];for(let l=0;l<a.inboundNodes.length;l++){let d=a.inboundNodes[l],p=ss.nodeKey(a,l),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],b=d.nodeIndices[m],y=d.tensorIndices[m],v=ss.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}c.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=c,n.push(u)}e.layers=n;let r=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],c=ss.nodeKey(o,i);if(!this.containerNodes.has(c))continue;let u=t[c];u==null&&(u=0);let l=this.inputLayersTensorIndices[a];r.push([o.name,u,l])}e.inputLayers=r;let s=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],c=ss.nodeKey(o,i);if(!this.containerNodes.has(c))continue;let u=t[c];u==null&&(u=0);let l=this.outputLayersTensorIndices[a];s.push([o.name,u,l])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){let s={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],C=v[2];if(y=v[3]==null?{}:v[3],!(x in s)){o(m,g);return}let N=s[x];if(N.inboundNodes.length<=k){o(m,g);return}let F=N.inboundNodes[k];b.push(F.outputTensors[C])}b.length>0&&m.apply(Fn(b),y)}function c(m){let g=m.name,b=zr(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(r),s[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let u=t.name,l=t.layers;for(let m of l)c(m);for(;!m4(a);)for(let m of l){let g=s[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];ns(g in s);let x=s[g].inboundNodes[b].outputTensors;d.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];ns(g in s);let x=s[g].inboundNodes[b].outputTensors;p.push(x[y])}return new e({inputs:d,outputs:p,name:u})}get stateful(){if(this._stateful)throw new H("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(){M(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function BW(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function eS(e,t){return BW(e,t,"classWeight")}async function tS(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=M(()=>{if(e.shape.length===1)return xs(e);if(e.shape.length===2){if(e.shape[1]>1)return Xc(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());Ae(s);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let c=0;c<a.length;c++)w.assert(a[c].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[c]} has ${a[c].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let c=0;c<o.length;c++)w.assert(o[c].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[c]} has ${o[c].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function rS(e,t,n){if(n instanceof Ee)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let s of t){if(n[s]==null)throw new H(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function VW(e){if(e.length===3)throw new $e("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function UW(e,t,n){let r=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let s=n.validationData!=null,a,o;if(s)if(sS(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=VW(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),c=e.getDedupedMetricsNames(),u;s?u=c.slice().concat(c.map(g=>"val_"+g)):u=c.slice();let l=VI(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=UI(l,d,n.epochs,null,null,GW(t,n),null,s,u);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let b=0,y=0;for(r||(m=await t.iterator());r?b<n.batchesPerEpoch:!0;){let v=await m.next();if(r&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Sv.className="ThresholdedReLU";ae.registerClass(Sv);var Cv=class extends je{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new bv().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Me(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cv.className="Softmax";ae.registerClass(Cv);function pu(e,t,n){if(typeof e=="number")return qo(e,t);if(e.length!==t)throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!E4(s))throw new H(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function Wr(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function as(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+la([n-t,0]);else if(r==="same")e=e*t;else throw new H(`Unsupport padding mode: ${r}.`);return e}function Tv(e,t){return M(()=>(Ft(t),t==="channelsFirst"?Oe(e,[0,2,3,1]):e))}function NS(e,t){return M(()=>(Ft(t),t==="channelsFirst"?Oe(e,[0,2,3,4,1]):e))}function lV(e,t,n,r=1,s="valid",a,o=1){return M(()=>{if(a==null&&(a=Pr()),Ft(a),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Oe(e,[0,2,1])),s==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=ty(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=Lr(i,n)),i})}function _S(e,t,n,r=[1,1],s="valid",a,o,i=null){return M(()=>{if(a==null&&(a=Pr()),Ft(a),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let c=Tv(e,a);if(s==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return c=ia.conv2d({x:c,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(c=Oe(c,[0,3,1,2])),c})}function dV(e,t,n,r=[1,1,1],s="valid",a,o){return M(()=>{if(a==null&&(a=Pr()),Ft(a),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=NS(e,a);if(s==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=sy(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Lr(i,n)),a==="channelsFirst"&&(i=Oe(i,[0,4,1,2,3])),i})}var Nv=class extends je{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Nv.verifyArgs(t),this.rank=e,Yt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=pu(t.kernelSize,e,"kernelSize"),this.strides=pu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,cr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=ha(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=kt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Gt(t.biasConstraint),this.biasRegularizer=It(t.biasRegularizer),this.activityRegularizer=It(t.activityRegularizer),this.dilationRate=pu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(ns("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ly(e.kernelSize,"number",1,3))throw new H(`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:pa(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},id=class extends Nv{constructor(e,t){super(e,t);this.kernel=null,id.verifyArgs(t),this.filters=t.filters,Yt(this.filters,"filters"),this.kernelInitializer=kt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Gt(t.kernelConstraint),this.kernelRegularizer=It(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,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]:n}}],this.built=!0}call(e,t){return M(()=>{e=Me(e);let n,r=this.bias==null?null:this.bias.read(),s=bI(this.activation.getClassName());if(s!=null&&this.rank===2)n=_S(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=lV(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=_S(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=dV(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=Wr(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:lt(this.kernelRegularizer),kernelConstraint:Ut(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},cd=class extends id{constructor(e){super(2,e);cd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ly(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};cd.className="Conv2D";ae.registerClass(cd);var ud=class extends id{constructor(e){super(3,e);ud.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};ud.className="Conv3D";ae.registerClass(ud);var _v=class extends cd{constructor(e){super(e);if(this.inputSpec=[new Ot({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ot({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Me(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],c=r[o],u=this.kernelSize[0],l=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=as(i,d,u,this.padding),f=as(c,p,l,this.padding),m=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Oe(n,[0,2,3,1]));let g=ry(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Oe(g,[0,3,1,2])),this.bias!=null&&(g=Lr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=st(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],c=this.strides[1];return t[n]=this.filters,t[r]=as(t[r],i,a,this.padding),t[s]=as(t[s],c,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};_v.className="Conv2DTranspose";ae.registerClass(_v);var Ev=class extends ud{constructor(e){super(e);if(this.inputSpec=[new Ot({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ot({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{let n=Me(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let c=r[i],u=r[a],l=r[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=as(c,f,d,this.padding),y=as(u,m,p,this.padding),v=as(l,g,h,this.padding),x=[s,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Oe(n,[0,2,3,4,1]));let k=uk(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Oe(k,[0,4,1,2,3])),this.bias!==null&&(k=Lr(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=st(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],c=this.kernelSize[2],u=this.strides[0],l=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[r]=as(t[r],u,o,this.padding),t[s]=as(t[s],l,i,this.padding),t[a]=as(t[a],d,c,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ev.className="Conv3DTranspose";ae.registerClass(Ev);var ES=class extends id{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 H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=kt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=It(t.depthwiseRegularizer),this.depthwiseConstraint=Gt(t.depthwiseConstraint),this.pointwiseInitializer=kt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=It(t.pointwiseRegularizer),this.pointwiseConstraint=Gt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"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 Ot({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return M(()=>{e=Me(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Oe(e,[0,2,3,1])),n=ru(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Lr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Oe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.pointwiseRegularizer=lt(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};ES.className="SeparableConv";var Av=class extends ES{constructor(e){super(2,e)}};Av.className="SeparableConv2D";ae.registerClass(Av);var xf=class extends id{constructor(e){super(1,e);xf.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"&&!Ly(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};xf.className="Conv1D";ae.registerClass(xf);var $v=class extends je{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return M(()=>{if(e=Me(e),this.dataFormat==="channelsLast"){let n=Zh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Zh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Zh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Zh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$v.className="Cropping2D";ae.registerClass($v);var Fv=class extends je{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,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,T4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return M(()=>{let n=Me(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Oe(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[s,a]):qn.resizeBilinear(n,[s,a]);return Oe(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[s,a]):qn.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Fv.className="UpSampling2D";ae.registerClass(Fv);function pV(e,t,n=[1,1],r="valid",s,a){return M(()=>{s==null&&(s=Pr()),Ft(s);let o=Tv(e,s);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Wo(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=Oe(o,[0,3,1,2])),o})}var Dv=class extends Nv{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=kt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Gt(e.depthwiseConstraint),this.depthwiseRegularizer=It(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return M(()=>{e=Me(e);let n=pV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Lr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=Wr(t,this.kernelSize[0],this.padding,this.strides[0]),a=Wr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=lt(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};Dv.className="DepthwiseConv2D";ae.registerClass(Dv);function AS(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function $S(e,t,n,r=!1,s,a,o=!1,i=!1){return M(()=>{let c=t.shape.length;if(c<3)throw new H(`Input should be at least 3D, but is ${c}D.`);let u=[1,0].concat(Mr(2,c));if(t=Oe(t,u),a!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ce(ce(s,"bool"),"float32"),s.rank===c-1&&(s=yn(s,-1)),s=Oe(s,u)),r&&(t=ir(t,0),s!=null&&(s=ir(s,0)));let l=[],d,p=n,h=t.shape[0],f=ht(t),m;s!=null&&(m=ht(s));for(let b=0;b<h;++b){let y=f[b],v=M(()=>e(y,p));if(s==null)d=v[0],p=v[1];else{let x=M(()=>{let k=m[b],C=he(or(k),k),N=Y(V(v[0],k),V(p[0],C)),F=p.map((R,O)=>Y(V(v[1][O],k),V(R,C)));return{output:N,newStates:F}});d=x.output,p=x.newStates}i&&l.push(d)}let g;return i&&(g=Pt(l,1)),[d,g,p]})}var os=class extends je{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new If({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 Ot({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 Mr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){tv(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[r].concat(s)}else return r}computeMask(e,t){return M(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(s=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)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 $e("Constants support is not implemented in RNN yet.");tv(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ot({shape:[n,null,...r]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new H(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Ot({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new Ts("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("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(r=>wt([n,r])):this.states_=[wt([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>wt([n,r])):this.states_[0]=wt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):Ae(this.states_);for(let r=0;r<this.states_.length;++r){let s=e[r],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(s.shape,o))throw new H(`State ${r} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${s.shape}`);this.states_[r]=s}}this.states_=this.states_.map(r=>Xt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=AS(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let c of n)this.stateSpec.push(new Ot({shape:c.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(t.constants=r,a=a.concat(r),this.numConstants=r.length),a[0]instanceof Br){let c=[e].concat(a),u=this.inputSpec.concat(o),l=this.inputSpec;this.inputSpec=u;let d=super.apply(c,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;e=Me(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 H(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:r},c=$S((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),u=c[0],l=c[1],d=c[2];this.stateful&&this.resetStates(d,r);let p=this.returnSequences?l:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return M(()=>{let t=wt(e.shape);return t=ve(t,[1,2]),t=Ql(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?jy(t,[1,n]):t):this.cell.stateSize>1?[jy(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 n=this.cell.getConfig();return this.getClassName()===os.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,s=zr(r,n);return new e(Object.assign(t,{cell:s}))}};os.className="RNN";ae.registerClass(os);var ld=class extends je{},wf=class extends ld{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,Yt(this.units,"units"),this.activation=ha(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=It(e.kernelRegularizer),this.recurrentRegularizer=It(e.recurrentRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=iu([1,la([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=iu([1,la([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 M(()=>{if(e=e,e.length!==2)throw new H(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fa({ones:()=>or(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fa({ones:()=>or(n),rate:this.recurrentDropout,training:r}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=rs(V(e,a),this.kernel.read()):s=rs(e,this.kernel.read()),this.bias!=null&&(s=Lr(s,this.bias.read())),o!=null&&(n=V(n,o));let i=Y(s,rs(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:pa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};wf.className="SimpleRNNCell";ae.registerClass(wf);var Rv=class extends os{constructor(e){e.cell=new wf(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};Rv.className="SimpleRNN";ae.registerClass(Rv);var kf=class extends ld{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 H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Yt(this.units,"units"),this.activation=ha(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ha(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=It(e.kernelRegularizer),this.recurrentRegularizer=It(e.recurrentRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=iu([1,la([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=iu([1,la([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 M(()=>{if(e=e,e.length!==2)throw new H(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fa({ones:()=>or(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fa({ones:()=>or(r),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,c;0<this.dropout&&this.dropout<1&&(e=V(e,s[0]));let u=rs(e,this.kernel.read());this.useBias&&(u=Lr(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=V(r,a[0]));let l=this.recurrentKernel.read(),[d,p]=jn(l,[2*this.units,this.units],l.rank-1),h=rs(r,d),[f,m,g]=jn(u,3,u.rank-1),[b,y]=jn(h,2,h.rank-1);o=this.recurrentActivation.apply(Y(f,b)),i=this.recurrentActivation.apply(Y(m,y));let v=rs(V(i,r),p);c=this.activation.apply(Y(g,v));let x=Y(V(o,r),V(Y(1,Tt(o)),c));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:pa(this.activation),recurrentActivation:pa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};kf.className="GRUCell";ae.registerClass(kf);var Pv=class extends os{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 kf(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Pv.className="GRU";ae.registerClass(Pv);var dd=class extends ld{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,Yt(this.units,"units"),this.activation=ha(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ha(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=kt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=It(e.kernelRegularizer),this.recurrentRegularizer=It(e.recurrentRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.kernelConstraint=Gt(e.kernelConstraint),this.recurrentConstraint=Gt(e.recurrentConstraint),this.biasConstraint=Gt(e.biasConstraint),this.dropout=iu([1,la([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=iu([1,la([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=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,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 r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Ir{apply(i,c){let u=s.apply([a]),l=new Qh().apply([a]),d=s.apply([a*2]);return TI(TI(u,l),d)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fa({ones:()=>or(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fa({ones:()=>or(r),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,c,u,l;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let d=rs(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=V(r,o[0])),d=Y(d,rs(r,this.recurrentKernel.read())),this.useBias&&(d=Lr(d,this.bias.read()));let[p,h,f,m]=jn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),c=this.recurrentActivation.apply(h),u=Y(V(c,s),V(i,this.activation.apply(f))),l=this.recurrentActivation.apply(m);let g=V(l,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:pa(this.activation),recurrentActivation:pa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:lt(this.kernelRegularizer),recurrentRegularizer:lt(this.recurrentRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),recurrentConstraint:Ut(this.recurrentConstraint),biasConstraint:Ut(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};dd.className="LSTMCell";ae.registerClass(dd);var Ov=class extends os{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 dd(e);super(e)}call(e,t){return M(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ov.className="LSTM";ae.registerClass(Ov);var If=class extends ld{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 M(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){tv(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Yo(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return Object.assign({},e,r)}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(zr(s,n));return new e({cells:r})}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 n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return nv(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}rv(t)}};If.className="StackedRNNCells";ae.registerClass(If);function fa(e){let{ones:t,rate:n,training:r=!1,count:s=1}=e,a=()=>_I(t(),n),o=()=>td(a,t,r);return!s||s<=1?Xt(o().clone()):Array(s).fill(void 0).map(o).map(c=>Xt(c.clone()))}var hV=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(e);s<r.length;s++)t.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(e,r[s])&&(n[r[s]]=e[r[s]]);return n},FS=class extends os{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ot({ndim:5})]}call(e,t){return M(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,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 M(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=wt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){M(()=>{if(!this.stateful)throw new Ts("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new H("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(()=>wt(s)):this.states_=[wt(s)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>wt(s)):this.states_[0]=wt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`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()):Ae(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],c=s;if(!w.arraysEqual(i.shape,c))throw new H(`State ${o} is incompatible with layer ${this.name}: expected shape=${c}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Xt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",c=e[i?3:2],u=e[i?4:3],l=Wr(c,r[0],s,a[0],o[0]),d=Wr(u,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,l,d]:[l,d,n]]}};FS.className="ConvRNN2D";var Sf=class extends dd{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,Yt(this.filters,"filters"),this.kernelSize=pu(n,2,"kernelSize"),this.kernelSize.forEach(i=>Yt(i,"kernelSize")),this.strides=pu(r||1,2,"strides"),this.strides.forEach(i=>Yt(i,"strides")),this.padding=s||"valid",cr(this.padding),this.dataFormat=a||"channelsLast",Ft(this.dataFormat),this.dilationRate=pu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Yt(i,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let c=this.biasInitializer,u=this.filters;i=new(t=class extends Ir{apply(d,p){let h=c.apply([u]),f=Hn([u]),m=c.apply([u*2]);return Hy([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return M(()=>{if(e.length!==3)throw new H(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fa({ones:()=>or(r),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,c=(ee,te,ne)=>!te||!te[ne]?ee:V(te[ne],ee),u=c(r,i,0),l=c(r,i,1),d=c(r,i,2),p=c(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fa({ones:()=>or(s),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=c(s,h,0),m=c(s,h,1),g=c(s,h,2),b=c(s,h,3),y=3,[v,x,k,C]=jn(this.kernel.read(),o,y),[N,F,R,O]=this.useBias?jn(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,v,N,this.padding),l=this.inputConv(l,x,F,this.padding),d=this.inputConv(d,k,R,this.padding),p=this.inputConv(p,C,O,this.padding);let[$,P,T,L]=jn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,$),m=this.recurrentConv(m,P),g=this.recurrentConv(g,T),b=this.recurrentConv(b,L);let G=this.recurrentActivation.apply(Y(u,f)),j=this.recurrentActivation.apply(Y(l,m)),q=Y(V(j,a),V(G,this.activation.apply(Y(d,g)))),K=V(this.recurrentActivation.apply(Y(p,b)),this.activation.apply(q));return[K,K,q]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=hV(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let s=Dt(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Lr(s,n,this.dataFormat):s}recurrentConv(e,t){return Dt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Sf.className="ConvLSTM2DCell";ae.registerClass(Sf);var Mv=class extends FS{constructor(e){let t=new Sf(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Mv.className="ConvLSTM2D";ae.registerClass(Mv);var Cf=class extends je{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,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return td(()=>_I(n,this.rate,s,this.seed),()=>n,r)}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()}};Cf.className="Dropout";ae.registerClass(Cf);var Lv=class extends Cf{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Lv.className="SpatialDropout1D";ae.registerClass(Lv);var Bv=class extends je{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,Yt(this.units,"units"),this.activation=ha(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=kt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=kt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Gt(e.kernelConstraint),this.biasConstraint=Gt(e.biasConstraint),this.kernelRegularizer=It(e.kernelRegularizer),this.biasRegularizer=It(e.biasRegularizer),this.activityRegularizer=It(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(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=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=bI(this.activation.getClassName()),s;return r!=null?s=rs(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=rs(n,this.kernel.read()),this.bias!=null&&(s=Lr(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:pa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:lt(this.kernelRegularizer),biasRegularizer:lt(this.biasRegularizer),activityRegularizer:lt(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Bv.className="Dense";ae.registerClass(Bv);var zv=class extends je{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new H(`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],ua(e,1)]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=Oe(n,r)}return F4(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};zv.className="Flatten";ae.registerClass(zv);var Wv=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.activation=ha(e.activation)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.activation.apply(n)})}getConfig(){let e={activation:pa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Wv.className="Activation";ae.registerClass(Wv);var Vv=class extends je{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 M(()=>(e=Me(e),A4(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Vv.className="RepeatVector";ae.registerClass(Vv);var Uv=class extends je{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 n="Total size of new array must be unchanged.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let c=r[i];if(this.isUnknown(c))if(a===null)a=i;else throw new H("Can only specifiy one unknown dimension.");else s*=c}let o=ua(e);if(a!==null){if(s===0||o%s!=0)throw new H(n);r[a]=o/s}else if(o!==s)throw new H(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){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 M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return U(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Uv.className="Reshape";ae.registerClass(Uv);var Gv=class extends je{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=Mr(1,e.dims.length+1);if(!w.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 Ot({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Oe(Me(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Gv.className="Permute";ae.registerClass(Gv);var Hv=class extends je{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 n=Me(e),r=-1;return xh(eu(n,this.maskValue),r)}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e),r=-1,s=!0,a=xh(eu(n,this.maskValue),r,s);return V(n,ce(a,n.dtype))})}};Hv.className="Masking";ae.registerClass(Hv);var jv=class extends je{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(gt(e.inputLength))}this.inputDim=e.inputDim,Yt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Yt(this.outputDim,"outputDim"),this.embeddingsInitializer=kt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=It(e.embeddingsRegularizer),this.activityRegularizer=It(e.activityRegularizer),this.embeddingsConstraint=Gt(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 M(()=>this.maskZero?(e=Me(e),eu(e,He(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=gt(this.inputLength);if(t.length!==e.length-1)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let s=t[r],a=e[r+1];if(s!=null&&a!=null&&s!==a)throw new H(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return M(()=>{this.invokeCallHook(e,t);let n=Me(e);n.dtype!=="int32"&&(n=Yh(n,"int32"));let r=NI(this.embeddings.read(),U(n,[n.size]));return U(r,st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:lt(this.embeddingsRegularizer),activityRegularizer:lt(this.activityRegularizer),embeddingsConstraint:Ut(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};jv.className="Embedding";ae.registerClass(jv);var ti=class extends je{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}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 n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new H("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new H(`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=ca(t),t.length>1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let r=e.map(s=>s.length);e.indexOf(null)===-1&&ca(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return M(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(s=>s.rank);if(r.indexOf(null)===-1){let s=la(r);for(let a of e){let o=a.rank;for(let i=0;i<s-o;++i)a=Ql(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let i of e){let c=i.rank;if(c==null){let u=i.shape,l=u[0],d=u.slice(1).concat([l]),p=U(i,[l].concat(ua(u.slice(1))));p=Oe(p,[1,0]),p=U(p,d),n.push(p),s=!0}else if(c>1){let u=Mr(1,c).concat([0]);n.push(Oe(i,u)),s=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(s){if(o==null){let i=a.shape,c=i.length,u=i[c-1],l=[u].concat(i.slice(0,i.length-1));a=U(Oe(U(a,[-1,u]),[1,0]),l)}else if(o>1){let i=[o-1].concat(Mr(0,o-1));a=Oe(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=ca(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return M(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:yn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=Fr(n,t[r]);return n})}},qv=class extends ti{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Y(t,e[n]);return t})}};qv.className="Add";ae.registerClass(qv);var Kv=class extends ti{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};Kv.className="Multiply";ae.registerClass(Kv);var Xv=class extends ti{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Y(t,e[n]);return V(1/e.length,t)})}};Xv.className="Average";ae.registerClass(Xv);var Yv=class extends ti{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=ks(t,e[n]);return t})}};Yv.className="Maximum";ae.registerClass(Yv);var Zv=class extends ti{constructor(e){super(e)}mergeFunction(e){return M(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ul(t,e[n]);return t})}};Zv.className="Minimum";ae.registerClass(Zv);var Jv=class extends ti{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 H("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let s=e[r].slice();s.splice(this.axis,1);let a=!1;for(let o of n)if(w.arraysEqual(o,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Jv.className="Concatenate";ae.registerClass(Jv);function pd(e,t){for(;e<0;)e+=t;return e}function fV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,s=t.shape.length;n==null&&(n=[r-1,s-2]);let a=n;return M(()=>{let o;if(r>s){o=r-s;let c=[];for(let u=0;u<o;++u)c.push(1);t=U(t,t.shape.concat(c))}else if(s>r){o=s-r;let c=[];for(let u=0;u<o;++u)c.push(1);e=U(e,e.shape.concat(c))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ve(V(e,t),a[0]):i=ve(V(Oe(e,[1,0]),t),a[1]);else{let c=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=De(e,t,c,u)}if(o>0){let c;r>s?c=r+s-3:c=r-1;let u=[];for(let l=c;l<c+o;++l)u.push(l);i=Is(i,u)}return i.shape.length===1&&(i=yn(i,1)),i})}var Qv=class extends ti{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.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],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new H(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((s,a)=>pd(s,e[a].shape.length)):r=[pd(this.axes,t.shape.length),pd(this.axes,n.shape.length)],this.normalize&&(t=pf(t,r[0]),n=pf(n,r[1])),fV(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[pd(this.axes,e.length),pd(this.axes,t.length)],n}computeOutputShape(e){w.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(),n=e[1].slice();if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let s=t.concat(n);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}};Qv.className="Dot";ae.registerClass(Qv);var ex=class extends je{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 M(()=>{this.invokeCallHook(e,t);let n=Me(e);return td(()=>Y(Jh(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};ex.className="GaussianNoise";ae.registerClass(ex);var tx=class extends je{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 M(()=>{this.invokeCallHook(e,t);let n=Me(e);return this.rate>0&&this.rate<1?td(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return V(n,Jh(n.shape,1,s))},()=>n,t.training||!1):n})}};tx.className="GaussianDropout";ae.registerClass(tx);var nx=class extends je{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Me(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 M(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return td(()=>{let s=Me(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,c=Vo(tu(n),this.rate);c=Yh(c,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,l=-u*i*this.rate,d=Y(V(s,c),V(Y(c,-1),i));return Y(V(d,u),l)},()=>Me(e),t.training||!1)}return e})}};nx.className="AlphaDropout";ae.registerClass(nx);function hd(e,t,n,r,s,a=.001){let o;if(e.rank===2)o=Q1(e,t,n,r,s,a);else if(e.rank===3)o=ek(e,t,n,r,s,a);else if(e.rank===4)o=tk(e,t,n,r,s,a);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function mV(e,t,n,r,s=.001){return M(()=>{let a=Ah(e,r),o=a.mean,i=a.variance;return[hd(e,o,i,n,t,s),o,i]})}function gV(e,t,n,r,s=.001){return M(()=>{let a=Ah(e,r),o=a.mean,i=a.variance,c=[];for(let f of 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ot({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return M(()=>{let n=t.training==null?!1:t.training,r=Me(e),s=r.shape,a=s.length,o=Mr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let c=qo(1,a);c[i]=s[i];let u=o.slice();u.sort();let l=!w.arraysEqual(u,Mr(0,a).slice(0,a-1)),d=()=>{if(l){let b=U(this.movingMean.read(),c),y=U(this.movingVariance.read(),c),v=this.center?U(this.beta.read(),c):null,x=this.scale?U(this.gamma.read(),c):null;return hd(r,b,y,v,x,this.epsilon)}else return hd(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=bV(r,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{M(()=>{let x=1-v,k=b.read(),C=V(he(k,y),x);b.write(he(k,C))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer),betaConstraint:Ut(this.betaConstraint),gammaConstraint:Ut(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};rx.className="BatchNormalization";ae.registerClass(rx);var sx=class extends je{constructor(e){e==null&&(e={});super(e);if(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=kt(e.betaInitializer||"zeros"),this.gammaInitializer=kt(e.gammaInitializer||"ones"),this.betaRegularizer=It(e.betaRegularizer),this.gammaRegularizer=It(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(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!==ca(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Me(e),r=n.shape,s=r.length;return M(()=>{let a=!0,{mean:o,variance:i}=Ah(n,this.axis,a),c=qo(1,s);for(let f of this.axis)c[f]=r[f];let u=f=>f!=null&&f.shape.length!==s&&this.axis!==[s-1]?U(f,c):f,l=u(this.gamma.read()),d=u(this.beta.read()),p=[],h=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(p.push(r[f]),h.push(1)):(p.push(1),h.push(r[f]));return o=Un(o,p),i=Un(i,p),l=Un(l,h),d=Un(d,h),hd(n,o,i,d,l,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),betaRegularizer:lt(this.betaRegularizer),gammaRegularizer:lt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};sx.className="LayerNormalization";ae.registerClass(sx);function yV(e,t,n){return M(()=>{if(e.rank!==4)throw new H(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Pr()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ot({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;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?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(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?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return M(()=>yV(Me(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ax.className="ZeroPadding2D";ae.registerClass(ax);function Tf(e,t,n,r,s,a){return M(()=>{Ft(s),wI(a),cr(r),n==null&&(n=[1,1]),r==null&&(r="valid"),s==null&&(s=Pr()),a==null&&(a="max"),e=Tv(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Rt(e,t,n,i):o=vr(e,t,n,i),s==="channelsFirst"&&(o=Oe(o,[0,3,1,2])),o})}function DS(e,t,n,r,s,a){return M(()=>{Ft(s),wI(a),cr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=Pr()),a==null&&(a="max"),e=NS(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=hy(e,t,n,i):o=Qb(e,t,n,i),s==="channelsFirst"&&(o=Oe(o,[0,4,1,2,3])),o})}var RS=class extends je{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 H(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Yt(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 H(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,cr(this.padding),this.inputSpec=[new Ot({ndim:3})]}computeOutputShape(e){e=st(e);let t=Wr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return M(()=>{this.invokeCallHook(e,t),e=Ql(Me(e),2);let n=this.poolingFunction(Me(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Is(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ox=class extends RS{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ft(s),cr(r),Tf(e,t,n,r,s,"max")}};ox.className="MaxPooling1D";ae.registerClass(ox);var ix=class extends RS{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ft(s),cr(r),Tf(e,t,n,r,s,"avg")}};ix.className="AveragePooling1D";ae.registerClass(ix);var PS=class extends je{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(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 H(`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];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),cr(this.padding),this.inputSpec=[new Ot({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Wr(t,this.poolSize[0],this.padding,this.strides[0]),n=Wr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},cx=class extends PS{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ft(s),cr(r),Tf(e,t,n,r,s,"max")}};cx.className="MaxPooling2D";ae.registerClass(cx);var ux=class extends PS{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ft(s),cr(r),Tf(e,t,n,r,s,"avg")}};ux.className="AveragePooling2D";ae.registerClass(ux);var OS=class extends je{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(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 H(`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];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),cr(this.padding),this.inputSpec=[new Ot({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Wr(t,this.poolSize[0],this.padding,this.strides[0]),n=Wr(n,this.poolSize[1],this.padding,this.strides[1]),r=Wr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return M(()=>(this.invokeCallHook(e,t),this.poolingFunction(Me(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}},lx=class extends OS{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ft(s),cr(r),DS(e,t,n,r,s,"max")}};lx.className="MaxPooling3D";ae.registerClass(lx);var dx=class extends OS{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Ft(s),cr(r),DS(e,t,n,r,s,"avg")}};dx.className="AveragePooling3D";ae.registerClass(dx);var MS=class extends je{constructor(e){super(e);this.inputSpec=[new Ot({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},px=class extends MS{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return At(n,1)})}};px.className="GlobalAveragePooling1D";ae.registerClass(px);var hx=class extends MS{constructor(e){super(e||{})}call(e,t){return M(()=>{let n=Me(e);return $r(n,1)})}};hx.className="GlobalMaxPooling1D";ae.registerClass(hx);var LS=class extends je{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new Ot({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new $e}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},fx=class extends LS{call(e,t){return M(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?At(n,[1,2]):At(n,[2,3])})}};fx.className="GlobalAveragePooling2D";ae.registerClass(fx);var mx=class extends LS{call(e,t){return M(()=>{let n=Me(e);return this.dataFormat==="channelsLast"?$r(n,[1,2]):$r(n,[2,3])})}};mx.className="GlobalMaxPooling2D";ae.registerClass(mx);var BS=class extends je{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,n={}){let r=t.layer,s=zr(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},gx=class extends BS{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new H(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return M(()=>(e=Me(e),$S((a,o)=>[Me(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};gx.className="TimeDistributed";ae.registerClass(gx);function vV(e){Xo(C4,"BidirectionalMergeMode",e)}var xV="concat",bx=class extends BS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=zr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=zr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?xV:e.mergeMode,vV(this.mergeMode),e.weights)throw new $e("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,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Fn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=AS(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let c=n.length;if(c%2>0)throw new H("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(l=>new Ot({shape:l.shape}));this.forwardLayer.stateSpec=u.slice(0,c/2),this.backwardLayer.stateSpec=u.slice(c/2),o.push(...u)}if(r!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof Br;for(let c of a)if(c instanceof Br!==i)throw new H("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let c=[e].concat(a),u=this.inputSpec.concat(o),l=this.inputSpec;this.inputSpec=u;let d=super.apply(c,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return M(()=>{let n=t.initialState,r,s;if(n==null)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),c=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:c}))}let a;this.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=ir(s,1));let o;return this.mergeMode==="concat"?o=Hy([r,s]):this.mergeMode==="sum"?o=Y(r,s):this.mergeMode==="ave"?o=V(.5,Y(r,s)):this.mergeMode==="mul"?o=V(r,s):this.mergeMode==null&&(o=[r,s]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Yo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Yo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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IG=class{constructor(e,t,n,r,s,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=ke(0),Xt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return 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r=I("size",e,t,n),s=I("dtype",e,t,n),a=I("elementShape",e,t,n),o=I("dynamicSize",e,t,n),i=I("clearAfterRead",e,t,n),c=I("identicalElementShapes",e,t,n),u=I("name",e,t,n),l=new IG(u,s,r,a,c,o,i);return n.addTensorArray(l),[l.idTensor,ke(1)]}case"TensorArrayWriteV3":{let r=I("tensorArrayId",e,t,n),s=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(r.id);return o.write(s,a),[o.idTensor]}case"TensorArrayReadV3":{let r=I("tensorArrayId",e,t,n),s=I("index",e,t,n);return[n.getTensorArray(r.id).read(s)]}case"TensorArrayGatherV3":{let r=I("tensorArrayId",e,t,n),s=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(r.id).gather(s,a)]}case"TensorArrayScatterV3":{let r=I("tensorArrayId",e,t,n),s=I("indices",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(r.id);return o.scatter(s,a),[o.idTensor]}case"TensorArrayConcatV3":{let r=I("tensorArrayId",e,t,n),s=n.getTensorArray(r.id),a=I("dtype",e,t,n);return[s.concat(a)]}case"TensorArraySplitV3":{let r=I("tensorArrayId",e,t,n),s=I("tensor",e,t,n),a=I("lengths",e,t,n),o=n.getTensorArray(r.id);return o.split(a,s),[o.idTensor]}case"TensorArraySizeV3":{let r=I("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return[ke(s.size(),"int32")]}case"TensorArrayCloseV3":{let r=I("tensorArrayId",e,t,n),s=n.getTensorArray(r.id);return s.clearAndClose(),[s.idTensor]}case"TensorListSetItem":{let r=I("tensorListId",e,t,n),s=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorList(r.id);return o.setItem(s,a),[o.idTensor]}case"TensorListGetItem":{let r=I("tensorListId",e,t,n),s=I("index",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(s,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let r=I("indices",e,t,n),s=I("tensor",e,t,n),a=I("elementShape",e,t,n),o=I("numElements",e,t,n),i=TG(s,r,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=CG(r,s,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),s=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(s,o,a)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(s,a,o)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),s=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=SG(r,s,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),s=n.getTensorList(r.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[s.concat(a,o)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),s=I("tensor",e,t,n),a=n.getTensorList(r.id);return a.pushBack(s),[a.idTensor]}case"TensorListPopBack":{let 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r=I("boxes",e,t,n),s=I("scores",e,t,n),a=I("maxOutputSize",e,t,n),o=I("iouThreshold",e,t,n),i=I("scoreThreshold",e,t,n),c=I("softNmsSigma",e,t,n);return{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:c}}var $G=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:c}=Rx(e,t,n),u=await qn.nonMaxSuppressionWithScoreAsync(r,s,a,o,i,c);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=Rx(e,t,n),c=I("padToMaxOutputSize",e,t,n),u=await qn.nonMaxSuppressionPaddedAsync(r,s,a,o,i,c);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:s,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=Rx(e,t,n);return[await qn.nonMaxSuppressionAsync(r,s,a,o,i)]}case"Where":{let r=ce(I("condition",e,t,n),"bool"),s=[await Ny(r)];return 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r=I("image",e,t,n),s=I("boxes",e,t,n),a=I("boxInd",e,t,n),o=I("cropSize",e,t,n),i=I("method",e,t,n),c=I("extrapolationValue",e,t,n);return[qn.cropAndResize(r,s,a,o,i,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},MG=(e,t,n)=>{switch(e.op){case"Equal":return[sr(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[eu(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Gn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Vo(I("a",e,t,n),I("b",e,t,n))];case"Less":return[cy(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[Uo(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[Fr(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[_h(I("a",e,t,n))];case"LogicalOr":return[py(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[gn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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u=w.arraysEqual(c.shape,a);if(!u&&!w.arraysEqual(Is(c).shape,o))throw new Error("the input tensors shape does not match");return u?c:U(c,a)});return[Pt(i,r)]});case"Unpack":{let r=I("axis",e,t,n),s=I("tensor",e,t,n);return ht(s,r)}case"Tile":{let r=I("reps",e,t,n);return[Un(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),s=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return jn(a,s,r)}case"ScatterNd":{let r=I("indices",e,t,n),s=I("values",e,t,n),a=I("shape",e,t,n);return[zk(r,s,a)]}case"GatherNd":{let r=I("x",e,t,n),s=I("indices",e,t,n);return[Wk(r,s)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),s=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[Ey(r,a,s,a.dtype===o.dtype?o:ce(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},VG=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:s,emptyRowIndicator:a,reverseIndexMap:o}=Kl.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[r,s,a,o]}case"SparseReshape":{let{outputIndices:r,outputShape:s}=Kl.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[r,s]}case"SparseSegmentMean":return[Kl.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Kl.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},UG=(e,t,n)=>{switch(e.op){case"FFT":return[Rh(I("x",e,t,n))];case"IFFT":return[jl(I("x",e,t,n))];case"RFFT":return[Ph(I("x",e,t,n))];case"IRFFT":return[Sy(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},GG=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:s}=Wh.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[r,s]}case"StringSplit":{let{indices:r,values:s,shape:a}=Wh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[r,s,a]}case"StringToHashBucketFast":return[Wh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},HG=(e,t,n)=>{switch(e.op){case"Cast":return[ce(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let r=I("axis",e,t,n);return[yn(I("x",e,t,n),r)]}case"Squeeze":{let r=I("axis",e,t,n);return[Is(I("x",e,t,n),r)]}case"Reshape":return[U(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[Tk(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[wr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let r=I("blockShape",e,t,n),s=I("paddings",e,t,n);return[$h(I("x",e,t,n),r,s)]}case"BatchToSpaceND":{let r=I("blockShape",e,t,n),s=I("crops",e,t,n);return[kh(I("x",e,t,n),r,s)]}case"DepthToSpace":{let r=I("blockSize",e,t,n),s=I("dataFormat",e,t,n).toUpperCase();return[dk(I("x",e,t,n),r,s)]}case"BroadcastTo":return[zl(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[nk(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function IC(e,t,n,r){let s=((a,o,i)=>{switch(a.category){case"arithmetic":return M(()=>wG(a,o,i));case"basic_math":return M(()=>kG(a,o,i));case"control":return _G(a,o,i);case"convolution":return M(()=>EG(a,o,i));case"creation":return M(()=>AG(a,o,i));case"dynamic":return $G(a,o,i);case"evaluation":return M(()=>FG(a,o,i));case"image":return M(()=>OG(a,o,i));case"graph":return M(()=>DG(a,o,i));case"logical":return M(()=>MG(a,o,i));case"matrices":return M(()=>LG(a,o,i));case"normalization":return M(()=>BG(a,o,i));case"reduction":return M(()=>zG(a,o,i));case"slice_join":return M(()=>WG(a,o,i));case"sparse":return M(()=>VG(a,o,i));case"spectral":return M(()=>UG(a,o,i));case"string":return M(()=>GG(a,o,i));case"transformation":return M(()=>HG(a,o,i));case"hash_table":return PG(a,o,i,r);case"custom":let c=ZS(a.op);if(c&&c.customExecutor)return c.customExecutor(new xG(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var SC=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,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 n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}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 CC(e,t,n,r){let s=new Set,a=[],o=null,i=null,c=new Set,u=Object.keys(e).map(p=>Kn(p)[0]),l=[];r!=null&&(l=r.map(p=>Kn(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((TC(p)||YG(p)||ZG(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(p.name),n[p.name]==null&&u.indexOf(p.name)===-1&&l.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{c.has(h.name)||(c.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function jG(e,t,n){let{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map(l=>Kn(l)[0]).map(l=>e.nodes[l]),i=e.initNodes;o.forEach(l=>{r.has(l.name)&&a.push(l)}),e.weights.forEach(l=>{r.has(l.name)&&a.push(l)}),i!=null&&i.forEach(l=>{r.has(l.name)&&a.push(l)});let c=new Set,u=[];for(;a.length>0;){let l=a.pop();c.add(l.name),t[l.name]||u.push(l),l.children.forEach(d=>{!c.has(d.name)&&r.has(d.name)&&d.inputs.every(p=>c.has(p.name))&&a.push(d)})}return u}var qG=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],KG=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],XG=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function TC(e){return qG.indexOf(e.op)>=0}function YG(e){return KG.indexOf(e.op)>=0}function ZG(e){return XG.indexOf(e.op)>=0}var Px=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(n=>{this._functionExecutorMap[n]=new Px(e.functions[n],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(n=>e[n].map(r=>r.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 n=e.map(s=>s.name).sort(),r=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=CC(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;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 [${a}]`);if(r.length>0){let o=t.map(c=>c.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${r}]`)}return jG(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(l=>this.graph.nodes[Kn(l)[0]]),s=t.map(l=>Kn(l)[0]),a=s.map(l=>this.graph.nodes[l]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(r,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let c={},u={};return M(()=>{let l=new SC(this.weightMap,c,u,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=Kn(f),b=[];b[g]=e[f],d[m]=b});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=IC(m,d,l,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,l,p,s,h)}}return this.parent==null&&l.dispose(p),t.map(f=>wn(f,d,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let c=QU(i.name,n,r);c!=null&&c.forEach(u=>{if(u&&!u.kept&&!s.has(u.id)){let l=o[u.id];l===1?(u.dispose(),delete o[u.id]):l!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},s={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new SC(this.weightMap,r,s,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>wn(d,o,a)),c=i.map(d=>d.id),u=Object.keys(e).map(d=>e[d].id),l=new Set([...c,...u,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(h=>{h&&!h.kept&&!h.isDisposed&&!l.has(h.id)&&h.dispose()})}),this.parent==null&&a.dispose(l),i}async executeFunctionAsync(e,t,n){let r=e.reduce((s,a,o)=>(s[this.inputs[o].name]=a,s),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let s=Object.keys(e),a=s.map(y=>this.graph.nodes[Kn(y)[0]]),o=n.map(y=>Kn(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:c,missingInputs:u,dynamicNode:l,syncInputs:d}=CC(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[v,x]=Kn(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let y=this.processStack(a,p,t,h,g,m,o,f,c);await Promise.all(y)}l==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=i.filter(y=>!TC(y)&&!wn(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw l!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${u}]. ${y}`)}return h}processStack(e,t,n,r,s,a,o,i,c){let u=[];for(;t.length>0;){let l=t.pop();n.currentContext=l.contexts;let d="";if(l.node.op==="Enter"&&I("isConstant",l.node,r,n)&&([d]=Es(l.node.name,n)),r[l.node.name]==null){let p=IC(l.node,r,n,this._resourceManager);d||([d]=Es(l.node.name,n));let h=n.currentContext;w.isPromise(p)?u.push(p.then(f=>(r[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,l.node,r,n,a,o,i),this.processChildNodes(l.node,t,n,r,s,c),f))):(r[d]=p,this.checkTensorForDisposal(d,l.node,r,n,a,o,i),this.processChildNodes(l.node,t,n,r,s,c))}else this.processChildNodes(l.node,t,n,r,s,c)}return u}processChildNodes(e,t,n,r,s,a){e.children.forEach(o=>{let[i]=Es(o.name,n);s[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(c=>!!wn(c,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(c=>!!wn(c,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Kn(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,c)=>a[c]===-1||a[c]===i);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Kn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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[n]=Kn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},JG=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},QG="?tfjs-format=file",eH="model.json",NC=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new JG}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Kt.browserHTTPRequest(e,this.loadOptions);else{let t=Kt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Kt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=Kt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Px(bC.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=bC.Instance.transformGraph(e.modelInitializer);this.initializer=new Px(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Kt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ee)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function tH(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},WC=class extends Zt{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(Q().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new WC(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(n){throw new Error(`Error thrown while initializing video stream: ${n.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,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[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=[],n=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),r({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,n=new Float32Array(e.length*t);return e.forEach((r,s)=>n.set(r,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),Vn(n,t)}},VC=class extends Zt{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=Ue([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-r)/2,o=s+n,i=r+a;this.cropBox=Dr([a,s,i,o],[1,4])}else this.cropBox=Dr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Q().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 n=new VC(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=Lo.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return M(()=>{let t=yn(ce(e,"float32"),0),n;n=qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return U(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},UC=class{},GC=class extends Zt{split(e){return new EH(this,e)}},EH=class extends GC{constructor(e,t){super();this.upstream=e,this.impl=new AH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},AH=class extends Lx{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},$H=class extends Zt{decodeUTF8(){return new FH(this)}},FH=class extends GC{constructor(e){super();this.upstream=e,this.impl=new DH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},DH=class extends Lx{constructor(e){super();if(this.upstream=e,Q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=N0();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 n;return Q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},HC=class extends $H{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Q().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof 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UC{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return jC(this.url)?new qC(this.url,this.fileOptions).iterator():RH(this.url,this.fileOptions)}};function OH(e,t={}){return new zC(new KC(e),t)}function MH(e){let t=Mx(e);return Xn(async()=>t)}function LH(e){return Xn(async()=>{let t=await e();return Mx(()=>t.next())})}async function BH(e,t){return VC.create(e,t)}async function zH(e){return WC.create(e)}var WH="3.9.0";function we(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var VH=ts.whereImpl,Df=class extends ul{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new vp(this,ws())}nextDataId(){return Df.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Q().get("IS_NODE")&&_.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,s){this.data.set(e,{values:t,dtype:r,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return _.mergeRealAndImagArrays(r,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>w.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return ws().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=w.now();return e(),{kernelMs:w.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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o=w.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=ur({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),o=[o[0]],_.assertAxesAreInnerMostDims("argMin",o,c.shape.length);let[l,d]=_.computeOutAndReduceShapes(c.shape,o),p=w.sizeFromShape(l),h=w.makeZerosTypedArray(p,"int32"),f=w.sizeFromShape(d),m=n.data.get(c.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(l,"int32",h)}var p5={kernelName:pl,backendName:"cpu",kernelFunc:d5},h5=at(qi,e=>Math.asin(e)),f5={kernelName:qi,backendName:"cpu",kernelFunc:h5},m5=at(Ki,e=>Math.asinh(e)),g5={kernelName:Ki,backendName:"cpu",kernelFunc:m5},b5=at(Xi,e=>Math.atan(e)),y5={kernelName:Xi,backendName:"cpu",kernelFunc:b5},v5=Mt((e,t)=>Math.atan2(e,t)),x5=Jt(Zi,v5),w5={kernelName:Zi,backendName:"cpu",kernelFunc:x5},k5=at(Yi,e=>Math.atanh(e)),I5={kernelName:Yi,backendName:"cpu",kernelFunc:k5};function Yx(e,t,n,r,s,a){let o=s.strideHeight,i=s.strideWidth,c=s.dilationHeight,u=s.dilationWidth,l=s.effectiveFilterHeight,d=s.effectiveFilterWidth,p=s.padInfo.top,h=s.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Be(s.outShape,n),g=m.values,b=s.outShape[1]*s.outShape[2]*s.outShape[3],y=s.outShape[2]*s.outShape[3],v=s.outShape[3];for(let x=0;x<s.batchSize;++x){let k=x*b,C=x*r[0];for(let N=0;N<s.inChannels;++N)for(let F=0;F<s.outHeight;++F){let 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P=Math.min(t.inWidth,d+O),T=Number.NEGATIVE_INFINITY,L=-1;for(let G=v;G<x;G+=o){let j=G-y;for(let q=N;q<F;q+=i){let K=q-C;for(let ee=$;ee<P;ee+=c){let te=ee-O,ne=e.get(m,G,q,ee,g);ne>=T&&(T=ne,L=j*l*d+K*l+te)}}}n.set(L,m,b,k,R,g)}}}return n}function C5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;we(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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l=_.computePool3DInfo(a.shape,o,i,1,c,u),d=l.strideDepth,p=l.strideHeight,h=l.strideWidth,f=l.filterDepth,m=l.filterHeight,g=l.filterWidth,b=l.dilationDepth,y=l.dilationHeight,v=l.dilationWidth,x=l.effectiveFilterDepth,k=l.effectiveFilterHeight,C=l.effectiveFilterWidth,N=x-1-l.padInfo.front,F=C-1-l.padInfo.left,R=k-1-l.padInfo.top,O=Be(a.shape,"float32"),$=1/(f*m*g),P=n.bufferSync(s);for(let T=0;T<l.batchSize;++T)for(let L=0;L<l.inChannels;++L)for(let G=0;G<l.inDepth;++G)for(let j=0;j<l.inHeight;++j)for(let q=0;q<l.inWidth;++q){let K=G-N,ee=j-R,te=q-F,ne=0;for(let re=0;re<x;re+=b){let J=(K+re)/d;if(!(J<0||J>=l.outDepth||Math.floor(J)!==J))for(let oe=0;oe<k;oe+=y){let ie=(ee+oe)/p;if(!(ie<0||ie>=l.outHeight||Math.floor(ie)!==ie))for(let ue=0;ue<C;ue+=v){let fe=(te+ue)/h;if(fe<0||fe>=l.outWidth||Math.floor(fe)!==fe)continue;ne+=P.get(T,J,ie,fe,L)}}}O.set(ne*$,T,G,j,q,L)}return n.makeTensorInfo(O.shape,O.dtype,O.values)}var A5={kernelName:Cp,backendName:"cpu",kernelFunc:E5};function $5(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;we([s,a],"avgPoolGrad");let{filterSize:i,strides:c,pad:u}=r,l=_.computePool2DInfo(o.shape,i,c,1,u),d=l.strideHeight,p=l.strideWidth,h=l.filterHeight,f=l.filterWidth,m=l.dilationHeight,g=l.dilationWidth,b=l.effectiveFilterHeight,y=l.effectiveFilterWidth,v=y-1-l.padInfo.left,x=b-1-l.padInfo.top,k=Be(o.shape,"float32"),C=1/(h*f),N=n.data.get(s.dataId).values,F=Be(s.shape,"float32",N);for(let R=0;R<l.batchSize;++R)for(let O=0;O<l.inChannels;++O)for(let $=0;$<l.inHeight;++$)for(let P=0;P<l.inWidth;++P){let T=$-x,L=P-v,G=0;for(let j=0;j<b;j+=m){let q=(T+j)/d;if(!(q<0||q>=l.outHeight||Math.floor(q)!==q))for(let K=0;K<y;K+=g){let ee=(L+K)/p;if(ee<0||ee>=l.outWidth||Math.floor(ee)!==ee)continue;G+=F.get(R,q,ee,O)}}k.set(G*C,R,$,P,O)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var F5={kernelName:Sp,backendName:"cpu",kernelFunc:$5};function D5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,scale:a,offset:o,mean:i,variance:c}=t;w.assert(i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),we([s,i,c,a,o],"batchNorm");let{varianceEpsilon:u}=r;u==null&&(u=.001);let l=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(c.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(l.length),g=f.length,b=h.length,y=p.length,v=d.length,x=0,k=0,C=0,N=0;for(let F=0;F<l.length;++F)m[F]=f[x++]+(l[F]-d[k++])*h[C++]/Math.sqrt(p[N++]+u),x>=g&&(x=0),k>=v&&(k=0),C>=b&&(C=0),N>=y&&(N=0);return n.makeTensorInfo(s.shape,s.dtype,m)}var R5={kernelName:Ya,backendName:"cpu",kernelFunc:D5};function P5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;we([s],"batchToSpaceND");let i=a.reduce((b,y)=>b*y),c=_.getReshaped(s.shape,a,i),u=_.getPermuted(c.length,a.length),l=_.getReshapedPermuted(s.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(l,o,a.length),h=bt({inputs:{x:s},backend:n,attrs:{shape:c}}),f=ur({inputs:{x:h},backend:n,attrs:{perm:u}}),m=bt({inputs:{x:f},backend:n,attrs:{shape:l}}),g=ri({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var O5={kernelName:Ji,backendName:"cpu",kernelFunc:P5};function M5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,c=n.data.get(a.dataId).values,u=Vx(i,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var L5={kernelName:Tp,backendName:"cpu",kernelFunc:M5};function B5(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=_.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var z5={kernelName:bb,backendName:"cpu",kernelFunc:B5},W5=at(Xs,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),V5={kernelName:Xs,backendName:"cpu",kernelFunc:W5},U5=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(w.sizeFromShape(t.shape)),s=n.data.get(t.dataId),a=s.complexTensorInfos.real,o=s.complexTensorInfos.imag,i=n.data.get(a.dataId).values,c=n.data.get(o.dataId).values;for(let u=0;u<i.length;u++){let l=i[u],d=c[u];r[u]=Math.hypot(l,d)}return n.makeOutput(r,t.shape,"float32")},G5={kernelName:fl,backendName:"cpu",kernelFunc:U5};function gu(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.imag,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}var H5={kernelName:Wp,backendName:"cpu",kernelFunc:gu};function bu(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=_.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>w.sizeFromShape(m.shape)>0);if(i.length===1)return is({inputs:{x:i[0]},backend:n});let c=i.map(m=>m.shape);if(_.assertParamsConsistent(c,a),i[0].dtype==="complex64"){let m=i.map(x=>ni({inputs:{input:x},backend:n})),g=i.map(x=>gu({inputs:{input:x},backend:n})),b=bu({inputs:m,backend:n,attrs:{axis:a}}),y=bu({inputs:g,backend:n,attrs:{axis:a}}),v=Yn({inputs:{real:b,imag:y},backend:n});return m.forEach(x=>n.disposeIntermediateTensorInfo(x)),g.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(y),v}let u=i.map(m=>{let g=w.sizeFromShape(m.shape.slice(a));return bt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),l=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=_.computeOutShape(u.map(m=>m.shape),1);let d=u[0].shape[0]===1,p=Ux(l,o,t[0].dtype,d),h=_.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var j5={kernelName:Qi,backendName:"cpu",kernelFunc:bu};function WT(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:c,dilations:u,dimRoundingMode:l}=r;we([s,a],"conv2d");let d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,a.shape,o,u,i,l,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,b=p.padInfo.left,y=p.padInfo.top,v=p.dataFormat==="channelsLast",x=new 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n.makeTensorInfo(y.shape,y.dtype,y.values)}var X5={kernelName:_p,backendName:"cpu",kernelFunc:K5};function Y5(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:c,dataFormat:u,dimRoundingMode:l}=r;we([s,a],"conv2dBackpropInput");let d=w.computeStrides(a.shape),p=w.computeStrides(s.shape),h=_.convertConv2DDataFormat(u),f=_.computeConv2DInfo(o,a.shape,i,1,c,l,!1,h),m=new Bt(f.inShape,"float32"),g=m.values,b=n.data.get(s.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=d,{batchSize:C,filterHeight:N,filterWidth:F,inChannels:R,inHeight:O,inWidth:$,outChannels:P,outHeight:T,outWidth:L,strideHeight:G,strideWidth:j}=f;h=f.dataFormat;let q=N-1-f.padInfo.top,K=F-1-f.padInfo.left,ee=h==="channelsLast",te=m.strides[0],ne=ee?m.strides[1]:m.strides[2],re=ee?m.strides[2]:1,J=ee?1:m.strides[1],oe=p[0],ie=ee?p[1]:p[2],ue=ee?p[2]:1,fe=ee?1:p[1];for(let Se=0;Se<C;++Se)for(let Ce=0;Ce<R;++Ce)for(let Ne=0;Ne<O;++Ne){let 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u=w.computeStrides(s.shape),l=w.computeStrides(a.shape),d=_.computeConv3DInfo(s.shape,c,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,b=d.filterWidth,y=new Bt(d.filterShape,"float32"),v=y.values,[x,k,C,N]=y.strides,F=n.data.get(a.dataId).values,[R,O,$,P]=l,T=n.data.get(s.dataId).values,[L,G,j,q]=u,K=d.padInfo.front,ee=d.padInfo.left,te=d.padInfo.top;for(let ne=0;ne<m;++ne){let re=Math.max(0,Math.ceil((K-ne)/p)),J=Math.min(d.outDepth,(d.inDepth+K-ne)/p),oe=ne*x;for(let ie=0;ie<g;++ie){let ue=Math.max(0,Math.ceil((te-ie)/h)),fe=Math.min(d.outHeight,(d.inHeight+te-ie)/h),Se=ie*k+oe;for(let Ce=0;Ce<b;++Ce){let Ne=Math.max(0,Math.ceil((ee-Ce)/f)),Pe=Math.min(d.outWidth,(d.inWidth+ee-Ce)/f),_e=Ce*C+Se;for(let ct=0;ct<d.inChannels;++ct){let tt=ct*N+_e;for(let nt=0;nt<d.outChannels;++nt){let Je=0;for(let ot=0;ot<d.batchSize;++ot){let ze=ot*L,En=ot*R;for(let St=re;St<J;++St){let en=(ne+St*p-K)*G+ze,mr=St*O+En;for(let dn=ue;dn<fe;++dn){let 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ct=_e-fe,tt=Math.max(0,Math.ceil(ct/oe)),nt=Math.min(ne,(T+ct)/oe);for(let Je=0;Je<K;++Je){let ot=Je-Se,ze=Math.max(0,Math.ceil(ot/ie)),En=Math.min(re,(L+ot)/ie);for(let St=0;St<ee;++St){let Bn=St-Ce,en=Math.max(0,Math.ceil(Bn/ue)),mr=Math.min(J,(G+Bn)/ue),dn=0;for(let Qn=tt;Qn<nt;++Qn){let er=Qn*oe-ct;for(let tn=ze;tn<En;++tn){let tr=tn*ie-ot;for(let nr=en;nr<mr;++nr){let zn=nr*ue-Bn,qr=v*Ne+x*Qn+k*tn+C*nr,hs=F*(T-1-er)+R*(L-1-tr)+O*(G-1-zn)+$*Pe;for(let zs=0;zs<te;++zs){let Ni=y[qr+zs],Kr=N[hs+zs];dn+=Ni*Kr}}}}h[f*Ne+m*_e+g*Je+b*St+Pe]=dn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var rj={kernelName:Ap,backendName:"cpu",kernelFunc:nj},sj=at(Wa,e=>Math.cos(e)),aj={kernelName:Wa,backendName:"cpu",kernelFunc:sj},oj=at(Va,e=>Math.cosh(e)),ij={kernelName:Va,backendName:"cpu",kernelFunc:oj};function 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new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var hj={kernelName:$p,backendName:"cpu",kernelFunc:pj};function fj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;w.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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yj={kernelName:Fp,backendName:"cpu",kernelFunc:bj};function vj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:u,inputShape:l}=r;we([s,a],"depthwiseConv2DNativeBackpropInput");let d=w.computeStrides(s.shape),p=w.computeStrides(a.shape),h=_.computeConv2DInfo(l,a.shape,o,i,c,u,!0),f=new Bt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(s.dataId).values,[x,k,C]=d,N=n.data.get(a.dataId).values,[F,R,O]=p,{batchSize:$,filterHeight:P,filterWidth:T,inChannels:L,inHeight:G,inWidth:j,outChannels:q,outHeight:K,outWidth:ee,strideHeight:te,strideWidth:ne}=h,re=P-1-h.padInfo.top,J=T-1-h.padInfo.left,oe=q/L;for(let ie=0;ie<$;++ie)for(let ue=0;ue<L;++ue)for(let fe=0;fe<G;++fe){let Se=fe-re,Ce=Math.max(0,Math.ceil(Se/te)),Ne=Math.min(K,(P+Se)/te);for(let Pe=0;Pe<j;++Pe){let _e=Pe-J,ct=Math.max(0,Math.ceil(_e/ne)),tt=Math.min(ee,(T+_e)/ne),nt=0;for(let Je=Ce;Je<Ne;++Je){let ot=Je*te-Se;for(let ze=ct;ze<tt;++ze){let 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kj={kernelName:Rp,backendName:"cpu",kernelFunc:wj},Ij={kernelName:gl,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:s}=e,{strides:a,pad:o,dilations:i}=n,c=t,u=c.data.get(r.dataId).values,l=r.shape.length,d=c.data.get(s.dataId).values,p=s.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:C,filterWidth:N,dilationHeight:F,dilationWidth:R,outShape:O}=_.computeDilation2DInfo(r.shape,s.shape,a,o,"NHWC",i),$=w.sizeFromShape(O),P=O.length,T=w.getArrayFromDType(r.dtype,$);for(let G=0;G<h;++G)for(let j=0;j<b;++j){let q=j*x-v.top;for(let K=0;K<y;++K){let ee=K*k-v.left;for(let te=0;te<g;++te){let ne=Number.MIN_SAFE_INTEGER;for(let J=0;J<C;++J){let oe=q+J*F;if(oe>=0&&oe<f)for(let ie=0;ie<N;++ie){let ue=ee+ie*R;if(ue>=0&&ue<m){let fe=w.locToIndex([G,oe,ue,te],l,w.computeStrides(r.shape)),Se=w.locToIndex([J,ie,te],p,w.computeStrides(s.shape)),Ce=u[fe]+d[Se];Ce>ne&&(ne=Ce)}}}let 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Nj(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:c}=_.decodeEinsumEquation(s,a.length);_.checkEinsumDimSizes(o.length,c,a);let{path:u,steps:l}=_.getEinsumComputePath(i,c),d=l.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of l[m]){let{permutationIndices:b,expandDims:y}=_.getEinsumPermutation(h,c[g]),v;_.isIdentityPermutation(b)?v=a[g]:(v=ur({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=bt({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=Pf({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(u[m]>=0&&(p=vd({inputs:{x:p},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var _j={kernelName:Mp,backendName:"cpu",kernelFunc:Nj};function Ej(e){let{inputs:t,backend:n}=e,{dy:r,y:s}=t;we([r,s],"eluGrad");let a=new 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r=w.sizeFromShape(e.shape),s=n.data.get(e.dataId),a=n.data.get(s.complexTensorInfos.real.dataId).values,o=n.data.get(s.complexTensorInfos.imag.dataId).values;if(Vj(r)){let i=Qx(a,o,r,t,n),c=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(c,"float32",i.real),l=n.makeTensorInfo(c,"float32",i.imag),d=n.makeTensorInfo([],"float32",w.createScalarValue(r,"float32")),p=is({inputs:{x:d},backend:n}),h=Jx.kernelFunc({inputs:{a:u,b:d},backend:n}),f=Jx.kernelFunc({inputs:{a:l,b:p},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=_.mergeRealAndImagArrays(a,o),c=Uj(i,r,t);return _.splitRealAndImagArrays(c)}}function Vj(e){return(e&e-1)==0}function Qx(e,t,n,r,s){if(n===1)return{real:e,imag:t};let 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dK={kernelName:Oc,backendName:"cpu",kernelFunc:lK},pK={kernelName:Sl,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;we(n,"square");let s=r.data.get(n.dataId).values,a=new Float32Array(s.length);for(let i=0;i<s.length;++i){let c=s[i];a[i]=c*c}return{dataId:r.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},hK=at(Zs,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),fK={kernelName:Zs,backendName:"cpu",kernelFunc:hK};function mK(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:c,endMask:u,ellipsisMask:l,newAxisMask:d,shrinkAxisMask:p}=r;we(s,"stridedSlice");let{nonStrided:h,$begin:f,$strides:m,size:g,newShape:b,outShape:y}=mn.sliceInfo(s.shape,a,o,i,c,u,l,d,p),v=bt({inputs:{x:s},backend:n,attrs:{shape:b}}),x;if(h){let C=ri({inputs:{x:v},backend:n,attrs:{begin:f,size:g}});x=bt({inputs:{x:C},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(C)}else if(y.some(C=>C===0))x=n.makeTensorInfo(y,s.dtype,[]);else{let 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v2(e){if(Hf==null){let t=cs(e);Hf=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Hf)}function x2(e){if(e===0)return 0;let t,n=cs(e);return dr(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:dr(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function dr(e,t){return e.getExtension(t)!=null}function aw(e){try{if(cs(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function w2(e){if(e===0)return!1;let t=cs(e);if(e===1){if(!dr(t,"OES_texture_float"))return!1}else if(!dr(t,"EXT_color_buffer_float"))return!1;return ow(t)}function k2(e){if(e===0)return!1;let t=cs(e);if(e===1){if(!dr(t,"OES_texture_float")||!dr(t,"WEBGL_color_buffer_float"))return!1}else{if(dr(t,"EXT_color_buffer_float"))return ow(t);let r="EXT_color_buffer_half_float";if(dr(t,r)){let s=t.getExtension(r);return oX(t,s)}return!1}return ow(t)}function ow(e){let t=nw(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,s=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,s,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function oX(e,t){let n=nw(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let s=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,s,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(o),i}function I2(e){return e!==2?!1:cs(e).fenceSync!=null}function vu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Te=Q();Te.registerFlag("HAS_WEBGL",()=>Te.getNumber("WEBGL_VERSION")>0);Te.registerFlag("WEBGL_VERSION",()=>aw(2)?2:aw(1)?1:0);Te.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Te.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Te.get("WEBGL_VERSION")===2);Te.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Te.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Te.registerFlag("WEBGL_PACK",()=>Te.getBool("HAS_WEBGL"));Te.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_CLIP",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_PACK_REDUCE",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_LAZILY_UNPACK",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_CONV_IM2COL",()=>Te.getBool("WEBGL_PACK"));Te.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>y2(Te.getNumber("WEBGL_VERSION")));Te.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>v2(Te.getNumber("WEBGL_VERSION")));Te.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Te.getNumber("WEBGL_VERSION");return e===0?0:x2(e)});Te.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Te.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Pl.isMobile());Te.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>w2(Te.getNumber("WEBGL_VERSION")));Te.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Te.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Te.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Te.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>k2(Te.getNumber("WEBGL_VERSION")));Te.registerFlag("WEBGL_FENCE_API_ENABLED",()=>I2(Te.getNumber("WEBGL_VERSION")));Te.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Te.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Te.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Te.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Pl.isMobile()&&Te.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Te.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Te.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Te.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Te.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function kn(){let e,t,n,r,s,a,o,i,c,u;return Q().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",s="texture",a="outputColor",o="out vec4 outputColor;",i=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,c="",u=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",r="varying",s="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,c=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:c,defineRound:u}}function ii(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function jf(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function iX(e,t){let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function cX(e,t,n="index"){let r=e.map((a,o)=>o),s=iX(r,t);return s.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${s[o]}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${s[o]}`:`index -= ${e[o]} * ${s[o]}`;return`${i}; ${c};`}).join("")}function iw(e){let t=w.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function cw(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var S2=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:C2}=_;function uX(e,t,n){let r=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=uw(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:r.push(`uniform int ${h.name}Shape;`);break;case 2:r.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:r.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:r.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}r.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:r.push("uniform int outShape;");break;case 2:r.push("uniform ivec2 outShape;"),r.push("uniform int outShapeStrides;");break;case 3:r.push("uniform ivec3 outShape;"),r.push("uniform ivec2 outShapeStrides;");break;case 4:r.push("uniform ivec4 outShape;"),r.push("uniform ivec3 outShapeStrides;");break;default:break}r.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{r.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let s=r.join(`
`),a=e.map(h=>lX(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=kn(),c=hX(i),u,l,d=gX(i);return t.isPacked?(u=dX(t.logicalShape,o,n.enableShapeUniforms),l=mX(i)):(u=pX(t.logicalShape,o,n.enableShapeUniforms),l=fX(i)),n.packedInputs&&(d+=xX),[d,c,l,s,u,a,n.userCode].join(`
`)}function xu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return FX(e,t);case 1:return RX(e,t);case 2:return OX(e,t);case 3:return LX(e,t);case 4:return zX(e,t);case 5:return WX(e);case 6:return VX(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function T2(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return $X(e);case 1:return DX(e,t);case 2:return PX(e,t);case 3:return MX(e,t);default:return BX(e,t)}}function lX(e,t,n=!1,r){let s="";n?s+=T2(e,r):s+=xu(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=UX(e,t):s+=GX(e,t)),s}function dX(e,t,n){switch(e.length){case 0:return N2();case 1:return wX(e,t,n);case 2:return EX(e,t,n);case 3:return IX(e,t,n);default:return CX(e,t,n)}}function pX(e,t,n){switch(e.length){case 0:return N2();case 1:return kX(e,t,n);case 2:return AX(e,t,n);case 3:return SX(e,t,n);case 4:return TX(e,t,n);case 5:return NX(e,t);case 6:return _X(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function hX(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function fX(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function mX(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function gX(e){return`${e.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${e.varyingFs} vec2 resultUV;
${e.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;
${e.defineSpecialNaN}
${e.defineSpecialInf}
${e.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${bX}
${yX}
${vX}
`}var bX=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,yX=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,vX=`
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);
}
`,xX=`
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 N2(){return`
int getOutputCoords() {
return 0;
}
`}function wX(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${r[1]}.0);
}
`:r[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${r[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
}
`}function kX(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function IX(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec3(b, r, c);
}
`}function SX(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${jf(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let r=ii(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
return ivec3(r, c, d);
}
`}function CX(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),a=s*Math.ceil(e[e.length-2]/2),o=a,i="",c="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
int b${u} = index / ${o};
index -= b${u} * ${o};
`+i,c=`b${u}, `+c;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec${e.length}(${c});
}
`}function TX(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${jf(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let r=ii(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
return ivec4(r, c, d, d2);
}
`}function NX(e,t){let n=ii(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function _X(e,t){let n=ii(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function EX(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));
}
`;let s=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.x * ${r[1]} + resTexRC.y;
int r = 2 * (index / ${s});
int c = imod(index, ${s}) * 2;
return ivec2(r, c);
}
`}function AX(e,t,n){return w.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function ci(e){return`offset${e}`}function $X(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=kn();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function FX(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;let[s,a]=e.shapeInfo.texShape;if(s===1&&a===1)return`
float ${r}() {
return sampleTexture(${n}, halfCR);
}
`;let o=ci(n);if(t)return`
float ${r}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,c]=e.shapeInfo.texShape;return`
float ${r}() {
vec2 uv = uvFromFlat(${i}, ${c}, ${o});
return sampleTexture(${n}, uv);
}
`}function DX(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=kn();if(t)return`
vec4 ${r}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];return`
vec4 ${r}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function RX(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${r}(int index) {
${wu(e)}
}
`;let s=e.shapeInfo.texShape,a=s[0],o=s[1];if(o===1&&a===1)return`
float ${r}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=ci(n);return o===1?t?`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${r}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function PX(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],c=kn();if(a!=null&&w.arraysEqual(n,a))return t?`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return ${c.texture2D}(${r}, uv);
}
`:`
vec4 ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${c.texture2D}(${r}, uv);
}
`;if(t)return`
vec4 ${s}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${c.texture2D}(${r}, uv);
}
`;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=Math.ceil(n[1]/2);return`
vec4 ${s}(int row, int col) {
vec2 uv = packedUVfrom2D(${l}, ${u[0]}, ${u[1]}, row, col);
return ${c.texture2D}(${r}, uv);
}
`}function OX(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`;let p=a[0],h=a[1];return`
float ${s}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`}let{newShape:o,keptDims:i}=w.squeezeShape(n),c=o;if(c.length<n.length){let p=ku(e,c),h=["row","col"];return`
${xu(p,t)}
float ${s}(int row, int col) {
return ${s}(${Iu(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${wu(e)}
}
`;let u=a[0],l=a[1],d=ci(r);return l===1?t?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${r}, uv);
}
`:u===1?t?`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
return sampleTexture(${r}, uv);
}
`:t?`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${u}, ${l}, index);
return sampleTexture(${r}, uv);
}
`}function MX(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=ku(e,p),m=["b","row","col"];return`
${T2(f,t)}
vec4 ${s}(int b, int row, int col) {
return ${s}(${Iu(m,h)});
}
`}let i=kn();if(t)return`
vec4 ${s}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${r}, uv);
}
`;let c=o[0],u=o[1],l=Math.ceil(n[2]/2),d=l*Math.ceil(n[1]/2);return`
vec4 ${s}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${c}, ${u}, ${d}, ${l}, b, row, col);
return ${i.texture2D}(${r}, uv);
}
`}function LX(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:c}=w.squeezeShape(n),u=i;if(u.length<n.length){let m=ku(e,u),g=["row","col","depth"];return`
${xu(m,t)}
float ${s}(int row, int col, int depth) {
return ${s}(${Iu(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${wu(e)}
}
`;let l=e.shapeInfo.texShape,d=l[0],p=l[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${s}(int row, int col, int depth) {
int stride1 = ${r}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`;if(p===o&&h==null)return t?`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${r}, uv);
}
`;let f=ci(r);return t?`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${r}Shape[1] * ${r}Shape[2];
int stride1 = ${r}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${r}, uv);
}
`}function BX(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=kn();if(t)return`
vec4 ${r}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${s.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,c=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=c[0],l=c[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${r}(${h}) {
int index = ${f};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${u});
return ${s.texture2D}(${n}, uv);
}
`}function zX(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:c,keptDims:u}=w.squeezeShape(n);if(c.length<n.length){let y=ku(e,c),v=["row","col","depth","depth2"];return`
${xu(y,t)}
float ${s}(int row, int col, int depth, int depth2) {
return ${s}(${Iu(v,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${wu(e)}
}
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${r}Shape[3];`,m=`int stride1 = ${r}Shape[2] * stride2;`,g=`int stride0 = ${r}Shape[1] * stride1;`;if(h===i&&l==null)return t?`
float ${s}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;if(h===a&&l==null)return t?`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}TexShape[1], ${r}TexShape[0]);
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;let b=ci(r);return t?`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${b});
return sampleTexture(${r}, uv);
}
`:`
float ${s}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${b});
return sampleTexture(${r}, uv);
}
`}function WX(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=t[4],a=t[3]*s,o=t[2]*a,i=t[1]*o,{newShape:c,keptDims:u}=w.squeezeShape(t);if(c.length<t.length){let m=ku(e,c),g=["row","col","depth","depth2","depth3"];return`
${xu(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Iu(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${s})) +
depth3;
${wu(e)}
}
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&l==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&l==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=ci(n);return`
float ${r}(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 * ${o} + depth * ${a} +
depth2 * ${s} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function VX(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:s,keptDims:a}=w.squeezeShape(t);if(s.length<t.length){let g=ku(e,s),b=["row","col","depth","depth2","depth3","depth4"];return`
${xu(g)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Iu(b,a)});
}
`}let o=t[5],i=t[4]*o,c=t[3]*i,u=t[2]*c,l=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${l}, ${u}, ${c}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${wu(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===l&&d==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${c}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&d==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=ci(n);return`
float ${r}(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 * ${l} + col * ${u} + depth * ${c} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function wu(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function UX(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=C2(e.shapeInfo.logicalShape,t.logicalShape),c=dt(o),u=o-a,l,d=["x","y","z","w","u","v"];a===0?l="":o<2&&i.length>=1?l="coords = 0;":l=i.map(y=>`coords.${d[y+u]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((y,v)=>`coords.${d[v+u]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!b)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let y=a-2,v=a-1;i.indexOf(y)>-1&&i.indexOf(v)>-1?h="return vec4(outputValue.x);":i.indexOf(y)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(v)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${s}() {
${c} coords = getOutputCoords();
${l}
vec4 outputValue = get${r}(${p});
${h}
}
`}function GX(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,c=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===c&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
float ${s}() {
return sampleTexture(${n}, resultUV);
}
`;let u=dt(c),l=C2(e.shapeInfo.logicalShape,t.logicalShape),d=c-i,p,h=["x","y","z","w","u","v"];i===0?p="":c<2&&l.length>=1?p="coords = 0;":p=l.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return c<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${s}() {
${u} coords = getOutputCoords();
${p}
return get${r}(${f});
}
`}function dt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function uw(e,t,n){let{newShape:r,keptDims:s}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,c=!e&&a>1&&!w.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:c,uniformShape:c?i:t,keptDims:s}}function ku(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Iu(e,t){return t.map(n=>e[n]).join(", ")}function HX(e,t,n,r){let s=n.map((v,x)=>{let k={logicalShape:v.shape,texShape:v.isUniform?null:v.texData.texShape,isUniform:v.isUniform,isPacked:v.isUniform?!1:v.texData.isPacked,flatOffset:null};return v.texData!=null&&v.texData.slice!=null&&v.texData.slice.flatOffset>0&&(k.flatOffset=v.texData.slice.flatOffset),{name:t.variableNames[x],shapeInfo:k}}),a=s.map(v=>v.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=uX(s,o,t),c=e.createProgram(i),u=null,l=e.getUniformLocation(c,"NAN",!1);Q().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d=!1,p={},h={},f={};for(let v=0;v<t.variableNames.length;v++){let x=t.variableNames[v];p[x]=e.getUniformLocation(c,x,d),p[`offset${x}`]=e.getUniformLocation(c,`offset${x}`,d),t.enableShapeUniforms&&(h[`${x}Shape`]=e.getUniformLocation(c,`${x}Shape`,d),f[`${x}TexShape`]=e.getUniformLocation(c,`${x}TexShape`,d))}let m,g,b;t.enableShapeUniforms&&(m=e.getUniformLocation(c,"outShape",d),b=e.getUniformLocation(c,"outShapeStrides",d),g=e.getUniformLocation(c,"outTexShape",d));let y=[];return t.customUniforms&&t.customUniforms.forEach((v,x)=>{y[x]=e.getUniformLocation(c,v.name,d)}),{program:t,source:i,webGLProgram:c,uniformLocations:p,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:l,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:b,outTexShapeLocation:g}}function _2(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!w.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${s} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,c=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(i,c))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${c} must match`)})}function jX(e,t,n,r,s){t.program.enableShapeUniforms||(_2(t.inShapeInfos,n),_2([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Q().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,u)=>{let l=t.program.variableNames[u],d=t.uniformLocations[l],p=t.uniformLocations[`offset${l}`],h=t.inShapesLocations[`${l}Shape`],f=t.inTexShapesLocations[`${l}TexShape`];if(h){let{uniformShape:m}=uw(t.program.packedInputs,c.shape,c.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,c.texData.texShape[0],c.texData.texShape[1]),d!=null){if(c.isUniform){if(w.sizeFromShape(c.shape)<2)e.gl.uniform1f(d,c.uniformValues[0]);else{let m=c.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}c.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture,d,u)}});let i=t.outShapeLocation;if(i)switch(r.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(r.shape));break;default:break}if(t.outShapeStridesLocation){let c=w.computeStrides(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(c));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(c));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(c));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&s&&t.program.customUniforms.forEach((c,u)=>{let l=t.customUniformLocations[u],d=s[u];if(c.type==="float")e.gl.uniform1fv(l,d);else if(c.type==="vec2")e.gl.uniform2fv(l,d);else if(c.type==="vec3")e.gl.uniform3fv(l,d);else if(c.type==="vec4")e.gl.uniform4fv(l,d);else if(c.type==="int")e.gl.uniform1iv(l,d);else if(c.type==="ivec2")e.gl.uniform2iv(l,d);else if(c.type==="ivec3")e.gl.uniform3iv(l,d);else if(c.type==="ivec4")e.gl.uniform4iv(l,d);else throw Error(`uniform type ${c.type} is not supported yet.`)}),e.executeProgram()}function qX(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let c=o.texData.texShape,{useSqueezeShape:u,uniformShape:l,keptDims:d}=uw(e.packedInputs,o.shape,c),p="",h="",f="";if(l.length===1&&e.packedInputs){let k=[Math.ceil(c[0]/2),Math.ceil(c[1]/2)];p=`${k[0]>1}_${k[1]>1}`}else if(l.length===2&&!e.packedInputs)h=`${l[0]>1}_${l[1]>1}`;else if(l.length>2&&!e.packedInputs){let k=w.computeStrides(l);f=`${k[0]===c[1]}_${k[k.length-1]===c[1]}`}let m=o.shape.length,g=l.length===2&&w.arraysEqual(o.shape,c),b=w.sizeFromShape(o.shape)===1,y=_.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(c,n.texData.texShape),x=e.packedInputs||l.length>2?"":`${c[0]>1}_${c[1]>1}`;r+=`${m}_${v}_${u?d:""}_${l.length}_${b}_${y}_${g}_${p}_${h}_${f}_${x}_${i}`}else{let c=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${c}_${i}`}});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s+`${Q().getNumber("WEBGL_VERSION")}`,a}function pr(e){return Q().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var KX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=wd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=kn();this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?jf(["r","c","d"],e):ii(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},XX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=wd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=kn();this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?jf(["r","c","d"],e):ii(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},YX=class{constructor(e){this.variableNames=["A"],this.outTexUsage=lr.DOWNLOAD;let t=kn();this.outputShape=e,this.userCode=`
${S2}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},ZX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=lr.DOWNLOAD;let t=kn();this.outputShape=e,this.userCode=`
${S2}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},JX=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=kn();this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length);let r="result";t&&(r="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?cw():iw(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${r}, 0., 0., 0.);
}
`}},QX=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=kn();this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;r+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?cw():iw(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${r}
${n.output} = ${s};
}
`}},E2={};Fe(E2,{bindVertexProgramAttributeStreams:()=>L2,createBufferFromOutputTexture:()=>W2,createFloat16MatrixTexture:()=>R2,createFloat16PackedMatrixTexture:()=>M2,createFloat32MatrixTexture:()=>D2,createIndexBuffer:()=>F2,createPackedMatrixTexture:()=>O2,createUnsignedBytesMatrixTexture:()=>P2,createVertexBuffer:()=>$2,createVertexShader:()=>A2,downloadByteEncodedFloatMatrixFromOutputTexture:()=>U2,downloadFloat32MatrixFromBuffer:()=>V2,downloadMatrixFromPackedOutputTexture:()=>H2,downloadPackedMatrixFromBuffer:()=>G2,getInternalFormatForFloat16MatrixTexture:()=>dw,getInternalFormatForFloat16PackedMatrixTexture:()=>fw,getInternalFormatForFloat32MatrixTexture:()=>lw,getInternalFormatForPackedMatrixTexture:()=>hw,getInternalFormatForUnsignedBytesMatrixTexture:()=>pw,uploadDenseMatrixToTexture:()=>B2,uploadPixelDataToTexture:()=>z2});function A2(e){let t=kn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return n2(e,n)}function $2(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return o2(e,t)}function F2(e){let t=new Uint16Array([0,1,2,2,1,3]);return i2(e,t)}function Td(e,t,n,r,s,a){u2(t,n);let o=c2(e),i=e.TEXTURE_2D;return ye(e,()=>e.bindTexture(i,o)),ye(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ye(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ye(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),ye(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),ye(e,()=>e.texImage2D(i,0,r,t,n,0,s,a,null)),ye(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function lw(e){return e.internalFormatFloat}function D2(e,t,n,r){let[s,a]=kd(t,n);return Td(e,s,a,lw(r),r.textureFormatFloat,e.FLOAT)}function dw(e){return e.internalFormatHalfFloat}function R2(e,t,n,r){let[s,a]=kd(t,n);return Td(e,s,a,dw(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function pw(e){return e.downloadTextureFormat}function P2(e,t,n,r){let[s,a]=kd(t,n);return Td(e,s,a,pw(r),e.RGBA,e.UNSIGNED_BYTE)}function hw(e){return e.internalFormatPackedFloat}function O2(e,t,n,r){let[s,a]=yu(t,n);return Td(e,s,a,hw(r),e.RGBA,e.FLOAT)}function fw(e){return e.internalFormatPackedHalfFloat}function M2(e,t,n,r){let[s,a]=yu(t,n);return Td(e,s,a,fw(r),e.RGBA,r.textureTypeHalfFloat)}function L2(e,t,n){let r=0,s=3*4,a=3*4+2*4;return ye(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),rw(e,t,"clipSpacePos",n,3,a,r)&&rw(e,t,"uv",n,2,a,s)}function B2(e,t,n,r,s,a){ye(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,c;s instanceof Uint8Array?(o=new Uint8Array(n*r*4),i=e.UNSIGNED_BYTE,c=e.RGBA):(o=new Float32Array(n*r*4),i=e.FLOAT,c=a.internalFormatPackedFloat),o.set(s),ye(e,()=>e.texImage2D(e.TEXTURE_2D,0,c,n,r,0,e.RGBA,i,o)),ye(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function z2(e,t,n){ye(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?ye(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ye(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ye(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function W2(e,t,n,r){let s=e.createBuffer();ye(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,s));let i=4*4*t*n;return ye(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),ye(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ye(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),s}function V2(e,t,n){let r=e,s=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}function U2(e,t,n,r){let[s,a]=kd(t,n),o=4,i=new Uint8Array(KK(t*n,o));return ye(e,()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function G2(e,t,n,r,s,a,o,i){let c=e,u=new Float32Array(XK(a,o));return c.bindBuffer(c.PIXEL_PACK_BUFFER,t),c.getBufferSubData(c.PIXEL_PACK_BUFFER,0,u),c.bindBuffer(c.PIXEL_PACK_BUFFER,null),u}function H2(e,t,n){let r=new Float32Array(t*n*4);return ye(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var j2=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Q().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,QT(t,e)):this.gl=cs(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(Q().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Id(this.gl,s),dr(this.gl,a))this.textureHalfFloatExtension=Id(this.gl,a);else if(Q().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),dr(this.gl,r))this.colorBufferHalfFloatExtension=Id(this.gl,r);else if(Q().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",dr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(dr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=$2(this.gl),this.indexBuffer=F2(this.gl),this.framebuffer=l2(this.gl),this.textureConfig=nw(this.gl,this.textureHalfFloatExtension)}get debug(){return Q().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ye(e,()=>e.finish()),ye(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ye(e,()=>e.deleteFramebuffer(this.framebuffer)),ye(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ye(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ye(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),D2(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),R2(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),P2(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),z2(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),B2(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),M2(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),O2(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(sw(this.gl,this.framebuffer),this.outputTexture=null),ye(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>U2(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return G2(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return V2(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=W2(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Q().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=r.clientWaitSync(s,0,0);return a===r.ALREADY_SIGNALED||a===r.CONDITION_SATISFIED},t=s}else Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>H2(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=r2(t,e);this.vertexShader==null&&(this.vertexShader=A2(t));let r=s2(t);return ye(t,()=>t.attachShader(r,this.vertexShader)),ye(t,()=>t.attachShader(r,n)),a2(t,r),this.debug&&zf(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=L2(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ye(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&zf(this.gl,this.program),ye(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?p2(this.gl,e,t):h2(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ye(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),f2(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=yu(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&zf(this.gl,this.program),Sd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ye(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ye(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Id(this.gl,Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=e7(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Wf(this.gl,e,this.framebuffer),this.debug&&Sd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Wf(this.gl,this.outputTexture,this.framebuffer),this.debug&&Sd(this.gl)):sw(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;Wf(r,e,this.framebuffer),this.debug&&Sd(r),this.outputTexture=e,ye(r,()=>r.viewport(0,0,t,n)),ye(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),ye(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function e7(e){let t=0;for(;t<e.length&&e[t]();++t);return 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z7=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 n=In("rc",t),r=dt(t),s=V7(t,e,n),a=U7(t,e[e.length-1],e[e.length-2],n),o=G7(e,n);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${s}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${o}));
}
}
`}}};function W7(e,t){let n=[];for(let r=0;r<=1;r++)for(let s=0;s<=1;s++){let a=`${r===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function V7(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let s=e-2;s<e;s++)r+=`${n[s]} >= ${t[s]}`,s<e-1&&(r+="||");return r}function U7(e,t,n,r){if(e===1)return"";let s=r.slice(-2);return`
int r = ${s[0]};
int c = ${s[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function G7(e,t){let n=e.length,r=W7(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${r[0]}),
cEdge ? 0. : getA(${r[1]}),
rEdge ? 0. : getA(${r[2]}),
rEdge || cEdge ? 0. : getA(${r[3]})`}var Z2=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length);let n="";for(let r=0;r<4;r++){let s="thisRC = rc;";r%2==1&&(s+="thisRC.z += 1;"),r>1&&(s+="thisRC.y += 1;"),n+=`
${s}
${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${r}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${r>0?"}":""}
`}this.userCode=`
${H7(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?cw():iw(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function H7(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?cX(["r","c","d"],"inputShape"):ii(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var j7=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=Q2(t,n),s=eN(e,r,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=J2(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[s].shift();return this.usedTextures[s].push(i),i}let o;return r===sn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===sn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===sn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===sn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===sn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let s=Q2(n,r),a=eN(t,s,r);a in this.freeTextures||(this.freeTextures[a]=[]);let o=J2(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=Q().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let c=this.usedTextures[a],u=c.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");c.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 q7(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function J2(e,t,n,r,s){let a=K7(t,r),o;if(s){let[c,u]=yu(e[0],e[1]);o=c*u}else{let[c,u]=kd(e[0],e[1]);o=c*u}let i=q7(n,a);return o*i}function K7(e,t){switch(e){case sn.PACKED_2X2_FLOAT32:return hw(t);case sn.PACKED_2X2_FLOAT16:return fw(t);case sn.UNPACKED_FLOAT32:return lw(t);case sn.UNPACKED_FLOAT16:return dw(t);case sn.PACKED_4X1_UNSIGNED_BYTE:return pw(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function X7(e){return Q().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?sn.PACKED_2X2_FLOAT32:sn.UNPACKED_FLOAT32:e?sn.PACKED_2X2_FLOAT16:sn.UNPACKED_FLOAT16}function Q2(e,t){if(e===lr.UPLOAD)return sn.PACKED_2X2_FLOAT32;if(e===lr.RENDER||e==null)return X7(t);if(e===lr.DOWNLOAD||e===lr.PIXELS)return sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function eN(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ya=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Ur="if (isnan(x)) return x;",Y7="return x;",tN="return abs(x);",Z7="return (x >= 0.0) ? x : (exp(x) - 1.0);",J7=Ur+`
return (x < 0.0) ? 0.0 : x;
`,Q7=Ur+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,qf="return x;",e9="return 1.0 / (1.0 + exp(-1.0 * x));",t9="return x;",n9=`
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;
`,r9=`
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;
`,s9=`
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;
`,a9="return 1.0 / (1.0 + exp(-1.0 * x));",Su=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},o9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=In("rc",t),r=dt(t),s=B7(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${s});
setOutput(getChannel(packedInput, ${o}));
}
`}},i9=ts.whereImpl,c9=1e-7,u9=1e-4,Kf={};function l9(e){return e in Kf||(Kf[e]={}),Kf[e]}var d9=Q().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),p9=600;function h9(){return Q().global.screen==null?1024:Q().global.screen.height*Q().global.screen.width*window.devicePixelRatio*p9/1024/1024}var Xf=class extends ul{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.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Q().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=cs(Q().getNumber("WEBGL_VERSION"));this.binaryCache=l9(Q().getNumber("WEBGL_VERSION")),this.gpgpu=new j2(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 j7(this.gpgpu),this.numMBBeforeWarning=h9(),this.texData=new vp(this,ws())}nextDataId(){return Xf.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Q().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Q().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:lr.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}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--}}move(e,t,n,r,s){if(Q().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:lr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new Su(o,qf):d=new ya(o,qf);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:r}],r),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let c=this.activeTimers!=null,u;c&&(u=w.now());let l;if(r==="complex64"){let d=this.readSync(s.real.dataId),p=this.readSync(s.imag.dataId);l=_.mergeRealAndImagArrays(d,p)}else l=this.getValuesFromTexture(e);return c&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:s,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(s!=null){let h;i?h=new Su(r,qf):h=new ya(r,qf);let f=this.runWebGLProgram(h,[{dataId:e,shape:r,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let c=null,u;if(a!=="complex64"&&Q().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);c=this.gpgpu.createBufferFromTexture(h.texture,...Bf(r))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];l=_.mergeRealAndImagArrays(f,m)}else if(c==null)l=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(r);l=this.gpgpu.downloadFloat32MatrixFromBuffer(c,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),c!=null){let h=this.gpgpu.gl;ye(h,()=>h.deleteBuffer(c))}let d=this.convertAndCacheOnCPU(e,l),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ws().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>w.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!e2(n))throw Q().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=w.sizeFromShape(t);if(Q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Bf(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(d),h}let a=Q().getBool("WEBGL_PACK")&&r===!0,o=a?Vf(t):t,i=a?new ZX(o):new YX(o),c=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(c.dataId),l=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(c),l}timerAvailable(){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((c,u)=>({name:a[u],ms:c})).map(c=>`${c.name}: ${c.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,c=this.dataRefCount.get(i);c>1?this.dataRefCount.set(i,c-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=d9){return Q().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){_.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return i9(e.shape,t)}packedUnaryOp(e,t,n){let r=new Su(e.shape,t),s=this.compileAndRun(r,[e],n);return ws().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=K2(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(Q().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,tN,e.dtype);let t=new ya(e.shape,tN),n=this.compileAndRun(t,[e]);return ws().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return ws().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new o9(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new z7(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ai(e.shape),...oi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[ai(t),...oi(t)],a=new Z2(s,n),o=!0,i=[n],c=this.runWebGLProgram(a,[r],e.dtype,i,o);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:s}=t,a=Vf(r),o,i=Bf(a);n?o=new XX(a):o=new KX(a);let c=!0,u=[i],l=this.runWebGLProgram(o,[{shape:a,dtype:s,dataId:e}],s,u,c);return{dtype:s,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,s=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===wd.DENSE){let m=Bf(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(a.shape)===0)return o.values=w.getTypedArrayFromDType(a.dtype,0),a;let i=[],c=t.map(m=>{if(m.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(m.dataId);if(g.texture==null){if(!e.packedInputs&&w.sizeFromShape(m.shape)<=Q().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!Cd(g.shape,m.shape)){let b=m,y=m.shape;m.shape=g.shape,m=this.packedReshape(m,y),i.push(m),g=this.texData.get(m.dataId),b.shape=y}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let u={shape:a.shape,texData:o,isUniform:!1},l=qX(e,c,u),d=this.getAndSaveBinary(l,()=>HX(this.gpgpu,e,c,u)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),jX(this.gpgpu,d,c,u,r),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Q().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=w.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Q().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&s===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Q().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=M(()=>{if(!Q().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Q().getBool("DEBUG");Q().set("DEBUG",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(Q().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?c9:u9}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let c=this.activeTimers!=null,u;c&&(u=w.now());let l=t.texShape;if(l==null&&(l=b2(n,i),t.texShape=l),s!=null){let d=Vf(n),p,h=l[1],f=l[0],m=s instanceof Uint8Array;i?([h,f]=yu(l[0],l[1]),p=new QX(d,m)):p=new JX(d,m);let g=this.makeTensorInfo([f,h],r);m?this.texData.get(g.dataId).usage=lr.PIXELS:this.texData.get(g.dataId).usage=lr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,s);let b=[[f,h]],y=!0,v=this.runWebGLProgram(p,[g],r,b,y),x=this.texData.get(v.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(v.dataId),t.values=null,c&&(this.uploadWaitMs+=w.now()-u)}else{let d=this.acquireTexture(l,o,r,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=f9(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!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,r)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}};Xf.nextDataId=0;function f9(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var m9="3.9.0";function nN(){Q().set("WEBGL_FORCE_F16_TEXTURES",!0)}Pl.isBrowser()&&vh("webgl",()=>new Xf,2);var g9={forceHalfFloat:nN},rN=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Cu=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=pr(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Yf=`
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;
`,Nd=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=_.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=pr(s);let a="";if(r)if(s===0||w.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${dt(s)} coords = getOutputCoords();
`,s===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=In("coords",s);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[s-2]} + 1) >= outShape[${s} - 2];
bool nextColOutOfBounds =
(${i[s-1]} + 1) >= outShape[${s} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[s-2]} + 1) >= ${this.outputShape[s-2]};
bool nextColOutOfBounds =
(${i[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 Zn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var b9={kernelName:Ja,backendName:"webgl",kernelFunc:Zn};function va(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=Zn({inputs:{x:r},backend:n}),c=Zn({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:c},a}var y9={kernelName:Np,backendName:"webgl",kernelFunc:va},sN="return (a < 0.) ? b * a : a;",aN=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function v9(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(aN,s.shape,o.shape):new Cu(sN,s.shape,o.shape),c=n.runWebGLProgram(i,[s,o],s.dtype);return n.disposeIntermediateTensorInfo(o),c}var x9={kernelName:Qa,backendName:"webgl",kernelFunc:v9},oN="return (a < 0.) ? b * a : a;",iN=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function w9(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(iN,r.shape,s.shape):new Cu(oN,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)}var k9={kernelName:ho,backendName:"webgl",kernelFunc:w9},cN="if (isnan(x)) return x;",I9=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,S9=`
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 Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,c=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,c);return i.makeTensorInfo(o.shape,c,p)}let u=Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,l;return u?l=new Su(o.shape,t):l=new ya(o.shape,e),i.runWebGLProgram(l,[o],c)}}function an({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:c,b:u}=o,l=i;if(r&&c.dtype==="complex64"){let f=l.texData.get(c.dataId),m=l.texData.get(u.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,C={dataId:x.dataId,dtype:x.dtype,shape:c.shape},N={dataId:k.dataId,dtype:k.dtype,shape:u.shape},F=new Cu(e,c.shape,u.shape);return l.runWebGLProgram(F,[C,N],yr(x.dtype,k.dtype))}),y=va({inputs:{real:g,imag:b},backend:l});return l.disposeIntermediateTensorInfo(g),l.disposeIntermediateTensorInfo(b),y}let d=a||yr(c.dtype,u.dtype);if((c.dtype==="string"||u.dtype==="string"||l.shouldExecuteOnCPU([c,u]))&&s!=null){let f=l.texData.get(c.dataId).values,m=l.texData.get(u.dataId).values,g=c.dtype==="string"?_.fromUint8ToStringArray(f):f,b=c.dtype==="string"?_.fromUint8ToStringArray(m):m,[y,v]=s(c.shape,u.shape,g,b,d),x=l.makeTensorInfo(v,d),k=l.texData.get(x.dataId);return k.values=y,x}let p=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Nd(t,c.shape,u.shape,n):h=new Cu(e,c.shape,u.shape),l.runWebGLProgram(h,[c,u],d)}}function Zf(e,t=!1){if(e==="linear")return t?t9:Y7;if(e==="relu")return t?r9:J7;if(e==="elu")return t?n9:Z7;if(e==="relu6")return t?s9:Q7;if(e==="prelu")return t?iN:oN;if(e==="leakyrelu")return t?aN:sN;if(e==="sigmoid")return t?a9:e9;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var uN=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,c=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=pr(this.outputShape.length);let u=r?e[1]:e[2],l=Math.ceil(u/2),d=r?"i * 2, rc.y":"rc.y, i * 2",p=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:c?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),c&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${l}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${l}; i++) {
int batchA = ${y};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${b}
${g}
setOutput(result);
}
`}},lN={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},dN=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},pN="return a * b;";function gw(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=_.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),c=n.texData.get(s.dataId),u=new dN(lN.REAL,r.shape,s.shape),l=new dN(lN.IMAG,r.shape,s.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:s.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:s.shape}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(l,d,"float32"),f=va({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),c=n.texData.get(s.dataId),[u,l]=x7(r.shape,s.shape,i.values,c.values,a),d=n.makeTensorInfo(l,a),p=n.texData.get(d.dataId);return p.values=u,d}let o;return Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Nd(pN,r.shape,s.shape):o=new Cu(pN,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var C9={kernelName:co,backendName:"webgl",kernelFunc:gw};function T9(e,t,n){let r=[ai(e.shape),...oi(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[ai(t),...oi(t)],o=new Z2(a,r),i=!0,c=[r],u=n.runWebGLProgram(o,[s],e.dtype,c,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function me(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),c=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(c);w.assert(i===u,()=>`The new shape (${c}) has ${u} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let l=o.texData.get(s.dataId);return l.isPacked&&!Cd(s.shape,c)&&!(l.texture!==null&&Cd(l.shape,c))?T9(s,c,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:c,dtype:s.dtype})}var N9={kernelName:Nc,backendName:"webgl",kernelFunc:me},hN=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,c="sumValue += dot(values, ones);";if(t!=null){let l=1/t;c=`sumValue += dot(values * ${w.isInt(l)?l.toPrecision(2):l}, ones);`}let u="";s%n>0&&(u=`
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) {
${u}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${o}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${c}
}
int inIdx = inOffset + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${c}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${c}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${c}
}
setOutput(sumValue);
}
`}},_9=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let c=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?c="sumValue":t==="prod"?c="prodValue":t==="all"?c="allValue":t==="any"&&(c="anyValue");let u=Math.floor(n/4)*4,l=n%4,d=`
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 = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(o="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(o="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";s%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${u};
if (${l===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${l===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${l===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${c});
}
`}};function E9(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function ui(e,t,n,r){let s=E9(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:c,outSize:u}=s[o],l,d;n==="mean"?l=o===0?new hN({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:u},i):new hN({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:u}):l=new _9({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:u},n),d=a,a=r.runWebGLProgram(l,[a],t),d.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(d)}return a}var A9=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let r=dt(this.rank),s=$9(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function $9(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var F9=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=dt(this.rank),s=Y2("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=s[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,c=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${c};
if(${i}) {
result[1] = ${c};
}
--${s[this.rank-1]};
if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${c};
if(${i}) {
result[3] = ${c};
}
}
setOutput(result);
}
`}};function Jf(e,t,n){let r=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new F9(e.shape,t):new A9(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function D9(e,t,n,r){let s=t,a=e.shape.length,o=w.parseAxisParam(s,e.shape),i=o,c=_.getAxesPermutation(i,a),u=c!=null,l=e;u&&(l=Jf(e,c,r),i=_.getInnerMostAxes(i.length,a)),_.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=_.computeOutAndReduceShapes(l.shape,i),h=d;n&&(h=_.expandShapeToKeepDim(d,o));let f=w.sizeFromShape(p),g=w.sizeFromShape(e.shape)/f,b=me({inputs:{x:l},attrs:{shape:[g,f]},backend:r}),y=hh(e.dtype),v=ui(b,y,"sum",r),x=me({inputs:{x:v},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),u&&r.disposeIntermediateTensorInfo(l),x}function Qf(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return D9(s,a,o,n)}var R9={kernelName:Io,backendName:"webgl",kernelFunc:Qf};function Sn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,c=new Array(i);for(let l=0;l<c.length;l++)c[l]=s.shape[a[l]];let u;if(o.shouldExecuteOnCPU([s])){let d=o.texData.get(s.dataId).values,p=mw(d,s.shape,s.dtype,a,c);u=o.makeTensorInfo(c,s.dtype);let h=o.texData.get(u.dataId);h.values=p}else u=Jf(s,a,o);return u}var P9={kernelName:Eo,backendName:"webgl",kernelFunc:Sn},fN=1e3;function em({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:c=null}){let u=e.shape.length,l=t.shape.length,d=n?e.shape[u-2]:e.shape[u-1],p=r?t.shape[l-1]:t.shape[l-2],h=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[l-2]:t.shape[l-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),v=b===y||b===1||y===1;w.assert(u>=2&&l>=2&&v,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let k=(b>y?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);w.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let C=n?[b,d,h]:[b,h,d],N=r?[y,f,p]:[y,p,f],F=me({inputs:{x:e},backend:s,attrs:{shape:C}}),R=me({inputs:{x:t},backend:s,attrs:{shape:N}}),O=[F,R],$=Math.max(b,y),P=n?F.shape[1]:F.shape[2],T=a!=null,L=o!=null,G=c==="leakyrelu",j=c!=null?Zf(c,!0):null,q=T||L||G||j!=null,K;if((h===1||f===1)&&P>fN&&q===!1){let te=F,ne=R;n&&(te=Sn({inputs:{x:F},backend:s,attrs:{perm:[0,2,1]}}),O.push(te)),r&&(ne=Sn({inputs:{x:R},backend:s,attrs:{perm:[0,2,1]}}),O.push(ne));let re=f!==1,J=f===1,oe=te;re&&(oe=me({inputs:{x:te},backend:s,attrs:{shape:[$,P,1]}}),O.push(oe));let ie=f===1?2:1,ue=ne;J&&(ue=me({inputs:{x:ne},backend:s,attrs:{shape:[$,1,P]}}),O.push(ue));let fe=gw({inputs:{a:oe,b:ue},backend:s});K=Qf({inputs:{x:fe},backend:s,attrs:{axis:ie,keepDims:!0}}),O.push(fe)}else{let te=yr(e.dtype,t.dtype),ne=new uN(C,N,[$,h,f],n,r,T,j,L,G),re=[F,R];if(a!=null&&re.push(a),L&&re.push(o),G){let J=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));re.push(J),O.push(J)}K=s.runWebGLProgram(ne,re,te)}let ee=me({inputs:{x:K},backend:s,attrs:{shape:k}});O.push(K);for(let te of O)s.disposeIntermediateTensorInfo(te);return ee}function O9(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:c,transposeB:u,activation:l,leakyreluAlpha:d}=r;return em({a:s,b:a,transposeA:c,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:l})}var M9={kernelName:Ao,backendName:"webgl",kernelFunc:O9},mN="return abs(x);";function L9(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=K2(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Su(r.shape,mN):s=new ya(r.shape,mN),n.runWebGLProgram(s,[r],r.dtype)}var B9={kernelName:Vi,backendName:"webgl",kernelFunc:L9},z9=Ur+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,W9=Ke({opSnippet:z9}),V9={kernelName:Ui,backendName:"webgl",kernelFunc:W9},U9=Ur+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,G9=Ke({opSnippet:U9}),H9={kernelName:Gi,backendName:"webgl",kernelFunc:G9},gN="return a + b;",j9=an({opSnippet:gN,packedOpSnippet:gN,supportsComplex:!0,cpuKernelImpl:t7}),q9={kernelName:Ks,backendName:"webgl",kernelFunc:j9},K9=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${r};
setOutput(result);
}
`}},X9=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let r=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function tm(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Zn({inputs:{x:r[0]},backend:n});if(r.length>Q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),u=tm({inputs:r.slice(0,c),backend:n}),l=tm({inputs:r.slice(c),backend:n});return tm({inputs:[u,l],backend:n})}let s=r.map(c=>c.dtype).reduce((c,u)=>yr(c,u)),a=r.map(c=>c.shape),i=Q().getBool("WEBGL_PACK")?new X9(r[0].shape,a):new K9(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var Y9={kernelName:Da,backendName:"webgl",kernelFunc:tm};function Z9(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=_.getAxesPermutation(u,i),d=s;l!=null&&(d=Sn({inputs:{x:s},backend:n,attrs:{perm:l}}),u=_.getInnerMostAxes(u.length,i)),_.assertAxesAreInnerMostDims("all",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=me({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=ui(m,m.dtype,"all",n),b;if(o){let y=_.expandShapeToKeepDim(p,c);b=me({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=me({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(d),b}var J9={kernelName:Hi,backendName:"webgl",kernelFunc:Z9};function Q9(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=_.getAxesPermutation(u,i),d=s;l!=null&&(d=Sn({inputs:{x:s},backend:n,attrs:{perm:l}}),u=_.getInnerMostAxes(u.length,i)),_.assertAxesAreInnerMostDims("any",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=me({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=ui(m,m.dtype,"any",n),b;if(o){let y=_.expandShapeToKeepDim(p,c);b=me({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=me({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(d),b}var eY={kernelName:ji,backendName:"webgl",kernelFunc:Q9},tY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"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 * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},nY=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.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),r||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,c=dt(i),u=In("coords",i),l,d;if(a===1){d=i+1;let N=dt(d);l=`
${N} sourceLocR = ${N}(${u.join()}, 0);
++${u[i-1]};
${N} sourceLocG = ${N}(${u.join()}, 0);
++${u[i-2]};
${N} sourceLocA = ${N}(${u.join()}, 0);
--${u[i-1]};
${N} sourceLocB = ${N}(${u.join()}, 0);
--${u[i-2]};`}else d=i,l=`
${c} sourceLocR = coords;
++${u[i-1]};
${c} sourceLocG = coords;
++${u[i-2]};
${c} sourceLocA = coords;
--${u[i-1]};
${c} sourceLocB = coords;
--${u[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(N=>"int "+N),m=In("sourceLocR",d-1).concat("inIdx.r"),g=In("sourceLocG",d-1).concat("inIdx.g"),b=In("sourceLocB",d-1).concat("inIdx.b"),y=In("sourceLocA",d-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${b.join()}),
getBestIndicesAChannel(${y.join()})));`,k=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${b.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,C=r?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${C}
void main() {
${c} coords = getOutputCoords();
bool hasNextCol = ${u[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
${l}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${k};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${k};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${v}(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 bN(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=_.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},c=new tY(i,n,r==null),u=[t];r!=null&&u.push(r);let l=e.runWebGLProgram(c,u,"int32");if(l.shape[1]===1)return l;let d=bN(e,t,n,l);return e.disposeIntermediateTensorInfo(l),d}function yN(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=_.computeOptimalWindowSize(a),i=new nY(s,o,n,r==null),c=r==null?[t]:[t,r],u=e.runWebGLProgram(i,c,"int32");if(u.shape.length===t.shape.length){let l=yN(e,t,n,u);return e.disposeIntermediateTensorInfo(u),l}return u}function vN(e,t,n,r){let s=[n];if(_.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!Q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,c=t;i&&(c=e.unpackTensor(t),a.push(c));let[u,l]=_.computeOutAndReduceShapes(c.shape,s),d=w.sizeFromShape(l),p=me({inputs:{x:c},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=bN(e,p,r);a.push(h);let f=me({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return yN(e,t,r)}function rY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=Sn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),_.assertAxesAreInnerMostDims("argMax",[o[0]],c.shape.length);let l=vN(n,c,o[0],"max");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var sY={kernelName:Ra,backendName:"webgl",kernelFunc:rY};function aY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=_.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=Sn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=_.getInnerMostAxes(o.length,c.shape.length)),_.assertAxesAreInnerMostDims("argMin",[o[0]],c.shape.length);let l=vN(n,c,o[0],"min");return u.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var oY={kernelName:pl,backendName:"webgl",kernelFunc:aY},iY=Ur+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,cY=Ke({opSnippet:iY}),uY={kernelName:qi,backendName:"webgl",kernelFunc:cY},lY=Ur+"return log(x + sqrt(x * x + 1.0));",dY=Ke({opSnippet:lY}),pY={kernelName:Ki,backendName:"webgl",kernelFunc:dY},hY=Ur+`
return atan(x);
`,fY=Ke({opSnippet:hY}),mY={kernelName:Xi,backendName:"webgl",kernelFunc:fY},gY=I9+`
return atan(a, b);
`,bY=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+S9+`
return result;
`,yY=an({opSnippet:gY,packedOpSnippet:bY}),vY={kernelName:Zi,backendName:"webgl",kernelFunc:yY},xY=Ur+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,wY=Ke({opSnippet:xY}),kY={kernelName:Yi,backendName:"webgl",kernelFunc:wY},_d=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,l=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
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 < ${l};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d};
wC += ${u}) {
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 ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?s?m:g:`wR * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let y="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(a/4)*4,k=a%4,C=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${y}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
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 < ${l};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${u};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
getValue(batch, xR, xC + 3 * ${u}, d)
);
${C}
}
int xC = xCCorner + ${x};
if (${k===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${C}
} else if (${k===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${C}
} else if (${k===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${C}
}
}
setOutput(${v});
}
`}},bw=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,c=e.strideWidth,u=e.dilationDepth,l=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${c});
const ivec3 pads = ivec3(${m}, ${g}, ${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 < ${p};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${d}) {
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 ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?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 * ${h} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let C=Math.floor(a/4)*4,N=a%4,F=`
if (${y}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${c});
const ivec3 pads = ivec3(${m}, ${g}, ${b});
const float initializationValue = ${v};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${v});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${p};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${C}; wC += 4) {
int xC = xCCorner + wC * ${d};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
);
${F}
}
int xC = xCCorner + ${C};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${F}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${F}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
initializationValue
);
${F}
}
}
setOutput(${k});
}
}
`}};function IY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;vu(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let l=_.computePool2DInfo(s.shape,a,o,u,i,c);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return Zn({inputs:{x:s},backend:n});let d=new _d(l,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var SY={kernelName:Pa,backendName:"webgl",kernelFunc:IY};function CY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:c,dataFormat:u}=r,l=[1,1,1],d=_.computePool3DInfo(s.shape,a,o,l,i,c,u),p=new bw(d,"avg",!1);return n.runWebGLProgram(p,[s],"float32")}var TY={kernelName:hl,backendName:"webgl",kernelFunc:CY},NY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.top,l=c-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${u}, ${l});
const float avgMultiplier = float(${d});
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 < ${i};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${r}.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+= ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},_Y=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,u=e.dilationWidth,l=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=l-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
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 < ${l};
wD += ${i}) {
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 < ${d};
wR += ${c}) {
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 < ${p};
wC += ${u}) {
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(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function EY(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:c,pad:u,dimRoundingMode:l}=r,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,c,d,u,l),h=new _Y(p);return n.runWebGLProgram(h,[s],o.dtype)}var AY={kernelName:Cp,backendName:"webgl",kernelFunc:EY};function $Y(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;vu([s,a],"avgPoolGrad");let{filterSize:i,strides:c,pad:u}=r,l=_.computePool2DInfo(o.shape,i,c,1,u),d=new NY(l);return n.runWebGLProgram(d,[s],o.dtype)}var FY={kernelName:Sp,backendName:"webgl",kernelFunc:$Y};function DY(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return em({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var RY={kernelName:Oa,backendName:"webgl",kernelFunc:DY},PY=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},OY=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(_.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(_.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},MY=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;w.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:c}=n;c==null&&(c=.001);let u=[r,s,a],l=null;o!=null&&(l=o.shape,u.push(o));let d=null;i!=null&&(d=i.shape,u.push(i));let p=Q().getBool("WEBGL_PACK_NORMALIZATION")?new OY(r.shape,s.shape,a.shape,l,d,c):new PY(r.shape,s.shape,a.shape,l,d,c);return t.runWebGLProgram(p,u,u[0].dtype)},LY={kernelName:Ya,backendName:"webgl",kernelFunc:MY},BY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=dt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=zY(this.rank),r,s=e.map((a,o)=>`sourceLoc.${yw[o]} = start[${o}] + coords.${yw[o]};`);r=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
void main() {
${r}
setOutput(getSource(${n}));
}
`}},yw=["x","y","z","w","u","v"];function zY(e){if(e===1)return"sourceLoc";if(e<=6)return yw.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var WY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=dt(this.rank),n=In("coords",this.rank),r=In("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.y = ${a};
--${r[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${a};
}
}
`,c=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,l)=>`start[${l}]`).join()});`:e.map((u,l)=>`${r[l]} = ${n[l]} + start[${l}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${c}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function VY(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=mn.computeFlatOffset(t,w.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let c=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,c+1),a}function Tu(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,c]=mn.parseSliceParams(s,a,o);if(mn.assertParamsValid(s,i,c),w.sizeFromShape(c)===0)return n.makeTensorInfo(c,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let d=n.texData.get(s.dataId),p=N7(d.values,i,c,s.shape,s.dtype);return n.makeTensorInfo(c,s.dtype,p)}let{isPacked:u}=n.texData.get(s.dataId),l=mn.isSliceContinous(s.shape,i,c);if(u||!l){let d=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WY(c):new BY(c),p=[i];return n.runWebGLProgram(d,[s],s.dtype,p)}return n.uploadToGPU(s.dataId),VY(s,i,c,n)}var UY={kernelName:$c,backendName:"webgl",kernelFunc:Tu},GY=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),c=_.getReshaped(s.shape,a,i),u=_.getPermuted(c.length,a.length),l=_.getReshapedPermuted(s.shape,a,i),d=_.getSliceBeginCoords(o,a.length),p=_.getSliceSize(l,o,a.length),h=[],f=me({inputs:{x:s},backend:n,attrs:{shape:c}}),m=Sn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=me({inputs:{x:m},backend:n,attrs:{shape:l}}),b=Tu({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},HY={kernelName:Ji,backendName:"webgl",kernelFunc:GY};function jY(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),c=n.readSync(a.dataId),u=q2(i,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var qY={kernelName:Tp,backendName:"webgl",kernelFunc:jY},KY="return float(a != b);",xN=an({opSnippet:KY,cpuKernelImpl:k7,dtype:"bool"}),XY={kernelName:vc,backendName:"webgl",kernelFunc:xN};function Ed(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return Zn({inputs:{x:s.complexTensorInfos.real},backend:n})}var YY={kernelName:Kp,backendName:"webgl",kernelFunc:Ed},ZY="return float(int(x));";function JY(e,t){let n=new ya(e.shape,ZY),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function vw(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return Zn({inputs:{x:s},backend:n});let o=wt(s.shape),i=vw({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),c=va({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),c}if(s.dtype==="complex64"){let o=Ed({inputs:{input:s},backend:n}),i=vw({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=Zn({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return JY(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),c=xN({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),c}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var QY={kernelName:Ma,backendName:"webgl",kernelFunc:vw},wN="return ceil(x);",eZ=Ke({opSnippet:wN,packedOpSnippet:wN,cpuKernelImpl:r7}),tZ={kernelName:La,backendName:"webgl",kernelFunc:eZ},nZ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},rZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function sZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;Q().getBool("WEBGL_PACK_CLIP")?i=new rZ(s.shape):i=new nZ(s.shape);let c=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,c)}var aZ={kernelName:Xs,backendName:"webgl",kernelFunc:sZ},oZ=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 kN(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function iZ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new oZ(r.shape),o=[kN(r,s.complexTensorInfos.real),kN(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var cZ={kernelName:fl,backendName:"webgl",kernelFunc:iZ},uZ=class{constructor(e){this.outputShape=[],this.outputShape=_.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);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 n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},lZ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,r=n.length,s=dt(r),a=In("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let c=o[t],u=o.slice(-2),l=o.join(),d=`if (${c} < ${i[0]}) {
return getChannel(
getT0(${l}), vec2(${u.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
if (${c} < ${i[f]} && ${c} >= ${i[f-1]}) {
return getChannel(
getT${f}(${nm(o,c,m)}),
vec2(${nm(u,c,m)}));
}`}let p=i.length,h=i[i.length-1];d+=`
return getChannel(
getT${p}(${nm(o,c,h)}),
vec2(${nm(u,c,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${d}
}
void main() {
${s} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[r-1]} = ${a[r-1]} + 1;
if (${a[r-1]} < ${n[r-1]}) {
result.g = getValue(${a});
}
${a[r-2]} = ${a[r-2]} + 1;
if (${a[r-2]} < ${n[r-2]}) {
result.a = getValue(${a});
}
${a[r-1]} = ${a[r-1]} - 1;
if (${a[r-2]} < ${n[r-2]} &&
${a[r-1]} < ${n[r-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function nm(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function rm(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return Zn({inputs:{x:s.complexTensorInfos.imag},backend:n})}var dZ={kernelName:Wp,backendName:"webgl",kernelFunc:rm};function Nu(e,t,n){let r=e[0].dtype;if(r==="complex64"){let l=e.map(m=>Ed({inputs:{input:m},backend:n})),d=e.map(m=>rm({inputs:{input:m},backend:n})),p=Nu(l,t,n),h=Nu(d,t,n),f=va({inputs:{real:p,imag:h},backend:n});return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let l=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return me({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),d=l.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),p=_.computeOutShape(l.map(b=>b.shape),1),h=l[0].shape[0]===1,f=s7(d,p,r,h),m=_.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,r,f);return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>Q().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(e.length/2),d=Nu(e.slice(0,l),t,n),p=Nu(e.slice(l),t,n),h=Nu([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let l=new lZ(e.map(d=>d.shape),t);return n.runWebGLProgram(l,e,r)}let{tensors2D:a,outShape:o}=pZ(e,t,n),i=new uZ(a.map(l=>l.shape)),c=n.runWebGLProgram(i,a,r);a.forEach(l=>n.disposeIntermediateTensorInfo(l));let u=me({inputs:{x:c},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(c),u}function pZ(e,t,n){let r=_.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>me({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function IN(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=_.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>w.sizeFromShape(u.shape)>0);if(i.length===1)return Zn({inputs:{x:i[0]},backend:n});let c=i.map(u=>u.shape);return _.assertParamsConsistent(c,a),Nu(i,a,n)}var hZ={kernelName:Qi,backendName:"webgl",kernelFunc:IN},SN=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,c=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(r?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${i}, ${c});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${y}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${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 < ${d}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${l};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
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 (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${k}
${x}
setOutput(result);
}
`}},fZ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,u=e.dilationWidth,l=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${s}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
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 < ${l}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},mZ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=pr(this.outputShape.length);let{dataFormat:n}=t,r=kn(),s=n==="channelsLast",a=s?0:1,o=s?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,c="";for(let u=0;u<=1;u++)for(let l=0;l<=1;l++)c+=`
blockIndex = rc.y + ${l};
pos = rc.x + ${u};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${s}) {
innerDims = vec2(d1, ch);
result[${u*2+l}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${u*2+l}] = 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;
${c}
${r.output} = result;
}
`}};function CN({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let c=e.shape,u=r.texData.get(e.dataId),l=n.inChannels,d=c[0]*c[1]*c[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((d===1||p===1)&&l>fN)&&u.isPacked&&h&&u.texture!=null&&c[2]%2!=0&&w.arraysEqual(u.shape.slice(-3),c.slice(-3))){let x=c[0]*c[1]*(c[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},C=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Cd(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let N=me({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let F=em({a:k,b:N,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=r.texData.get(F.dataId);w.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=C,R.shape=n.outShape,g=Zn({inputs:{x:F},backend:r}),g.shape=n.outShape,b.push(F)}else{let x=h?c[0]*c[1]*c[2]:c[0]*c[2]*c[3],k=me({inputs:{x:e},backend:r,attrs:{shape:[1,x,n.inChannels]}}),C=me({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=em({a:k,b:C,transposeA:f,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=me({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),b.push(k),b.push(C),b.push(N)}for(let x of b)r.disposeIntermediateTensorInfo(x);return g}function TN({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:c,filterHeight:u,inChannels:l,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=c*u*l,g=p*d,b=[m,g],y=!0,v=!1,x=[],k=me({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),C=me({inputs:{x:t},backend:r,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(C);let N=new mZ(b,n),F=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=r.runWebGLProgram(N,[k],"float32",F),O=me({inputs:{x:R},backend:r,attrs:{shape:[1,b[0],b[1]]}});x.push(R),x.push(O);let $=s!=null,P=a!=null,T=i==="leakyrelu",L=i?Zf(i,!0):null,G=new uN(O.shape,C.shape,[1,g,n.outChannels],y,v,$,L,P,T),j=[O,C];if(s&&j.push(s),P&&j.push(a),T){let te=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));j.push(te),x.push(te)}let q=r.runWebGLProgram(G,j,"float32"),K=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],ee=me({inputs:{x:q},backend:r,attrs:{shape:K}});x.push(q);for(let te of x)r.disposeIntermediateTensorInfo(te);return ee}function gZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:c,dilations:u,dimRoundingMode:l}=r,d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,a.shape,o,u,i,l,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=CN({x:s,filter:a,convInfo:p,backend:n});else if(Q().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)h=TN({x:s,filter:a,convInfo:p,backend:n});else{let m=new SN(p);h=n.runWebGLProgram(m,[s,a],"float32")}let f=me({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var bZ={kernelName:Ba,backendName:"webgl",kernelFunc:gZ},yZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${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);
}
`}},vZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,c=a?1:2,u=a?2:3,l=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${l}];
ivec2 dyCorner = ivec2(coords[${c}], coords[${u}]) - 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) / ${r}.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 < ${n}; 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 = ${n} - 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);
}
`}},xZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,o=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 * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},wZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,c=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${c}, ${u});
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 < ${n}; 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 = ${n} - 1 - wR;
for (int wC = 0; wC < ${r}; 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);
int wCPerm = ${r} - 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 kZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:c,dimRoundingMode:u,filterShape:l}=r,d=_.convertConv2DDataFormat(c),p=_.computeConv2DInfo(s.shape,l,o,1,i,u,!1,d),h=new yZ(p);return n.runWebGLProgram(h,[s,a],"float32")}var IZ={kernelName:_p,backendName:"webgl",kernelFunc:kZ};function SZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:c,dataFormat:u,dimRoundingMode:l}=r,d=_.convertConv2DDataFormat(u),p=_.computeConv2DInfo(o,a.shape,i,1,c,l,!1,d),h=new vZ(p);return n.runWebGLProgram(h,[s,a],"float32")}var CZ={kernelName:za,backendName:"webgl",kernelFunc:SZ};function TZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r,u=_.computeConv3DInfo(s.shape,a.shape,o,c,i),l=new fZ(u);return n.runWebGLProgram(l,[s,a],"float32")}var NZ={kernelName:ml,backendName:"webgl",kernelFunc:TZ};function _Z(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:c}=r,u=_.computeConv3DInfo(s.shape,c,o,1,i),l=new xZ(u);return n.runWebGLProgram(l,[s,a],"float32")}var EZ={kernelName:Ep,backendName:"webgl",kernelFunc:_Z};function AZ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:c}=r,u=_.computeConv3DInfo(c,a.shape,i,1,o),l=new wZ(u);return n.runWebGLProgram(l,[s,a],"float32")}var $Z={kernelName:Ap,backendName:"webgl",kernelFunc:AZ},FZ=cN+`
return cos(x);
`,DZ=Ke({opSnippet:FZ}),RZ={kernelName:Wa,backendName:"webgl",kernelFunc:DZ},PZ=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,OZ=Ke({opSnippet:PZ}),MZ={kernelName:Va,backendName:"webgl",kernelFunc:OZ},LZ=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,c]=e,[u]=t,[l,d]=n;this.outputShape=[u,l,d,c];let p=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=l>1?[`${(o-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${y});
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 = ${g};
float width_scale = ${v};
float in_y = ${b};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${s}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${s}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},BZ=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:c,extrapolationValue:u}=r,l=new LZ(s.shape,a.shape,i,c,u);return n.runWebGLProgram(l,[s,a,o],"float32")},zZ={kernelName:ec,backendName:"webgl",kernelFunc:BZ},NN=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let r=e.length,s=t?"0.0":`getX(${_N(r,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${dt(r)} coords = getOutputCoords();
int end = ${EN(r,"coords")};
float val = ${s};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${EN(r,"coords")} = idx;
val += getX(${_N(r,"coords")});
}
setOutput(val);
}
`}};function _N(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function EN(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function WZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,c=s.shape.length,u=_.getAxesPermutation([a],c),l=s;u!=null&&(l=Sn({inputs:{x:s},backend:n,attrs:{perm:u}}));let d=_.getInnerMostAxes(1,c)[0];if(d!==c-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${s.shape.length-1} but got axis=${a}`);let p=l.shape[d],h=Zn({inputs:{x:l},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new NN(l.shape,!1,i),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(o){let f=new NN(l.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=_.getUndoAxesPermutation(u),m=Sn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(l),m}return h}var VZ={kernelName:Ua,backendName:"webgl",kernelFunc:WZ};function UZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let c=n.readSync(s.dataId),u=n.readSync(a.dataId),l=q2(c,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}else if(s.shape.length===2){let c=n.bufferSync(s),u=n.bufferSync(a),l=n7(c,u,o,i);return n.makeTensorInfo(l.shape,a.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var GZ={kernelName:$p,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,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 jZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r;w.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=s.shape[0],c=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],l=o==="NHWC"?s.shape[3]:s.shape[1],d=c*a,p=u*a,h=l/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new HZ(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var qZ={kernelName:tc,backendName:"webgl",kernelFunc:jZ},AN=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=pr(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,c="",u="";n&&(r?c=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?c=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:c=`
float activation(float x) {
${n}
}
`,u="result = activation(result);");let l=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${c}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
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 < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${l}
${u}
setOutput(result);
}
`}},$N=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=pr(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,c=e.dilationWidth,u=e.filterHeight,l=e.filterWidth,d=l,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<l;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;for(let g=0;g<u;g++){for(let b=0;b<l;b++)p+=`
xTexelC${b*2} = vec4(0.0);
xTexelC${b*2}Ready = 0;
xTexelC${b*2+1} = vec4(0.0);
xTexelC${b*2+1}Ready = 0;
xC${b} = vec4(0.0);`;p+=`
xR = xRCorner + ${g} * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let b=0;b<(d+1)/2;b++){let y=b*2;if(p+=`
xC = xCCorner + ${y*c};
`,i===1){if(y<l&&(o%2==1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
`,c===1&&y>0?p+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
`:p+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xC${y} = xTexelC${y};
`,y+1<l)){let v=o%2==0?w.nearestLargerEven(c):c;c%2==0&&o%2==1||c%2!=0&&o%2!=1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${v};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
`,c>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),p+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):v===1?p+=`
xC${y+1} = xTexelC${y};
`:p+=`
xCOffset = xC + ${v};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y+1} = xTexelC${y+1};
`}}else y<l&&(o%2==1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`,y+1<l&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(
xTexelC${y}.xy, xTexelC${y+1}.xy);
`,y+1<l&&(p+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<l&&(p+=`
wTexel = getW(${g}, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<l&&(p+=`
wTexel = getW(${g}, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`}let h="",f="";n&&(r?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:s?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function KZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c,dimRoundingMode:u}=r,l=c;l==null&&(l=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(o,l),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let d=_.computeConv2DInfo(s.shape,a.shape,o,l,i,u,!0),p;Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new $N(d):p=new AN(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[s,a],"float32",h)}var XZ={kernelName:Ga,backendName:"webgl",kernelFunc:KZ},YZ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${s};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},ZZ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
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) / ${r}.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 < ${n}; 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 = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function JZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:u,filterShape:l}=r,d=_.computeConv2DInfo(s.shape,l,o,i,c,u,!0),p=new YZ(d);return n.runWebGLProgram(p,[s,a],"float32")}var QZ={kernelName:Fp,backendName:"webgl",kernelFunc:JZ};function eJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:u,inputShape:l}=r,d=_.computeConv2DInfo(l,a.shape,o,i,c,u,!0),p=new ZZ(d);return n.runWebGLProgram(p,[s,a],"float32")}var tJ={kernelName:Dp,backendName:"webgl",kernelFunc:eJ},nJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function rJ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=w.sizeFromShape(r.shape),o=me({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new nJ(a),c=n.runWebGLProgram(i,[o],o.dtype),u=me({inputs:{x:c},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),u}var sJ={kernelName:Rp,backendName:"webgl",kernelFunc:rJ},aJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:c,dilationWidth:u}=e,{top:l,left:d}=r;this.userCode=`
const ivec2 strides = ivec2(${s}, ${a});
const ivec2 pads = ivec2(${l}, ${d});
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 < ${o}; h++) {
int hIn = hBeg + h * ${c};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; w++) {
int wIn = wBeg + w * ${u};
if (wIn >= 0 && wIn < ${n}) {
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 oJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r,u=_.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",c),l,d=new aJ(u);l=n.runWebGLProgram(d,[s,a],"float32");let p=me({inputs:{x:l},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(l),p}var iJ={kernelName:gl,backendName:"webgl",kernelFunc:oJ};function cJ(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:c}=_.decodeEinsumEquation(s,a.length);_.checkEinsumDimSizes(o.length,c,a);let{path:u,steps:l}=_.getEinsumComputePath(i,c),d=l.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of l[m]){let{permutationIndices:b,expandDims:y}=_.getEinsumPermutation(h,c[g]),v;_.isIdentityPermutation(b)?v=a[g]:(v=Sn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),p===null?p=v:(p=gw({inputs:{a:v,b:p},backend:n}),f.push(p))}m<d-1&&(u[m]>=0&&(p=Qf({inputs:{x:p},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var uJ={kernelName:Mp,backendName:"webgl",kernelFunc:cJ},lJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",dJ=`
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;
`,pJ=Ke({opSnippet:lJ,packedOpSnippet:dJ}),hJ={kernelName:ja,backendName:"webgl",kernelFunc:pJ},fJ="return (b >= 1.0) ? a : a * (b + 1.0);",mJ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,gJ=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=Q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nd(mJ,r.shape,s.shape):new Cu(fJ,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},bJ={kernelName:Lp,backendName:"webgl",kernelFunc:gJ},yJ=`
return vec4(equal(a, b));
`,vJ="return float(a == b);",xJ=an({opSnippet:vJ,packedOpSnippet:yJ,dtype:"bool",cpuKernelImpl:a7}),wJ={kernelName:rc,backendName:"webgl",kernelFunc:xJ},kJ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${_.ERF_P};
float a1 = ${_.ERF_A1};
float a2 = ${_.ERF_A2};
float a3 = ${_.ERF_A3};
float a4 = ${_.ERF_A4};
float a5 = ${_.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,IJ=Ke({opSnippet:kJ}),SJ={kernelName:nc,backendName:"webgl",kernelFunc:IJ},FN="return exp(x);",DN=Ke({opSnippet:FN,packedOpSnippet:FN,cpuKernelImpl:o7}),CJ={kernelName:qa,backendName:"webgl",kernelFunc:DN};function xw(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),c=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),c=o+s+1),i.splice(c,0,1),me({inputs:{x:a},backend:r,attrs:{shape:i}})}var TJ={kernelName:sc,backendName:"webgl",kernelFunc:xw},RN="return exp(x) - 1.0;",NJ=Ke({opSnippet:RN,packedOpSnippet:RN,cpuKernelImpl:i7}),_J={kernelName:ac,backendName:"webgl",kernelFunc:NJ},PN=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="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) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; 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) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function ON(e,t,n){let r=n.texData.get(e.dataId),s=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=me({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),c=i.shape,u=new PN("real",c,t),l=new PN("imag",c,t),d=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:c},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:c}],p=n.runWebGLProgram(u,d,"float32"),h=n.runWebGLProgram(l,d,"float32"),f=va({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=me({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function EJ(e){let{inputs:t,backend:n}=e,{input:r}=t;return ON(r,!1,n)}var AJ={kernelName:Bp,backendName:"webgl",kernelFunc:EJ},$J=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function Ad(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new $J(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var FJ={kernelName:bl,backendName:"webgl",kernelFunc:Ad},DJ=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 - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},RJ={kernelName:oc,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new DJ(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},MN="return floor(x);",PJ=Ke({opSnippet:MN,packedOpSnippet:MN,cpuKernelImpl:c7}),OJ={kernelName:Ka,backendName:"webgl",kernelFunc:PJ},MJ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,LJ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,BJ=an({opSnippet:MJ,packedOpSnippet:LJ,dtype:"int32"}),zJ={kernelName:Xa,backendName:"webgl",kernelFunc:BJ},WJ=class{constructor(e){this.variableNames=["A"];let t=kn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},VJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=kn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.0, ${n}.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);
}
}
${t.output} = result;
}
`}},UJ={kernelName:oh,backendName:"webgl",kernelFunc:GJ},_u;function GJ(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[c,u]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],l=[u,c],d=[u,c,a];(i||o)&&(_u==null&&(_u=document.createElement("canvas").getContext("2d")),_u.canvas.width=c,_u.canvas.height=u,_u.drawImage(s,0,0,c,u),s=_u.canvas);let p=n.makeTensorInfo(l,"int32");n.texData.get(p.dataId).usage=lr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),s);let h=Q().getBool("WEBGL_PACK")?new VJ(d):new WJ(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function HJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:u,dataFormat:l,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=_.convertConv2DDataFormat(l),g=_.computeConv2DInfo(s.shape,a.shape,c,d,u,p,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=CN({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Q().getBool("WEBGL_CONV_IM2COL")&&s.shape[0]===1)b=TN({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let x=o!=null,k=i!=null,C=h==="leakyrelu",N=h?Zf(h,!1):null,F=new SN(g,x,N,k,C),R=[s,a];if(o&&R.push(o),i&&R.push(i),C){let O=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(O),y.push(O)}b=n.runWebGLProgram(F,R,"float32")}let v=me({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jJ={kernelName:$o,backendName:"webgl",kernelFunc:HJ};function qJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:u,dilations:l,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=r,f=[],m=l;m==null&&(m=[1,1]),w.assert(_.eitherStridesOrDilationsAreOne(c,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${c} and dilations '${m}'`);let g=_.computeConv2DInfo(s.shape,a.shape,c,m,u,d,!0),b=Q().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,y=p?Zf(p,b):null,v=[s,a],x=o!=null,k=i!=null,C=p==="leakyrelu";if(x&&v.push(o),k&&v.push(i),C){let O=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(O),f.push(O)}let N;b?N=new $N(g,x,y,k,C):N=new AN(g,x,y,k,C);let F=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(N,v,"float32",F);return f.forEach(O=>n.disposeIntermediateTensorInfo(O)),R}var KJ={kernelName:Fo,backendName:"webgl",kernelFunc:qJ},XJ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=dt(t.length),s=dt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${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 YJ(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[c,u,l,d]=_.prepareAndValidate(r,s),p=me({inputs:{x:s},backend:n,attrs:{shape:[u,o]}}),h=me({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/l,l]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.readSync(s.dataId),y=n.bufferSync(r),v=u7(b,y,r.dtype,u,o,l,d,r.shape,i);return n.makeTensorInfo(c,r.dtype,v.values)}let f=new XJ(o,d,[u,l]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=me({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var ZJ={kernelName:cc,backendName:"webgl",kernelFunc:YJ},JJ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=dt(this.rank),r=QJ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function QJ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[s]}`);return r.join()}function LN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,c=w.parseAxisParam(o,s.shape)[0],u=_.segment_util.collectGatherOpShapeInfo(s,a,c,i),l=w.sizeFromShape(a.shape),d=[],p=me({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=me({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,l/u.batchSize]}});d.push(p),d.push(h);let f=[u.batchSize,u.outerSize,l/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(p),x=l7(v,y,f);return d.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(u.outputShape,x.dtype,x.values)}let m=new JJ(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let b=me({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var eQ={kernelName:ic,backendName:"webgl",kernelFunc:LN},tQ="return float(a > b);",nQ=`
return vec4(greaterThan(a, b));
`,rQ=an({opSnippet:tQ,packedOpSnippet:nQ,cpuKernelImpl:d7,dtype:"bool"}),sQ={kernelName:uc,backendName:"webgl",kernelFunc:rQ},aQ="return float(a >= b);",oQ=`
return vec4(greaterThanEqual(a, b));
`,iQ=an({opSnippet:aQ,packedOpSnippet:oQ,dtype:"bool",cpuKernelImpl:p7}),cQ={kernelName:Za,backendName:"webgl",kernelFunc:iQ};function uQ(e){let{inputs:t,backend:n}=e,{input:r}=t;return ON(r,!0,n)}var lQ={kernelName:zp,backendName:"webgl",kernelFunc:uQ},dQ="return float(!isnan(x) && !isinf(x));",pQ=Ke({opSnippet:dQ,dtype:"bool"}),hQ={kernelName:lc,backendName:"webgl",kernelFunc:pQ},fQ="return float(isinf(x));",mQ=Ke({opSnippet:fQ,dtype:"bool"}),gQ={kernelName:dc,backendName:"webgl",kernelFunc:mQ},bQ="return float(isnan(x));",yQ=Ke({opSnippet:bQ,dtype:"bool"}),vQ={kernelName:pc,backendName:"webgl",kernelFunc:yQ},xQ="return float(a < b);",wQ=`
return vec4(lessThan(a, b));
`,kQ=an({opSnippet:xQ,packedOpSnippet:wQ,cpuKernelImpl:h7,dtype:"bool"}),IQ={kernelName:hc,backendName:"webgl",kernelFunc:kQ},SQ="return float(a <= b);",CQ=`
return vec4(lessThanEqual(a, b));
`,TQ=an({opSnippet:SQ,packedOpSnippet:CQ,cpuKernelImpl:f7,dtype:"bool"}),NQ={kernelName:fc,backendName:"webgl",kernelFunc:TQ};function _Q(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=m7(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var EQ={kernelName:Vp,backendName:"webgl",kernelFunc:_Q},AQ=`if (x < 0.0) return NAN;
return log(x);`,$Q=`
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;
`,FQ=Ke({opSnippet:AQ,packedOpSnippet:$Q,cpuKernelImpl:g7}),DQ={kernelName:eo,backendName:"webgl",kernelFunc:FQ},RQ="return log(1.0 + x);",PQ=Ke({opSnippet:RQ}),OQ={kernelName:mc,backendName:"webgl",kernelFunc:PQ},MQ="return float(a >= 1.0 && b >= 1.0);",LQ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,BQ=an({opSnippet:MQ,packedOpSnippet:LQ,dtype:"bool"}),zQ={kernelName:gc,backendName:"webgl",kernelFunc:BQ},WQ="return float(!(x >= 1.0));",VQ=Ke({opSnippet:WQ}),UQ={kernelName:yl,backendName:"webgl",kernelFunc:VQ},GQ="return float(a >= 1.0 || b >= 1.0);",HQ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,jQ=an({opSnippet:GQ,packedOpSnippet:HQ,dtype:"bool"}),qQ={kernelName:vl,backendName:"webgl",kernelFunc:jQ},KQ=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,c=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${c})`:s===1?i=`1.0/(${c})`:i=`exp(log(${c}) * 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 <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},XQ=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,c=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${c})`:s===1?i=`1.0/(${c})`:i=`exp(log(${c}) * 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 <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
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 * ${i};
setOutput(result);
}
`}},YQ=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:c}=r,u=Q().getBool("WEBGL_PACK_NORMALIZATION")?new XQ(s.shape,a,o,i,c):new KQ(s.shape,a,o,i,c);return n.runWebGLProgram(u,[s],s.dtype)},ZQ={kernelName:xl,backendName:"webgl",kernelFunc:YQ},JQ=class{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* 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);
}
`}},QQ=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:c,alpha:u,beta:l}=r,d=new JQ(s.shape,i,c,u,l);return n.runWebGLProgram(d,[s,a,o],s.dtype)},eee={kernelName:Up,backendName:"webgl",kernelFunc:QQ};function tee(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=me({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),c=ui(i,e.dtype,"max",r),u=me({inputs:{x:c},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(c),u}function BN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=_.getAxesPermutation(u,i),d=l!=null,p=n.shouldExecuteOnCPU([s]),h=s;if(d){if(p){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let N=0;N<x.length;N++)x[N]=s.shape[l[N]];let k=mw(v,s.shape,s.dtype,l,x);h=n.makeTensorInfo(x,s.dtype);let C=n.texData.get(h.dataId);C.values=k}else h=Jf(s,l,n);u=_.getInnerMostAxes(u.length,i)}_.assertAxesAreInnerMostDims("max",u,i);let[f,m]=_.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=_.expandShapeToKeepDim(f,c));let b;if(p){let v=n.texData.get(h.dataId).values,x=b7(v,w.sizeFromShape(m),g,s.dtype);b=n.makeTensorInfo(g,s.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=tee(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),b}var nee={kernelName:to,backendName:"webgl",kernelFunc:BN},ree=rN+`
return max(a, b);
`,see=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Yf+`
return result;
`,aee=an({opSnippet:ree,packedOpSnippet:see,cpuKernelImpl:y7}),oee={kernelName:no,backendName:"webgl",kernelFunc:aee};function iee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;vu(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,u=1;w.assert(_.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let l=_.computePool2DInfo(s.shape,a,o,u,i,c);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return Zn({inputs:{x:s},backend:n});let d=new _d(l,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var cee={kernelName:ro,backendName:"webgl",kernelFunc:iee};function uee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:c,dimRoundingMode:u}=r,l=[1,1,1],d=_.computePool3DInfo(s.shape,a,o,l,i,u,c),p=new bw(d,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var lee={kernelName:wl,backendName:"webgl",kernelFunc:uee},dee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,c=s*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
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 += ${r}) {
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) / ${n}.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 = ${c} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},pee=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,c=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.front,d=c-1-e.padInfo.top,p=u-1-e.padInfo.left,h=i*c*u-1;this.userCode=`
const ivec3 pads = ivec3(${l}, ${d}, ${p});
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 < ${i};
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 < ${c};
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 += ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.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 = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${c} * ${u} +
wR * ${u} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function hee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:c,pad:u,dimRoundingMode:l}=r,d=[1,1,1],p=_.computePool3DInfo(o.shape,i,c,d,u,l),h=new bw(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new pee(p),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var fee={kernelName:Hp,backendName:"webgl",kernelFunc:hee};function mee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;vu([a,o],"maxPoolGrad");let{filterSize:c,strides:u,pad:l,dimRoundingMode:d}=r,p=_.computePool2DInfo(i.shape,c,u,1,l,d),h=!0,f=new _d(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new dee(p),b=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var gee={kernelName:Gp,backendName:"webgl",kernelFunc:mee};function bee(e,t,n,r){let s=new _d(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new _d(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var yee={kernelName:jp,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,c=n;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.assert(_.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let l=_.computePool2DInfo(r.shape,s,a,u,o),[d,p]=bee(r,i,l,c);return[d,p]}};function vee(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=me({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),c=ui(i,"float32","mean",r),u=me({inputs:{x:c},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(c),u}var xee={kernelName:so,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,c=w.parseAxisParam(a,r.shape),u=c,l=_.getAxesPermutation(u,i),d=l!=null,p=o.shouldExecuteOnCPU([r]),h=[],f=r;if(d){if(p){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let F=0;F<k.length;F++)k[F]=r.shape[l[F]];let C=mw(x,r.shape,r.dtype,l,k);f=o.makeTensorInfo(k,r.dtype);let N=o.texData.get(f.dataId);N.values=C}else f=Jf(r,l,o);h.push(f),u=_.getInnerMostAxes(u.length,i)}_.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=_.computeOutAndReduceShapes(f.shape,u),b=m;s&&(b=_.expandShapeToKeepDim(m,c));let y=vee(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function wee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=_.getAxesPermutation(u,i),d=s;l!=null&&(d=Sn({inputs:{x:s},backend:n,attrs:{perm:l}}),u=_.getInnerMostAxes(u.length,s.shape.length)),_.assertAxesAreInnerMostDims("min",u,i);let[p,h]=_.computeOutAndReduceShapes(d.shape,u),f=w.sizeFromShape(h),m=me({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=ui(m,m.dtype,"min",n),b;if(o){let y=_.expandShapeToKeepDim(p,c);b=me({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=me({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(d),b}var kee={kernelName:ao,backendName:"webgl",kernelFunc:wee},Iee=rN+`
return min(a, b);
`,See=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Yf+`
return result;
`,Cee=an({opSnippet:Iee,packedOpSnippet:See,cpuKernelImpl:v7}),Tee={kernelName:oo,backendName:"webgl",kernelFunc:Cee},Nee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let r=e.length,s=dt(r),a=t.map(u=>u[0]).join(","),o=t.map((u,l)=>u[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),c=n==="reflect"?0:1;if(r===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${c};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${c};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${o});
void main() {
${s} outC = getOutputCoords();
for (int i = 0; i < ${r}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${c};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${c};
}
}
${s} coords = outC - start;
setOutput(getX(${i}));
}
`}},_ee=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let r=e.length,s=dt(r),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=In("rc",r),c=In("source",r),u=`${i[r-1]} < ${this.outputShape[r-1]}`,l=r===1?"source":`vec2(${c.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(r===1){let h=`
${s} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;p=`
${s} rc = outputLoc;
${h}
result[0] = getChannel(getX(${c.join()}), ${l});
${i[r-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${c.join()}), ${l});
}
`}else{let h=`
${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 - ${d}) +
gte * ((end - 1) * 2 - source + ${d});
source -= start;
`;p=`
${s} rc = outputLoc;
${h}
result[0] = getChannel(getX(${c.join()}), ${l});
${i[r-1]} += 1;
if(${u}) {
${h}
result[1] = getChannel(getX(${c.join()}), ${l});
}
rc = outputLoc;
${i[r-2]} += 1;
if(${i[r-2]} < ${this.outputShape[r-2]}) {
${h}
result[2] = getChannel(getX(${c.join()}), ${l});
${i[r-1]} += 1;
if(${u}) {
${h}
result[3] = getChannel(getX(${c.join()}), ${l});
}
}
`}this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${o});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},Eee=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _ee(r.shape,s,a):new Nee(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Aee={kernelName:io,backendName:"webgl",kernelFunc:Eee},$ee=`if (b == 0.0) return NAN;
return mod(a, b);`,Fee=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Yf+`
return result;
`,Dee=an({opSnippet:$ee,packedOpSnippet:Fee}),Ree={kernelName:bc,backendName:"webgl",kernelFunc:Dee},Pee=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
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}));
}
`}},Oee=`
if (a == b) {
return 1.0;
};
return a / b;`,Mee=`
// 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;
`,zN=an({opSnippet:Oee,packedOpSnippet:Mee,checkOutOfBounds:!0}),Lee={kernelName:Ha,backendName:"webgl",kernelFunc:zN},WN="return a - b;",VN=an({opSnippet:WN,packedOpSnippet:WN,supportsComplex:!0,cpuKernelImpl:P7}),Bee={kernelName:To,backendName:"webgl",kernelFunc:VN};function UN(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=BN({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),c=_.expandShapeToKeepDim(i.shape,o),u=me({inputs:{x:i},backend:n,attrs:{shape:c}}),l=VN({inputs:{a:s,b:u},backend:n}),d=DN({inputs:{x:l},backend:n}),p=Qf({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=me({inputs:{x:p},backend:n,attrs:{shape:c}}),f=zN({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var zee={kernelName:So,backendName:"webgl",kernelFunc:UN};function Wee(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,c=i?s:UN({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),u=c.shape[0],l=c.shape[1],d=new Pee(u,l,a),p=[[o]],h=n.runWebGLProgram(d,[c],"int32",p);return i||n.disposeIntermediateTensorInfo(c),h}var Vee={kernelName:qp,backendName:"webgl",kernelFunc:Wee},GN="return -x;";function Uee(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=w7(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return Q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Su(r.shape,GN):s=new ya(r.shape,GN),n.runWebGLProgram(s,[r],r.dtype)}var Gee={kernelName:yc,backendName:"webgl",kernelFunc:Uee},Hee=ts.nonMaxSuppressionV3Impl;function jee(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c}=r,u=n.readSync(s.dataId),l=n.readSync(a.dataId),{selectedIndices:d}=Hee(u,l,o,i,c);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var qee={kernelName:xc,backendName:"webgl",kernelFunc:jee},Kee=ts.nonMaxSuppressionV4Impl;function Xee(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,padToMaxOutputSize:u}=r,l=n.readSync(s.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Kee(l,d,o,i,c,u);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Yee={kernelName:wc,backendName:"webgl",kernelFunc:Xee},Zee=ts.nonMaxSuppressionV5Impl;function Jee(e){_.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,softNmsSigma:u}=r,l=n.readSync(s.dataId),d=n.readSync(a.dataId),p=o,h=i,f=c,m=u,{selectedIndices:g,selectedScores:b}=Zee(l,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Qee={kernelName:kc,backendName:"webgl",kernelFunc:Jee},ete=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${r}), float(${n}),
float(index == coords.y)));
}
`}},tte=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{depth:a,onValue:o,offValue:i}=r,c=w.sizeFromShape(s.shape),u=new ete(c,a,o,i),l=me({inputs:{x:s},backend:n,attrs:{shape:[c]}}),d=n.runWebGLProgram(u,[l],s.dtype);n.disposeIntermediateTensorInfo(l);let p=[...s.shape,a],h=me({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},nte={kernelName:uo,backendName:"webgl",kernelFunc:tte};function sm(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=Ed({inputs:{input:r},backend:n}),a=sm({inputs:{x:s},backend:n}),o=rm({inputs:{input:r},backend:n}),i=sm({inputs:{x:o},backend:n}),c=va({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return Ad({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var rte={kernelName:Wc,backendName:"webgl",kernelFunc:sm};function HN(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=Ed({inputs:{input:r},backend:n}),a=HN({inputs:{x:s},backend:n}),o=rm({inputs:{input:r},backend:n}),i=sm({inputs:{x:o},backend:n}),c=va({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return Ad({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var ste={kernelName:Ic,backendName:"webgl",kernelFunc:HN};function ate(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return xw({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(l=>{w.assertShapesMatch(a,l.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],c=t.map(l=>{let d=xw({inputs:{input:l},backend:n,attrs:{dim:s}});return i.push(d),d}),u=IN({inputs:c,backend:n,attrs:{axis:s}});return i.forEach(l=>n.disposeIntermediateTensorInfo(l)),u}var ote={kernelName:Sc,backendName:"webgl",kernelFunc:ate},ite=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,s=dt(r),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${s} start = ${s}(${a});
${s} end = ${s}(${o});
void main() {
${s} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${s} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},cte=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,s=dt(r),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=In("rc",r),c=In("source",r),u=`${i[r-1]} < ${this.outputShape[r-1]}`,l=r===1?"source":`vec2(${c.slice(-2).join()})`,d=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;
if(${u}) {
`,r===1?"":`}
rc = outputLoc;
${i[r-2]} += 1;
if(${i[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${i[r-1]} += 1;
if(${u}) {`],p=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=r===1?2:4;f<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${s} source = rc - start;
result[${f}] = getChannel(getX(${c.join()}), ${l});
}
`;h+=r===1?"} ":"}}",this.userCode=`
const ${s} start = ${s}(${a});
const ${s} end = ${s}(${o});
void main() {
${s} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},jN=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(w.sizeFromShape(s.shape)===0){let u=a.map((l,d)=>l[0]+s.shape[d]+l[1]);return Ad({backend:n,attrs:{shape:u,value:o,dtype:s.dtype}})}let i=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cte(s.shape,a,o):new ite(s.shape,a,o),c=[[o]];return n.runWebGLProgram(i,[s],s.dtype,c)},ute={kernelName:lo,backendName:"webgl",kernelFunc:jN},lte=`
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);
`,dte=`
// 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));
`+Yf+`
return result;
`,pte=an({opSnippet:lte,packedOpSnippet:dte}),hte={kernelName:po,backendName:"webgl",kernelFunc:pte};function fte(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=[],u=w.parseAxisParam(a,s.shape),l=u,d=_.getAxesPermutation(l,i),p=s;d!=null&&(p=Sn({inputs:{x:s},backend:n,attrs:{perm:d}}),l=_.getInnerMostAxes(l.length,i),c.push(p)),_.assertAxesAreInnerMostDims("prod",l,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:b}=I7(p.shape,p.dtype,f,l);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=_.computeOutAndReduceShapes(p.shape,l),g=w.sizeFromShape(m),b=me({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),y=hh(s.dtype),v=ui(b,y,"prod",n);h=me({inputs:{x:v},backend:n,attrs:{shape:f}}),c.push(b),c.push(v)}if(o){c.push(h);let f=_.expandShapeToKeepDim(h.shape,u);h=me({inputs:{x:h},backend:n,attrs:{shape:f}})}return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var mte={kernelName:Cc,backendName:"webgl",kernelFunc:fte},qN=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=S7(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},gte={kernelName:kl,backendName:"webgl",kernelFunc:qN},bte="return 1.0 / x;",yte=Ke({opSnippet:bte}),vte={kernelName:Tc,backendName:"webgl",kernelFunc:yte},xte=Ur+`
return (x < 0.0) ? 0.0 : x;
`,wte=`
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;
`,kte=Ke({opSnippet:xte,packedOpSnippet:wte}),Ite={kernelName:fo,backendName:"webgl",kernelFunc:kte},Ste=Ur+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Cte=`
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;
`,Tte=Ke({opSnippet:Ste,packedOpSnippet:Cte}),Nte={kernelName:go,backendName:"webgl",kernelFunc:Tte},_te=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],d;s?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/l[0]},
${u[1]/l[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// 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);
}
`}},Ete=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],d;s?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/l[0]},
${u[1]/l[1]},
${u[1]/l[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.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 = ${d};
// 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 < ${c-1};
bool hasNextRow = coords.z < ${n-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 Ate(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[c,u]=i,l=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ete(s.shape,c,u,a,o):new _te(s.shape,c,u,a,o);return n.runWebGLProgram(l,[s],"float32")}var $te={kernelName:mo,backendName:"webgl",kernelFunc:Ate},Fte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],c=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/c[0],l=i[1]/c[1],d=1/u,p=1/l,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${l});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// 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 >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${r-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 Dte(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new Fte(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Rte={kernelName:Yp,backendName:"webgl",kernelFunc:Dte},Pte=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0",p;s?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/l[0]},
${u[1]/l[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},Ote=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0",p;s?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/l[0]},
${u[1]/l[1]},
${u[1]/l[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.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 = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${c-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function Mte(e){let{inputs:t,backend:n,attrs:r}=e,{images:s}=t,{alignCorners:a,halfPixelCenters:o,size:i}=r,[c,u]=i,l=Q().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ote(s.shape,c,u,a,o):new Pte(s.shape,c,u,a,o);return n.runWebGLProgram(l,[s],s.dtype)}var Lte={kernelName:Il,backendName:"webgl",kernelFunc:Mte},Bte=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,s]=t,[,a,o]=e,i=[n&&a>1?r-1:r,n&&o>1?s-1:s],c=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/c[0],l=i[1]/c[1],d=1/u,p=1/l,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${l});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// 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 >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${c[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${c[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function zte(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new Bte(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Wte={kernelName:Xp,backendName:"webgl",kernelFunc:zte},Vte=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=dt(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${s}));
}
`}},Ute=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=In("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=dt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(r.slice())};
if(${s}){
result.g = ${c(r.slice())};
}
if(${a}) {
result.b = ${u(r.slice())};
if(${s}) {
result.a = ${l(r.slice())};
}
}
setOutput(result);
}
`;function i(h){return d(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((b,y)=>p(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Gte(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=w.parseAxisParam(a,s.shape);if(o===0)return Zn({inputs:{x:s},backend:n});let c=Q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ute(s.shape,i):new Vte(s.shape,i);return n.runWebGLProgram(c,[s],s.dtype)}var Hte={kernelName:bo,backendName:"webgl",kernelFunc:Gte},jte=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
vec3 fill = vec3(${t.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) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${s}
if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},qte={kernelName:Vc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,c=new jte(r.shape,a),[u,l]=_.getImageCenter(o,r.shape[1],r.shape[2]),d=[[u,l,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(c,[r],r.dtype,d)}},Kte=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Xte=Ke({opSnippet:Kte}),Yte={kernelName:yo,backendName:"webgl",kernelFunc:Xte},Zte="return inversesqrt(x);",Jte=Ke({opSnippet:Zte,cpuKernelImpl:C7}),Qte={kernelName:vo,backendName:"webgl",kernelFunc:Jte},KN=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=dt(s.length),c=dt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let l=`getIndices(${u})`,d="";r===1?d="i":r===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
${i} strides = ${i}(${s});
void main() {
${c} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${l});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function ene(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:c,sliceSize:u,strides:l,outputSize:d}=_.calculateShapes(a,s,o),p=[d/u,u];if(d===0)return n.makeTensorInfo(o,s.dtype);let h=me({inputs:{x:s},backend:n,attrs:{shape:[c,i]}}),f=me({inputs:{x:a},backend:n,attrs:{shape:[c,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new KN(c,i,h.shape.length,f.shape.length,l,p),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=me({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var tne={kernelName:_c,backendName:"webgl",kernelFunc:ene},nne=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],c=[];for(let u=0;u<t.length;u++)c.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);r=i.join(),s=c.join()}let a=dt(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${s}));
} else {
setOutput(getB(${s}));
}
}
`}};function rne(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new nne(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],yr(s.dtype,a.dtype))}var sne={kernelName:Ec,backendName:"webgl",kernelFunc:rne},ane=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${_.SELU_SCALEALPHA};
float scale = ${_.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,one=Ke({opSnippet:ane}),ine={kernelName:Ac,backendName:"webgl",kernelFunc:one},XN="return 1.0 / (1.0 + exp(-1.0 * x));",cne=Ke({opSnippet:XN,packedOpSnippet:XN,cpuKernelImpl:T7}),une={kernelName:wo,backendName:"webgl",kernelFunc:cne},lne=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,dne=Ke({opSnippet:lne}),pne={kernelName:Dc,backendName:"webgl",kernelFunc:dne},hne=cN+`
return sin(x);
`,fne=Ke({opSnippet:hne}),mne={kernelName:xo,backendName:"webgl",kernelFunc:fne},gne=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,bne=Ke({opSnippet:gne}),yne={kernelName:Fc,backendName:"webgl",kernelFunc:bne},vne=`
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;
`,xne=Ke({opSnippet:vne}),wne={kernelName:Rc,backendName:"webgl",kernelFunc:xne},kne=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((b,y)=>b*y),c=[[0,0]];c.push(...o);for(let b=1+a.length;b<s.shape.length;++b)c.push([0,0]);let u=[],l=jN({inputs:{x:s},backend:n,attrs:{paddings:c,constantValue:0}}),d=_.getReshaped(l.shape,a,i,!1),p=_.getPermuted(d.length,a.length,!1),h=_.getReshapedPermuted(l.shape,a,i,!1),f=me({inputs:{x:l},backend:n,attrs:{shape:d}}),m=Sn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=me({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(l),u.push(f),u.push(m),u.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Ine={kernelName:Pc,backendName:"webgl",kernelFunc:kne};function Sne(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(r.dataId),c=n.readSync(s.dataId),u=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[d,p,h,f,m]=_7(i,r.shape,r.dtype,c,s.dtype,u,l);return[n.makeTensorInfo(p,r.dtype,d),n.makeTensorInfo([p[0]],s.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}var Cne={kernelName:Zp,backendName:"webgl",kernelFunc:Sne};function Tne(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),c=Array.from(n.readSync(a.dataId)),[u,l,d]=E7(i,r.shape,r.dtype,o,c);return[n.makeTensorInfo(l,r.dtype,u),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Nne={kernelName:Jp,backendName:"webgl",kernelFunc:Tne};function _ne(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),c=n.readSync(a.dataId),[u,l]=X2(o,r.shape,r.dtype,i,c,!0);return n.makeTensorInfo(l,r.dtype,u)}var Ene={kernelName:Qp,backendName:"webgl",kernelFunc:_ne};function Ane(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),c=n.readSync(a.dataId),[u,l]=X2(o,r.shape,r.dtype,i,c);return n.makeTensorInfo(l,r.dtype,u)}var $ne={kernelName:eh,backendName:"webgl",kernelFunc:Ane};function Fne(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:c,numUpdates:u,strides:l,outputSize:d}=_.calculateShapes(a,s,i),p=!1,h=new KN(u,c,s.shape.length,a.shape.length,l,[d,1],p),f=n.runWebGLProgram(h,[a,s,o],a.dtype),m=me({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Dne={kernelName:th,backendName:"webgl",kernelFunc:Fne};function Rne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],c=_.prepareSplitSize(s,a,i),u=s.shape.length,l=new Array(u).fill(0),d=s.shape.slice();return c.map(p=>{let h=[...d];h[i]=p;let f=Tu({inputs:{x:s},backend:n,attrs:{begin:l,size:h}});return l[i]+=p,f})}var Pne={kernelName:Oc,backendName:"webgl",kernelFunc:Rne},YN="return sqrt(x);",One=Ke({opSnippet:YN,packedOpSnippet:YN,cpuKernelImpl:A7}),Mne={kernelName:ko,backendName:"webgl",kernelFunc:One},Lne="return x * x;",Bne=Ke({opSnippet:Lne}),zne={kernelName:Sl,backendName:"webgl",kernelFunc:Bne},ZN="return (a - b) * (a - b);",Wne=an({opSnippet:ZN,packedOpSnippet:ZN}),Vne={kernelName:Co,backendName:"webgl",kernelFunc:Wne};function Une({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=Ur+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new ya(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var Gne={kernelName:Zs,backendName:"webgl",kernelFunc:Une},Hne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,s=dt(n.length),a=dt(n.length),o="";if(r===1)o="coords * strides + begin";else{let i=0;o=n.map((c,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${s} begin = ${s}(${e});
${s} strides = ${s}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function jne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:c,endMask:u,ellipsisMask:l,newAxisMask:d,shrinkAxisMask:p}=r,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:b,outShape:y}=mn.sliceInfo(s.shape,a,o,i,c,u,l,d,p),v=me({inputs:{x:s},backend:n,attrs:{shape:b}}),x;if(h){let C=Tu({inputs:{x:v},backend:n,attrs:{begin:f,size:g}});x=me({inputs:{x:C},backend:n,attrs:{shape:y}}),n.disposeIntermediateTensorInfo(C)}else if(y.some(C=>C===0))x=n.makeTensorInfo(y,s.dtype,[]);else if(n.shouldExecuteOnCPU([v])){let F=n.texData.get(v.dataId).values,R=Be(v.shape,v.dtype,F),O=$7(y,R,m,f);x=n.makeTensorInfo(y,v.dtype,O.values)}else{let N=new Hne(f,m,y);x=n.runWebGLProgram(N,[v],v.dtype)}let k=me({inputs:{x},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(x),k}var qne={kernelName:Mc,backendName:"webgl",kernelFunc:jne};function Kne(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:c,preserveShortSequences:u}=r,{data:l,dataSplits:d}=t,p=n.readSync(l.dataId),h=n.readSync(d.dataId),[f,m]=F7(p,h,s,a,o,i,c,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Xne={kernelName:nh,backendName:"webgl",kernelFunc:Kne};function Yne(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[u,l,d]=D7(i,c,s),p=l.length;return[n.makeTensorInfo([p,2],"int32",u),n.makeTensorInfo([p],"string",l),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Zne={kernelName:rh,backendName:"webgl",kernelFunc:Yne};function Jne(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=R7(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var Qne={kernelName:sh,backendName:"webgl",kernelFunc:Jne},ere="return tan(x);",tre=Ke({opSnippet:ere}),nre={kernelName:No,backendName:"webgl",kernelFunc:tre},rre=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,sre=Ke({opSnippet:rre}),are={kernelName:_o,backendName:"webgl",kernelFunc:sre},ore=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let r=dt(this.rank),s=ire(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function ire(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function JN(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let c=n.readSync(s.dataId),u=s.dtype==="string"?c.map(p=>w.decodeString(p)):c,l=Be(s.shape,s.dtype,u),d=O7(l,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new ore(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var cre={kernelName:Ys,backendName:"webgl",kernelFunc:JN},ure=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},lre=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function li(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function QN(e){let t=1;for(;t<e;)t*=2;return t}function dre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=Q().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),c=Q().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=s.shape,l=u[u.length-1];if(n.shouldExecuteOnCPU([s])||l<i||a>c){let O=n.readSync(s.dataId),[$,P]=M7(O,u,s.dtype,a,o);return[n.makeTensorInfo($.shape,$.dtype,$.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,s.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(l===1)return[s,Ad({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let d=n.texData.get(s.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(s):s,m=w.sizeFromShape(u)/l,g=me({inputs:{x:h},attrs:{shape:[m,l]},backend:n});p&&li(n,h);let b=QN(a),y=QN(l),v=null,x=()=>v===null?[g,g]:[g,v],k=(O,$,P)=>{let T=x(),L=new ure(P),j=[[l],[v===null?1:0],[Number.NEGATIVE_INFINITY],[O],[$]],q=v;v=n.runWebGLProgram(L,T,"int32",j),li(n,q)};for(let O=1;O<b;O*=2){let $=O*2;for(let P=O;P>=1;P/=2)k($,P,[m,y])}for(let O=y;O>b;O/=2){let $=x(),P=new lre([m,O/2]),L=[[l],[v===null?1:0],[b]],G=v;v=n.runWebGLProgram(P,$,"int32",L),li(n,G);let j=b/2,q=j*2;for(let K=j;K>=1;K/=2)k(q,K,v.shape)}let C=v;v=Tu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),li(n,C);let N=LN({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});li(n,g);let F=u.slice(0,-1);F.push(a),C=v,v=me({inputs:{x:v},attrs:{shape:F},backend:n}),li(n,C);let R=N;return N=me({inputs:{x:N},attrs:{shape:F},backend:n}),li(n,R),[N,v]}var pre={kernelName:Lc,backendName:"webgl",kernelFunc:dre},hre=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${s});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${s});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${o} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function fre(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:c,outputShape:u}=r,[l,d,p,h]=s.shape,[f,m]=u!=null?u:[d,p],g=[l,f,m,h],b=new hre(d,p,o,i,c,g);return n.runWebGLProgram(b,[s,a],"float32")}var mre={kernelName:Bc,backendName:"webgl",kernelFunc:fre};function gre(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;vu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=r.readSync(a.dataId),{outputValues:i,outputShape:c,indices:u}=L7(o,s,a.shape,a.dtype);return[r.makeTensorInfo(c,a.dtype,i),r.makeTensorInfo([u.length],"int32",u)]}var bre={kernelName:ah,backendName:"webgl",kernelFunc:gre};function yre(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,c=s.shape[a],u=new Array(i-1),l=0;for(let m=0;m<i;m++)m!==a&&(u[l++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(c);for(let m=0;m<f.length;m++){p[a]=m;let g=Tu({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),b=me({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=b,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var vre={kernelName:zc,backendName:"webgl",kernelFunc:yre},xre=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,s=e.inSize,a=e.numSegments,o=a*Math.ceil(s/n);this.outputShape=[r,o];let i="0.0",c="sumValue",u=Math.floor(n/4)*4,l=n%4,d=`
sumValue += dot(values, segFilter);
`,p="";s%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${s}) {
return initializationValue;
}
`);let h="";s%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${s}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${i};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${u}; 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
);
${d}
}
int inIdx = inOffset + ${u};
if (${l===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
);
${d}
} else if (${l===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
);
${d}
} else if (${l===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
);
${d}
}
setOutput(${c});
}
`}};function wre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,c=[],u=0,l=_.getAxesPermutation([u],i),d=s;l!=null&&(d=Sn({inputs:{x:s},backend:n,attrs:{perm:l}}),c.push(d),u=_.getInnerMostAxes(1,i)[0]);let p=_.segment_util.computeOutShape(d.shape,u,o),h=w.sizeFromShape([d.shape[u]]),f=me({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});c.push(f);let m=hh(s.dtype),g=(x,k,C,N,F)=>{let R=x.shape[0],O=x.shape[1],$=_.segment_util.segOpComputeOptimalWindowSize(O,F),P={windowSize:$,inSize:O,batchSize:R,numSegments:F},T=new xre(P,k),L=n.compileAndRun(T,[x,C],N);if(c.push(L),L.shape[1]===F)return L;let G=qN({backend:n,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),j=JN({inputs:{x:G},backend:n,attrs:{reps:[O/$]}});return c.push(G),c.push(j),g(L,k,j,N,F)},b=g(f,"unsortedSegmentSum",a,m,o),y=me({inputs:{x:b},backend:n,attrs:{shape:p}}),v=y;if(l!=null){c.push(y);let x=_.getUndoAxesPermutation(l);v=Sn({inputs:{x:v},backend:n,attrs:{perm:x}})}return c.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var kre={kernelName:Cl,backendName:"webgl",kernelFunc:wre},Ire=[ZQ,eee,M9,B9,V9,H9,q9,Y9,J9,eY,sY,oY,uY,pY,vY,mY,kY,TY,SY,AY,FY,RY,LY,HY,qY,QY,tZ,aZ,cZ,y9,hZ,IZ,CZ,bZ,EZ,$Z,NZ,RZ,MZ,zZ,VZ,GZ,qZ,QZ,tJ,XZ,sJ,iJ,uJ,hJ,bJ,wJ,SJ,CJ,TJ,_J,AJ,FJ,RJ,OJ,zJ,UJ,jJ,KJ,ZJ,eQ,sQ,cQ,b9,lQ,dZ,hQ,gQ,vQ,x9,IQ,NQ,EQ,OQ,DQ,zQ,UQ,qQ,nee,lee,cee,fee,gee,yee,oee,xee,kee,Tee,Aee,Ree,Vee,C9,Gee,qee,Yee,Qee,XY,nte,ste,ote,ute,hte,k9,mte,gte,YY,Lee,vte,Nte,Ite,N9,$te,Rte,Lte,Wte,Hte,qte,Yte,Qte,tne,sne,ine,une,pne,mne,yne,UY,zee,wne,Ine,Cne,Nne,Ene,$ne,Dne,Pne,Mne,zne,Vne,Gne,qne,Xne,Zne,Qne,Bee,R9,nre,are,cre,pre,mre,P9,bre,vre,kre,rte];for(let e of Ire)Nl(e);var Pn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Pn||(Pn={}));var $d;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})($d||($d={}));var e_;function Sre(e){e_=e.wasm.cwrap(Ao,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Cre(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:c,transposeB:u,activation:l,leakyreluAlpha:d}=r,p=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let F=n.dataIdMap.get(o.dataId);if(F.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${F.shape.length}.`);f=F.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=$d[l];if(g==null)throw new Error(`${l} activation not yet supported for FusedConv2D in the 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Fd(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:s}=e,[a,o]=mn.parseSliceParams(t,n,r),i=mn.isSliceContinous(t.shape,a,o),c=s.readSync(t.dataId),u=s.makeOutput(o,t.dtype),l=w.computeStrides(t.shape),d=s.dataIdMap.get(u.dataId);if(i){let f=mn.computeFlatOffset(a,l);return t.dtype==="string"?d.stringBytes=c.slice(f,f+w.sizeFromShape(o)):s.typedArrayFromHeap(u).set(c.subarray(f,f+w.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Of(c,a,o,t.shape,t.dtype);return d.stringBytes=f,u}let p=s.typedArrayFromHeap(u),h=t.shape.length;if(h===2)ese(c,l[0],p,a,o);else if(h===3)tse(c,l[0],l[1],p,a,o);else if(h===4)nse(c,l[0],l[1],l[2],p,a,o);else{let f=Of(c,a,o,t.shape,t.dtype);p.set(f)}return u}function ese(e,t,n,r,s){let a=0,o=r[0],i=r[1],c=o+s[0];for(let u=o;u<c;u++){let l=u*t+i;n.set(e.subarray(l,l+s[1]),a),a+=s[1]}}function tse(e,t,n,r,s,a){let o=0,i=s[0],c=s[1],u=s[2],l=i+a[0],d=c+a[1];for(let p=i;p<l;p++)for(let h=c;h<d;h++){let f=p*t+h*n+u;r.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function 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On({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));s=_.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=Ux(f,s,t[0].dtype,m),b=_.computeOutShape(a.map(v=>v.shape),r);o.shape=b;let y=n.dataIdMap.get(o.dataId);return y.stringBytes=_.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),o}let c=w.sizeFromShape(a[0].shape.slice(0,r)),u=0,l=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(r));return u+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<c;h++){let f=h*u;for(let m=0;m<d.length;m++){let g=l[m],b=h*g,y=d[m].subarray(b,b+g);p.set(y,f),f+=g}}return o}var dse={kernelName:Qi,backendName:"wasm",kernelFunc:u_},l_;function pse(e){l_=e.wasm.cwrap(Ba,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hse(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:c,dilations:u,pad:l,dimRoundingMode:d,dataFormat:p}=n,h=_.convertConv2DDataFormat(p),f=_.computeConv2DInfo(s.shape,a.shape,c,u,l,d,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,C=f.dilationWidth,N=f.strideHeight,F=f.strideWidth,R=f.inChannels,O=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Tn(s,a),this._shift=r,this._positions=t.map(o=>o.mul(new Re(s,a)).add(r))}get shift(){return new Re(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Re(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Re(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let a=t instanceof yt?t.box.floor():new it(t);return this.shiftBy(a.x,a.y).align(null,n)}let{useDlibAlignment:r,minBoxPadding:s}={useDlibAlignment:!1,minBoxPadding:.2,...n};return r?this.alignDlib():this.alignMinBbox(s)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,r,s]=t,a=d=>s.sub(d).magnitude(),o=(a(n)+a(r))/2,i=Math.floor(o/Vie),c=hi(t),u=Math.floor(Math.max(0,c.x-zie*i)),l=Math.floor(Math.max(0,c.y-Wie*i));return new Fu(u,l,Math.min(i,this.imageWidth+u),Math.min(i,this.imageHeight+l))}alignMinBbox(t){let n=Dw(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var Q_=class extends fr{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],hi([t[3],t[4]])]}};var Du=class extends fr{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return 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Ps=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((r,s)=>{if(Ds(r)){this._imageTensors[s]=r,this._inputDimensions[s]=r.shape;return}if(hr(r)){let o=r.shape[0];if(o!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof Ye.getEnv().Canvas?r:Wd(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return this._inputDimensions}get inputSize(){return this._inputSize}get reshapedInputDimensions(){return us(this.batchSize,0,1).map((t,n)=>this.getReshapedInputDimensions(n))}getInput(t){return this.canvases[t]||this.imageTensors[t]}getInputDimensions(t){return this._inputDimensions[t]}getInputHeight(t){return this._inputDimensions[t][0]}getInputWidth(t){return this._inputDimensions[t][1]}getReshapedInputDimensions(t){if(typeof this.inputSize!="number")throw new Error("getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet");let n=this.getInputWidth(t),r=this.getInputHeight(t);return Aw({width:n,height:r},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,M(()=>{let r=us(this.batchSize,0,1).map(a=>{let o=this.getInput(a);if(o instanceof Ee){let i=hr(o)?o:yn(o);return i=Pw(i,n),(i.shape[1]!==t||i.shape[2]!==t)&&(i=qn.resizeBilinear(i,[t,t],!1,!1)),i.as3D(t,t,3)}if(o instanceof Ye.getEnv().Canvas)return Lo.fromPixels(jw(o,t,n));throw new 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n=`${t}-weights_manifest.json`;if(!e)return{modelBaseUri:"",manifestUri:n};if(e==="/")return{modelBaseUri:"/",manifestUri:`/${n}`};let r=e.startsWith("http://")?"http://":e.startsWith("https://")?"https://":"";e=e.replace(r,"");let s=e.split("/").filter(i=>i),a=e.endsWith(".json")?s[s.length-1]:n,o=r+(e.endsWith(".json")?s.slice(0,s.length-1):s).join("/");return o=e.startsWith("/")?`/${o}`:o,{modelBaseUri:o,manifestUri:o==="/"?`/${a}`:`${o}/${a}`}}async function Kw(e,t){let{manifestUri:n,modelBaseUri:r}=hm(e,t),s=await qw(n);return Kt.loadWeights(s,r)}function Xie(e,t,n=!1){let{width:r,height:s}=n?gi(t):t;return e.width=r,e.height=s,{width:r,height:s}}var cn=class{constructor(t){this._params=void 0;this._paramMappings=[];this._name=t}get params(){return this._params}get paramMappings(){return this._paramMappings}get isLoaded(){return!!this.params}getParamFromPath(t){let{obj:n,objProp:r}=this.traversePropertyPath(t);return 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p=a("exit_flow/reduction_block"),h=s("exit_flow/separable_conv"),f={reduction_block:p,separable_conv:h};return Nn(e,n),{params:{entry_flow:l,middle_flow:d,exit_flow:f},paramMappings:n}}function uE(e,t,n){return Y(Dt(e,t.filters,n,"same"),t.bias)}function e0(e,t,n=!0){let r=n?qe(e):e;return r=Ln(r,t.separable_conv0,[1,1]),r=Ln(qe(r),t.separable_conv1,[1,1]),r=Rt(r,[3,3],[2,2],"same"),r=Y(r,uE(e,t.expansion_conv,[2,2])),r}function nce(e,t){let n=Ln(qe(e),t.separable_conv0,[1,1]);return n=Ln(qe(n),t.separable_conv1,[1,1]),n=Ln(qe(n),t.separable_conv2,[1,1]),n=Y(n,e),n}var t0=class extends cn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return M(()=>{let r=ce(t.toBatchTensor(112,!0),"float32"),a=Hr(r,[122.782,117.001,104.298]).div(255),o=qe(uE(a,n.entry_flow.conv_in,[2,2]));return o=e0(o,n.entry_flow.reduction_block_0,!1),o=e0(o,n.entry_flow.reduction_block_1),us(this._numMainBlocks,0,1).forEach(i=>{o=nce(o,n.middle_flow[`main_block_${i}`])}),o=e0(o,n.exit_flow.reduction_block),o=qe(Ln(o,n.exit_flow.separable_conv,[1,1])),o})}async forward(t){return this.forwardInput(await ft(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeightMap(t){return cE(t,this._numMainBlocks)}extractParams(t){return iE(t,this._numMainBlocks)}};function lE(e){let t=[],{extractWeights:n,getRemainingWeights:r}=_n(e),s=mm(n,t),a=s(512,1,"fc/age"),o=s(512,2,"fc/gender");if(r().length!==0)throw new Error(`weights remaing after extract: ${r().length}`);return{paramMappings:t,params:{fc:{age:a,gender:o}}}}function dE(e){let t=[],n=Jn(e,t);function r(a){let o=n(`${a}/weights`,2),i=n(`${a}/bias`,1);return{weights:o,bias:i}}let s={fc:{age:r("fc/age"),gender:r("fc/gender")}};return Nn(e,t),{params:s,paramMappings:t}}var Ms;(function(n){n.FEMALE="female",n.MALE="male"})(Ms||(Ms={}));var Im=class extends cn{constructor(t=new t0(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return M(()=>{let r=t instanceof Ps?this.faceFeatureExtractor.forwardInput(t):t,s=vr(r,[7,7],[2,2],"valid").as2D(r.shape[0],-1),a=Gd(s,n.fc.age).as1D(),o=Gd(s,n.fc.gender);return{age:a,gender:o}})}forwardInput(t){return M(()=>{let{age:n,gender:r}=this.runNet(t);return{age:n,gender:es(r)}})}async forward(t){return this.forwardInput(await ft(t))}async predictAgeAndGender(t){let n=await ft(t),r=await this.forwardInput(n),s=ht(r.age),a=ht(r.gender),o=s.map((c,u)=>({ageTensor:c,genderTensor:a[u]})),i=await Promise.all(o.map(async({ageTensor:c,genderTensor:u})=>{let l=c.dataSync()[0],d=u.dataSync()[0],p=d>.5,h=p?Ms.MALE:Ms.FEMALE,f=p?d:1-d;return 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Em=e=>typeof e=="number";function a0(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!Em(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>Em(t.x)&&Em(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(Em)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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DE(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=_n(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:u}=bce(s,o),l;if(t.withSeparableConvs){let[d,p,h,f,m,g,b,y,v]=r,x=t.isFirstLayerConv2d?i(d,p,3,"conv0"):u(d,p,"conv0"),k=u(p,h,"conv1"),C=u(h,f,"conv2"),N=u(f,m,"conv3"),F=u(m,g,"conv4"),R=u(g,b,"conv5"),O=y?u(b,y,"conv6"):void 0,$=v?u(y,v,"conv7"):void 0,P=i(v||y||b,5*n,1,"conv8");l={conv0:x,conv1:k,conv2:C,conv3:N,conv4:F,conv5:R,conv6:O,conv7:$,conv8:P}}else{let[d,p,h,f,m,g,b,y,v]=r,x=c(d,p,"conv0"),k=c(p,h,"conv1"),C=c(h,f,"conv2"),N=c(f,m,"conv3"),F=c(m,g,"conv4"),R=c(g,b,"conv5"),O=c(b,y,"conv6"),$=c(y,v,"conv7"),P=i(v,5*n,1,"conv8");l={conv0:x,conv1:k,conv2:C,conv3:N,conv4:F,conv5:R,conv6:O,conv7:$,conv8:P}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:l,paramMappings:o}}function yce(e,t){let n=Jn(e,t);function r(i){let c=n(`${i}/sub`,1),u=n(`${i}/truediv`,1);return{sub:c,truediv:u}}function s(i){let c=n(`${i}/filters`,4),u=n(`${i}/bias`,1);return{filters:c,bias:u}}function a(i){let c=s(`${i}/conv`),u=r(`${i}/bn`);return{conv:c,bn:u}}let o=Lu(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function RE(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=yce(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return Nn(e,n),{params:o,paramMappings:n}}var ps=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var o0=class extends cn{constructor(t){super("TinyYolov2");a0(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let r=Ls(t,n.conv0);return r=Rt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv1),r=Rt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv2),r=Rt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv3),r=Rt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv4),r=Rt(r,[2,2],[2,2],"same"),r=Ls(r,n.conv5),r=Rt(r,[2,2],[1,1],"same"),r=Ls(r,n.conv6),r=Ls(r,n.conv7),yi(r,n.conv8,"valid",!1)}runMobilenet(t,n){let r=this.config.isFirstLayerConv2d?Vu(yi(t,n.conv0,"valid",!1)):Bs(t,n.conv0);return r=Rt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv1),r=Rt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv2),r=Rt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv3),r=Rt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv4),r=Rt(r,[2,2],[2,2],"same"),r=Bs(r,n.conv5),r=Rt(r,[2,2],[1,1],"same"),r=n.conv6?Bs(r,n.conv6):r,r=n.conv7?Bs(r,n.conv7):r,yi(r,n.conv8,"valid",!1)}forwardInput(t,n){let{params:r}=this;if(!r)throw new Error("TinyYolov2 - load model before inference");return M(()=>{let s=ce(t.toBatchTensor(n,!1),"float32");return s=this.config.meanRgb?Hr(s,this.config.meanRgb):s,s=s.div(255),this.config.withSeparableConvs?this.runMobilenet(s,r):this.runTinyYolov2(s,r)})}async forward(t,n){return this.forwardInput(await ft(t),n)}async detect(t,n={}){let{inputSize:r,scoreThreshold:s}=new ps(n),a=await ft(t),o=await this.forwardInput(a,r),i=M(()=>ht(o)[0].expandDims()),c={width:a.getInputWidth(0),height:a.getInputHeight(0)},u=await this.extractBoxes(i,a.getReshapedInputDimensions(0),s);o.dispose(),i.dispose();let l=u.map(g=>g.box),d=u.map(g=>g.score),p=u.map(g=>g.classScore),h=u.map(g=>this.config.classes[g.label]);return Rw(l.map(g=>g.rescale(r)),d,this.config.iouThreshold,!0).map(g=>new wa(d[g],p[g],h[g],l[g],c))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return RE(t,this.config)}extractParams(t){let n=this.config.filterSizes||o0.DEFAULT_FILTER_SIZES,r=n?n.length:void 0;if(r!==7&&r!==8&&r!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${r} filterSizes in config`);return DE(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,r){let{width:s,height:a}=n,o=Math.max(s,a),i=o/s,c=o/a,u=t.shape[1],l=this.config.anchors.length,[d,p,h]=M(()=>{let b=t.reshape([u,u,l,this.boxEncodingSize]),y=b.slice([0,0,0,0],[u,u,l,4]),v=b.slice([0,0,0,4],[u,u,l,1]),x=this.withClassScores?es(b.slice([0,0,0,5],[u,u,l,this.config.classes.length]),3):ke(0);return[y,v,x]}),f=[],m=await p.array(),g=await d.array();for(let b=0;b<u;b++)for(let y=0;y<u;y++)for(let v=0;v<l;v++){let x=Od(m[b][y][v][0]);if(!r||x>r){let k=(y+Od(g[b][y][v][0]))/u*i,C=(b+Od(g[b][y][v][1]))/u*c,N=Math.exp(g[b][y][v][2])*this.config.anchors[v].x/u*i,F=Math.exp(g[b][y][v][3])*this.config.anchors[v].y/u*c,R=k-N/2,O=C-F/2,$={row:b,col:y,anchor:v},{classScore:P,label:T}=this.withClassScores?await this.extractPredictedClass(h,$):{classScore:1,label:0};f.push({box:new $u(R,O,R+N,O+F),score:x,classScore:x*P,label:T,...$})}}return d.dispose(),p.dispose(),h.dispose(),f}async extractPredictedClass(t,n){let{row:r,col:s,anchor:a}=n,o=await t.array();return Array(this.config.classes.length).fill(0).map((i,c)=>o[r][s][a][c]).map((i,c)=>({classScore:i,label:c})).reduce((i,c)=>i.classScore>c.classScore?i:c)}},Uu=o0;Uu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Gu=class extends Uu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:NE,classes:["face"],...t?{anchors:EE,meanRgb:AE}:{anchors:_E,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new yt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?FE:$E}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function vce(e,t=!0){let n=new Gu(t);return n.extractWeights(e),n}var Am=class extends ps{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var _r=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function ki(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(c=>vi(c)?s(c):c.detection),o=r||(t instanceof Ee?await Pu(t,a):await Ru(t,a)),i=await n(o);return o.forEach(c=>c instanceof Ee&&c.dispose()),i}async function Hu(e,t,n,r,s){return ki([e],t,async a=>n(a[0]),r,s)}var PE=.4,OE=[new Re(1.603231,2.094468),new Re(6.041143,7.080126),new Re(2.882459,3.518061),new Re(4.266906,5.178857),new Re(9.041765,10.66308)],ME=[117.001,114.697,97.404];var ju=class extends Uu{constructor(){let t={withSeparableConvs:!0,iouThreshold:PE,classes:["face"],anchors:OE,meanRgb:ME,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new yt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var Ze={ssdMobilenetv1:new wi,tinyFaceDetector:new ju,tinyYolov2:new Gu,faceLandmark68Net:new zu,faceLandmark68TinyNet:new Sm,faceRecognitionNet:new Wu,faceExpressionNet:new wm,ageGenderNet:new Im},LE=(e,t)=>Ze.ssdMobilenetv1.locateFaces(e,t),xce=(e,t)=>Ze.tinyFaceDetector.locateFaces(e,t),wce=(e,t)=>Ze.tinyYolov2.locateFaces(e,t),BE=e=>Ze.faceLandmark68Net.detectLandmarks(e),kce=e=>Ze.faceLandmark68TinyNet.detectLandmarks(e),Ice=e=>Ze.faceRecognitionNet.computeFaceDescriptor(e),Sce=e=>Ze.faceExpressionNet.predictExpressions(e),Cce=e=>Ze.ageGenderNet.predictAgeAndGender(e),zE=e=>Ze.ssdMobilenetv1.load(e),Tce=e=>Ze.tinyFaceDetector.load(e),Nce=e=>Ze.tinyYolov2.load(e),_ce=e=>Ze.faceLandmark68Net.load(e),Ece=e=>Ze.faceLandmark68TinyNet.load(e),Ace=e=>Ze.faceRecognitionNet.load(e),$ce=e=>Ze.faceExpressionNet.load(e),Fce=e=>Ze.ageGenderNet.load(e),Dce=zE,Rce=LE,Pce=BE;var i0=class extends _r{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},qu=class extends i0{async run(){let t=await this.parentTask,n=await ki(t,this.input,async r=>Promise.all(r.map(s=>Ze.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>km(r,n[s]))}withAgeAndGender(){return new Xu(this,this.input)}},Ku=class extends i0{async run(){let t=await this.parentTask;if(!t)return;let n=await Hu(t,this.input,r=>Ze.faceExpressionNet.predictExpressions(r),this.extractedFaces);return km(t,n)}withAgeAndGender(){return new Yu(this,this.input)}},Ii=class extends qu{withAgeAndGender(){return new Ci(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}},Si=class extends Ku{withAgeAndGender(){return new Ti(this,this.input)}withFaceDescriptor(){return new Ca(this,this.input)}};var c0=class extends _r{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.extractedFaces=r}},Xu=class extends c0{async run(){let t=await this.parentTask,n=await ki(t,this.input,async r=>Promise.all(r.map(s=>Ze.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return Nm(_m(r,o,i),a)})}withFaceExpressions(){return new qu(this,this.input)}},Yu=class extends c0{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await Hu(t,this.input,a=>Ze.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return Nm(_m(t,r,s),n)}withFaceExpressions(){return new Ku(this,this.input)}},Ci=class extends Xu{withFaceExpressions(){return new Ii(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}},Ti=class extends Yu{withFaceExpressions(){return new Si(this,this.input)}withFaceDescriptor(){return new Ca(this,this.input)}};var $m=class extends _r{constructor(t,n){super();this.parentTask=t;this.input=n}},Sa=class extends $m{async run(){let t=await this.parentTask;return(await ki(t,this.input,r=>Promise.all(r.map(s=>Ze.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>Tm(t[s],r))}withFaceExpressions(){return new Ii(this,this.input)}withAgeAndGender(){return new Ci(this,this.input)}},Ca=class extends $m{async run(){let t=await this.parentTask;if(!t)return;let n=await Hu(t,this.input,r=>Ze.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return Tm(t,n)}withFaceExpressions(){return new Si(this,this.input)}withAgeAndGender(){return new Ti(this,this.input)}};var Fm=class extends _r{constructor(t,n,r){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=r}get landmarkNet(){return this.useTinyLandmarkNet?Ze.faceLandmark68TinyNet:Ze.faceLandmark68Net}},Dm=class extends Fm{async run(){let t=await this.parentTask,n=t.map(a=>a.detection),r=this.input instanceof Ee?await Pu(this.input,n):await Ru(this.input,n),s=await Promise.all(r.map(a=>this.landmarkNet.detectLandmarks(a)));return r.forEach(a=>a instanceof Ee&&a.dispose()),t.map((a,o)=>Bu(a,s[o]))}withFaceExpressions(){return new Ii(this,this.input)}withAgeAndGender(){return new Ci(this,this.input)}withFaceDescriptors(){return new Sa(this,this.input)}},Rm=class extends Fm{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Ee?await Pu(this.input,[n]):await Ru(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Ee&&a.dispose()),Bu(t,s)}withFaceExpressions(){return new Si(this,this.input)}withAgeAndGender(){return new Ti(this,this.input)}withFaceDescriptor(){return new Ca(this,this.input)}};var Pm=class extends _r{constructor(t,n=new Nr){super();this.input=t;this.options=n}},Kd=class extends Pm{async run(){let{input:t,options:n}=this,r;if(n instanceof Am)r=Ze.tinyFaceDetector.locateFaces(t,n);else if(n instanceof Nr)r=Ze.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof ps)r=Ze.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>fi({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new Dm(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new qu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Xu(this.runAndExtendWithFaceDetections(),this.input)}},Om=class extends Pm{async run(){let t=await new Kd(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?fi({},n):void 0)})}withFaceLandmarks(t=!1){return new Rm(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Ku(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Yu(this.runAndExtendWithFaceDetection(),this.input)}};function Oce(e,t=new Nr){return new Om(e,t)}function Mm(e,t=new Nr){return new Kd(e,t)}async function WE(e,t){return Mm(e,new Nr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Mce(e,t={}){return Mm(e,new ps(t)).withFaceLandmarks().withFaceDescriptors()}var Lce=WE;function u0(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a**2,0))}var Lm=class{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof Rs)return o;if(o instanceof Float32Array)return new Rs(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new Rs(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>u0(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new Md(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distance<r.distance?n:r)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Md("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>Rs.fromJSON(r));return new Lm(n,t.distanceThreshold)}};function Bce(e){let t=new ju;return t.extractWeights(e),t}function VE(e,t){let{width:n,height:r}=new Tn(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>VE(s,{width:n,height:r}));if(vi(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return Bu(fi(e,s),a)}return ls(e)?fi(e,e.detection.forSize(n,r)):e instanceof fr||e instanceof yt?e.forSize(n,r):e}var zce=typeof process!="undefined",UE=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",Wce={faceapi:oE,node:zce,browser:UE};UE&&(Fa.set("CHECK_COMPUTATION_FOR_ERRORS",!1),Fa.set("WEBGL_CPU_FORWARD",!0),Fa.set("WEBGL_PACK_DEPTHWISECONV",!1),Fa.set("WEBGL_USE_SHAPES_UNIFORMS",!0));return Vce;})();
/**
* @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
* 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
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 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 See the LICENSE file. */
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