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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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a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete 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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?o=d():(f=this.profiler.profileKernel(a,t,()=>d()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),o=f.outputs)}),c&&this.addTapeNode(a,t,o,n,l,r),this.state.profiling&&this.state.activeProfile.kernels.push({name:a,bytesAdded:this.state.numBytes-u,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-p,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(g=>t[g]!=null?t[g].shape:null),outputShapes:o.map(g=>g.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(m)?o:o[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=cy(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=r.map(l=>t[l]);let 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n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let a=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(a=e.size*r0(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:a,refCount:0}),this.state.numBytes+=a}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof Xr||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):(t.backend.decComplexRef(e.dataId),this.state.tensorInfo.get(e.dataId).refCount--)}disposeVariables(){for(let e in this.state.registeredVariables){let 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qO=R({softmaxCrossEntropy_:jO}),KO={fft:ep,ifft:eu,rfft:tp,irfft:Kh},XO={hammingWindow:jP,hannWindow:ok,frame:lk,stft:YP},Qa={flipLeftRight:eO,resizeNearestNeighbor:mk,resizeBilinear:hk,rotateWithOffset:nO,cropAndResize:ZP,nonMaxSuppression:rO,nonMaxSuppressionAsync:dO,nonMaxSuppressionWithScore:mO,nonMaxSuppressionWithScoreAsync:gO,nonMaxSuppressionPadded:bO,nonMaxSuppressionPaddedAsync:vO},gk={bandPart:NO,gramSchmidt:SO,qr:_O},YO={absoluteDifference:FO,computeWeightedLoss:Ir,cosineDistance:DO,hingeLoss:RO,huberLoss:OO,logLoss:zO,meanSquaredError:WO,sigmoidCrossEntropy:GO,softmaxCrossEntropy:qO},Nr=class extends t1{minimize(e,t=!1,n){let{value:a,grads:r}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(s)}else this.applyGradients(r);return Fe(r),t?a:(a.dispose(),null)}get iterations(){return 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Nr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;D(()=>{let l=Z(L(i,this.rho),L(ct(s),1-this.rho)),c=L(we(an(Z(o,this.epsilon)),an(Z(i,this.epsilon))),s),u=Z(L(o,this.rho),L(ct(c),1-this.rho));i.assign(l),o.assign(u);let 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Nr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>In(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;D(()=>{let i=Z(s,ct(r));s.assign(i);let o=Z(L(we(r,an(Z(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Fe(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};am.className="Adagrad";es(am);var rm=class extends Nr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],D(()=>{this.accBeta1=he(t).variable(),this.accBeta2=he(n).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);D(()=>{let n=ge(1,this.accBeta1),a=ge(1,this.accBeta2);t.forEach((r,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sm.className="Adamax";es(sm);var ap=class extends Nr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=P.registeredVariables[t];D(()=>{let s=Z(L(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Kt(he(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;D(()=>{let l=Z(L(i,this.decay),L(ct(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=Z(L(c,this.decay),L(s,1-this.decay)),p=we(L(s,this.learningRate),an(ge(l,Z(ct(u),this.epsilon)))),d=Z(L(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=ge(a,d);a.assign(h)}else{let c=Z(L(i,this.decay),L(ct(s),1-this.decay)),u=Z(L(o,this.momentum),we(L(s,this.learningRate),an(Z(c,this.epsilon))));i.assign(c),o.assign(u);let p=ge(a,u);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Fe(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Fe(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Fe(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};om.className="RMSProp";es(om);var Oi=class{static sgd(e){return new ap(e)}static momentum(e,t,n=!1){return new im(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new om(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new rm(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new nm(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new sm(e,t,n,a,r)}static 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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}}},DW=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DW++,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=Sr(n)+"_"+vm(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 r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let 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e){let{sorted:i,recipientMap:o}=vI(s,t);for(let l of i)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in o)a[l]==null&&(a[l]=new Set),o[l].forEach(c=>a[l].add(c))}}return{sorted:n,recipientCounts:d4(a)}}function d4(e){let t={};for(let n in e)t[n]=e[n].size;return t}function vI(e,t){let n=new Set,a=[],r={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),a.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let c of o.inputs)r[c.name]==null&&(r[c.name]=new Set),r[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:a,recipientMap:r}}function p4(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let a=0;a<e.sourceLayer.inboundNodes.length;++a)for(let r of e.sourceLayer.inboundNodes[a].outputTensors)if(r.id===e.id){n=a;break}t=e.sourceLayer.getOutputAt(n)}return t}var sr=class 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);os(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let y of this.inputs){let b=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex;tr(v===0,"input layer has >1 nodes"),tr(x===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let b=this.inputLayers[y];if(!(b instanceof su))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${b.getClassName()}.`);this.inputNames.push(b.name),this.feedInputShapes.push(b.batchInputShape),this.feedInputNames.push(b.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,b,v,x,N,T)=>{(x==null||N==null||T==null)&&(x=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let C=x.inboundNodes[N];if(v.indexOf(C)!==-1)throw new $a(`The tensor ${y.name} at layer "${x.name}" is part of a cycle.`);if(b.indexOf(C)!==-1)return;this.containerNodes.add(sr.nodeKey(x,N)),x.id in s||(s[x.id]=Object.keys(s).length),v.indexOf(C)===-1&&v.push(C);let $=C.inboundLayers.length;for(let F=0;F<$;F++){let O=C.inputTensors[F],W=C.inboundLayers[F],V=C.nodeIndices[F],H=C.tensorIndices[F];o(O,b,v,W,V,H)}for(b.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);i.push(C)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let b=t[y.id],v=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,v),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let x=0;x<y.inboundLayers.length;x++){let N=y.inboundLayers[x],T=y.nodeIndices[x],C=N.inboundNodes[T],$=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(b+1,$),n[C.id]=C}}let p={};for(let y in t){let b=t[y];b in p||(p[b]=[]),p[b].push(n[y])}let d={};for(let y in a){let b=a[y];b in d||(d[b]=[]),d[b].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(um);this.layers=[];for(let y of h){let b=d[y];b.sort((v,x)=>{let N=s[v.id],T=s[x.id];return N<T?-1:N>T?1:0});for(let v of b)v instanceof sr&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(um);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let v=b.outboundLayer;if(v!=null){for(let x of b.inputTensors)if(m.indexOf(x)===-1)throw new $a(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${f}`);for(let x of b.outputTensors)m.push(x);f.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(v=>v===y).length;if(b!==1)throw new $a(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 B("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let 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={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new B(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new B(`${s.length} of ${a} weights are not set: ${s}`)}Yb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${$m}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=ax(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=vt(e);let n=new Hi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return mp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=vt(e);let n;return t==null?n=zi(null,e.length):n=vt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=wm(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(um);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,v=n[b];u.push(v)}let p=c.computeOutputShape(Nn(u)),d=wm(p),h=c.inboundNodes.indexOf(l);for(let m=0;m<d.length;m++){let f=`${c.name}_${h}_${m}`;n[f]=d[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}for(let i=0;i<s.length;i++){let o=s[i];tr(o in n),r.push(n[o])}return Nn(r)}runInternalGraph(e,t){t==null&&(t=zi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],c=e[o],u=t[o];n[l.id]=[c,u]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(um);for(let o of a){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,p=c.inputTensors,d=c.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,g,y,b;if(c.callArgs!=null&&(m=c.callArgs),h.length===1){let[v,x]=h[0];m.mask==null&&(m.mask=x),y=vt(u.call(v,m)),b=vt(u.computeMask(v,x)),f=[v],g=[x]}else f=h.map(v=>v[0]),g=h.map(v=>v[1]),m.mask==null&&(m.mask=g),y=vt(u.call(f,m)),b=vt(u.computeMask(f,g));if(u.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<d.length;++v){let x=d[v],N=y[v],T=b[v];n[x.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){tr(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,c]=n[o.id];i.push(l.shape),r.push(l),s.push(c)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof sr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=sr.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return D(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=sr.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let u=0;u<s.inboundNodes.length;u++){let p=s.inboundNodes[u],d=sr.nodeKey(s,u),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let g=p.inboundLayers[f],y=p.nodeIndices[f],b=p.tensorIndices[f],v=sr.nodeKey(g,y),x=t[v];x==null&&(x=0),m.push([g.name,x,b,h])}l.push(m)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=sr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];a.push([i.name,c,u])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=sr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];r.push([i.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,g){f.name in s?s[f.name].push(g):s[f.name]=[g]}function o(f,g){let y=[],b;for(let v of g){let x=v[0],N=v[1],T=v[2];if(b=v[3]==null?{}:v[3],!(x in r)){i(f,g);return}let C=r[x];if(C.inboundNodes.length<=N){i(f,g);return}let $=C.inboundNodes[N];y.push($.outputTensors[T])}y.length>0&&f.apply(Nn(y),b)}function l(f){let g=f.name,y=Ra(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[g]=y,f.inboundNodes.forEach(b=>{if(!(b instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${b}`);i(y,b)})}let c=t.name,u=t.layers;for(let f of u)l(f);for(;!Lz(s);)for(let f of u){let g=r[f.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let b of y)o(g,b)}}let p=[],d=[],h=t.inputLayers;for(let f of h){let g=f[0],y=f[1],b=f[2];tr(g in r);let v=r[g].inboundNodes[y].outputTensors;p.push(v[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];tr(g in r);let v=r[g].inboundNodes[y].outputTensors;d.push(v[b])}return new e({inputs:p,outputs:d,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){D(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function h4(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===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!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function wI(e,t){return h4(e,t,"classWeight")}async function kI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await r.data());Fe(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),nt(i,"float32")}else return null}function m4(e,t){return L(e,t)}var f4=32;function NI(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=II("input",e.inputNames,n),i=II("output",e.outputNames,a),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)k.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)k.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function II(e,t,n){if(n instanceof z)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new B(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function g4(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 b4(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.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}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.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 r=n.validationData!=null,s,i;if(r)if(TI(n.validationData))k.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=g4(n.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=uI(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=cI(u,p,n.epochs,null,null,y4(t,n),null,r,c);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let g={};await d.onEpochBegin(m);let y=0,b=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let v=await f.next();if(a&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let h=0;h<this.inputs.length;++h)c.push({key:this.inputs[h],value:n[h]});let u=new Hi(c),p=mp(this.outputs,u,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=m4(m,r[h]));let f=Ct(m);t.push(f),h===0?d=m:d=Z(d,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],g=this.metricsTensors[h][1];m=Ct(f(a[g],p[g]))}Kt(m),s.push(m)}return d=Ct(d),this.calculateLosses().forEach(h=>{d=Z(d,h)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>D(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:a[l]});let i=new Hi(s),o=mp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Ct(c(r[l],o[l]));l===0?n=u:n=Z(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],p=Ct(c(r[u],o[u]));t.push(p)}return t})}async fit(e,t,n={}){return k4(this,e,t,n)}async fitDataset(e,t){return b4(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Fe(s),Nn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Nh().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Nh().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Sr(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Sr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=Sr(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Sr(Am(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Sr(Am(e)));{let e={};for(let t in this.metrics)e[t]=Sr(Am(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=hp(e.optimizer_config),n=Ra(t),a;if(typeof e.loss=="string")a=Bi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Bi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Bi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Bi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Bi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=qt.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await qt.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:C4,generatedBy:`TensorFlow.js tfjs-layers v${$m}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await qt.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=qt.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;yI(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){yI(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Cr.className="Model";re.registerClass(Cr);var AI=class extends Cr{};AI.className="Functional";re.registerClass(AI);async function _4(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=hp(n),r=Ra(a,t);if(e.weightsManifest!=null){let s=await qt.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Fe(s)}return r}async function A4(e,t){if(t==null&&(t={}),typeof e=="string"){let n=qt.getLoadHandlers(e,t);if(n.length===0)n.push(qt.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return E4(e,void 0,t)}async function E4(e,t,n){if(n==null&&(n={}),e.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=Ra(hp(r),t,i),l=a.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=F4(a.weightData,a.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Fe(c),Fe(u.map(p=>p.tensor))}return o}function F4(e,t){let n=qt.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var lu=class extends Cr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:vm("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new B(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof lu||e instanceof Cr,n;if(t){if(n=e,n.outputs.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new B("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new B("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=aI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new B(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=nI(this.outputs[0])}this.inboundNodes=[],new Im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:zi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(mt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Cr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new $a("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new $a("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new $a("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new $a("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");r=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof lu))throw new $e(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=Ra(o,void 0,a);a&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};lu.className="Sequential";re.registerClass(lu);function $4(e){return new Cr(e)}function D4(e){return new lu(e)}function M4(e,t){return t==null&&(t={}),A4(e,t)}function Kk(e){return aI(e)}function R4(e,t){ba.registerCallbackConstructor(e,t)}var On=class extends re.Serializable{getConfig(){return{}}},FI=class extends On{apply(e,t=1){return hB(e,t)}};FI.className="elu";re.registerClass(FI);var $I=class extends On{apply(e){return Gh(e)}};$I.className="selu";re.registerClass($I);var DI=class extends On{apply(e){return Ye(e)}};DI.className="relu";re.registerClass(DI);var MI=class extends On{apply(e){return D(()=>Ri(6,Ye(e)))}};MI.className="relu6";re.registerClass(MI);var RI=class extends On{apply(e){return e}};RI.className="linear";re.registerClass(RI);var PI=class extends On{apply(e){return ma(e)}};PI.className="sigmoid";re.registerClass(PI);var OI=class extends On{apply(e){return fB(e)}};OI.className="hardSigmoid";re.registerClass(OI);var LI=class extends On{apply(e){return Yl(e)}};LI.className="softplus";re.registerClass(LI);var zI=class extends On{apply(e){return mB(e)}};zI.className="softsign";re.registerClass(zI);var BI=class extends On{apply(e){return ql(e)}};BI.className="tanh";re.registerClass(BI);var ux=class extends On{apply(e,t=-1){return _a(e,t)}};ux.className="softmax";re.registerClass(ux);var WI=class extends On{apply(e,t=-1){return Ph(e,t)}};WI.className="logSoftmax";re.registerClass(WI);var VI=class extends On{apply(e,t=1){return D(()=>ma(e.mul(t)).mul(e))}};VI.className="swish";re.registerClass(VI);function ps(e){return e.getClassName()}function cx(e,t={}){return rp(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function ds(e){if(e==null){let t={};return t.className="linear",t.config={},cx(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},cx(t)}else return e instanceof On?e:cx(e)}function px(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var UI=class extends re.Serializable{},gp=class extends UI{constructor(e){super();px(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return D(()=>{let t=xt([1]);return this.hasL1&&(t=Z(t,Ce(L(this.l1,Bt(e))))),this.hasL2&&(t=Z(t,Ce(L(this.l2,up(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};gp.className="L1L2";re.registerClass(gp);function P4(e){return px(e),new gp({l1:e!=null?e.l1:null,l2:0})}function O4(e){return px(e),new gp({l2:e!=null?e.l2:null,l1:0})}var GI={l1l2:"L1L2"};function ft(e){return Sb(e)}function HI(e,t={}){return rp(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function It(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in GI?GI[e]:e,config:{}};return HI(t)}else return e instanceof UI?e:HI(e)}var dx=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Re(e);let n=Ye(e);return this.maxValue!=null&&(n=Xt(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};dx.className="ReLU";re.registerClass(dx);var hx=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Re(e);return qc(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};hx.className="LeakyReLU";re.registerClass(hx);var mx=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=kt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=It(e.alphaRegularizer),this.alphaConstraint=Gt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new Jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Re(e),Jc(e,this.alpha.read())}getConfig(){let e={alphaInitializer:_t(this.alphaInitializer),alphaRegularizer:ft(this.alphaRegularizer),alphaConstraint:Ut(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};mx.className="PReLU";re.registerClass(mx);var fx=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new $e(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Re(e);return Kl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};fx.className="ELU";re.registerClass(fx);var gx=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Re(e);return n.mul(op(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};gx.className="ThresholdedReLU";re.registerClass(gx);var yx=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ux().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Re(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}};yx.className="Softmax";re.registerClass(yx);function uu(e,t,n){if(typeof e=="number")return zi(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!uB(r))throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Pa(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function Dm(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+us([n-t,0]);else if(a==="same")e=e*t;else throw new B(`Unsupport padding mode: ${a}.`);return e}function bx(e,t){return D(()=>(Mt(t),t==="channelsFirst"?Ue(e,[0,2,3,1]):e))}function jI(e,t){return D(()=>(Mt(t),t==="channelsFirst"?Ue(e,[0,2,3,4,1]):e))}function L4(e,t,n,a=1,r="valid",s,i=1){return D(()=>{if(s==null&&(s=Fa()),Mt(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Ue(e,[0,2,1])),r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Eh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ar(o,n)),o})}function qI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Fa()),Mt(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=bx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=is.conv2d({x:l,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function z4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Fa()),Mt(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=jI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Yy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ar(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var xx=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",xx.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=uu(t.kernelSize,e,"kernelSize"),this.strides=uu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,na(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Mt(this.dataFormat),this.activation=ds(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=uu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(tr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!_b(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ps(this.activation),useBias:this.useBias,biasInitializer:_t(this.biasInitializer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},yp=class extends xx{constructor(e,t){super(e,t);this.kernel=null,yp.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=mt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return D(()=>{e=Re(e);let n,a=this.bias==null?null:this.bias.read(),r=$k(this.activation.getClassName());if(r!=null&&this.rank===2)n=qI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=L4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=qI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=z4(e,this.kernel.read(),a,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=mt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Pa(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:_t(this.kernelInitializer),kernelRegularizer:ft(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 B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},bp=class extends yp{constructor(e){super(2,e);bp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!_b(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};bp.className="Conv2D";re.registerClass(bp);var Mm=class extends yp{constructor(e){super(3,e);Mm.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Mm.className="Conv3D";re.registerClass(Mm);var vx=class extends bp{constructor(e){super(e);if(this.inputSpec=[new Jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Re(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],l=a[i],c=this.kernelSize[0],u=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Dm(o,p,c,this.padding),m=Dm(l,d,u,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let g=Ah(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ue(g,[0,3,1,2])),this.bias!=null&&(g=ar(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[a]=Dm(t[a],o,s,this.padding),t[r]=Dm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};vx.className="Conv2DTranspose";re.registerClass(vx);var KI=class extends yp{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=kt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=It(t.depthwiseRegularizer),this.depthwiseConstraint=Gt(t.depthwiseConstraint),this.pointwiseInitializer=kt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=It(t.pointwiseRegularizer),this.pointwiseConstraint=Gt(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Jt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{e=Re(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=Ue(e,[0,2,3,1])),n=Pi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(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=_t(this.depthwiseInitializer),e.pointwiseInitializer=_t(this.pointwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.pointwiseRegularizer=ft(this.pointwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseConstraint),e.pointwiseConstraint=Ut(this.pointwiseConstraint),e}};KI.className="SeparableConv";var wx=class extends KI{constructor(e){super(2,e)}};wx.className="SeparableConv2D";re.registerClass(wx);var Rm=class extends yp{constructor(e){super(1,e);Rm.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"&&!_b(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Rm.className="Conv1D";re.registerClass(Rm);var kx=class extends qe{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 D(()=>{if(e=Re(e),this.dataFormat==="channelsLast"){let n=cm(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return cm(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=cm(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return cm(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}};kx.className="Cropping2D";re.registerClass(kx);var Ix=class extends qe{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,Mt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,iB(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 D(()=>{let n=Re(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Ue(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ix.className="UpSampling2D";re.registerClass(Ix);function B4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Fa()),Mt(r);let i=bx(e,r);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=vr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var Nx=class extends xx{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=mt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,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 D(()=>{e=Re(e);let n=B4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Pa(t,this.kernelSize[0],this.padding,this.strides[0]),s=Pa(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=_t(this.depthwiseInitializer),e.depthwiseRegularizer=ft(this.depthwiseRegularizer),e.depthwiseConstraint=Ut(this.depthwiseRegularizer),e}};Nx.className="DepthwiseConv2D";re.registerClass(Nx);function XI(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function YI(e,t,n,a=!1,r,s,i=!1,o=!1){return D(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Da(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Zn(r,-1)),r=Ue(r,c)),a&&(t=Rn(t,0),r!=null&&(r=Rn(r,0)));let u=[],p,d=n,h=t.shape[0],m=ht(t),f;r!=null&&(f=ht(r));for(let y=0;y<h;++y){let b=m[y],v=D(()=>e(b,d));if(r==null)p=v[0],d=v[1];else{let x=D(()=>{let N=f[y],T=Mn(N).sub(N),C=v[0].mul(N).add(d[0].mul(T)),$=d.map((F,O)=>v[1][O].mul(N).add(F.mul(T)));return{output:C,newStates:$}});p=x.output,d=x.newStates}o&&u.push(p)}let g;return o&&(g=Dt(u,1)),[p,g,d]})}var rr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Pm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Jt({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 Da(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Kb(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return D(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}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.");Kb(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Jt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Jt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>xt([n,a])):this.states_=[xt([n,this.cell.stateSize])];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>xt([n,a])):this.states_[0]=xt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Fe(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new B(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Kt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=XI(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Jt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ma){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Re(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=YI((d,h)=>{let m=this.cell.call([d].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,a);let p=this.returnSequences?c:l;return this.returnState?[p].concat(u):p})}getInitialState(e){return D(()=>{let t=xt(e.shape);return t=Ce(t,[1,2]),t=lp(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Mb(t,[1,n]):t):this.cell.stateSize>1?[Mb(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()===rr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ra(a,n);return new e(Object.assign(t,{cell:r}))}};rr.className="RNN";re.registerClass(rr);var pp=class extends qe{},Om=class extends pp{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=ds(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=ru([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 D(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>Mn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>Mn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=nr(L(e,s),this.kernel.read()):r=nr(e,this.kernel.read()),this.bias!=null&&(r=ar(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,nr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ps(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(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)}};Om.className="SimpleRNNCell";re.registerClass(Om);var Tx=class extends rr{constructor(e){e.cell=new Om(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};Tx.className="SimpleRNN";re.registerClass(Tx);var Lm=class extends pp{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Yt(this.units,"units"),this.activation=ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ds(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=ru([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,us([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 D(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>Mn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>Mn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=nr(e,this.kernel.read());this.useBias&&(c=ar(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,s[0]));let u=this.recurrentKernel.read(),[p,d]=Pn(u,[2*this.units,this.units],u.rank-1),h=nr(a,p),[m,f,g]=Pn(c,3,c.rank-1),[y,b]=Pn(h,2,h.rank-1);i=this.recurrentActivation.apply(Z(m,y)),o=this.recurrentActivation.apply(Z(f,b));let v=nr(L(o,a),d);l=this.activation.apply(Z(g,v));let x=Z(L(i,a),L(Z(1,St(i)),l));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ps(this.activation),recurrentActivation:ps(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(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)}};Lm.className="GRUCell";re.registerClass(Lm);var Sx=class extends rr{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 Lm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Sx.className="GRU";re.registerClass(Sx);var xp=class extends pp{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=ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ds(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=ru([1,us([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ru([1,us([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=mt(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 a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ya{apply(i,o){let l=r.apply([s]),c=new dm().apply([s]),u=r.apply([s*2]);return Vk(Vk(l,c),u)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>Mn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>Mn(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let p=nr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,nr(a,this.recurrentKernel.read())),this.useBias&&(p=ar(p,this.bias.read()));let[d,h,m,f]=Pn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),c=Z(L(l,r),L(o,this.activation.apply(m))),u=this.recurrentActivation.apply(f);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ps(this.activation),recurrentActivation:ps(this.recurrentActivation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),recurrentInitializer:_t(this.recurrentInitializer),biasInitializer:_t(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ft(this.kernelRegularizer),recurrentRegularizer:ft(this.recurrentRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(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)}};xp.className="LSTMCell";re.registerClass(xp);var Cx=class extends rr{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 xp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Cx.className="LSTM";re.registerClass(Cx);var Pm=class extends pp{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 D(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Kb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Vi(`RNNCell_${a}`,()=>{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=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ra(r,n));return new e({cells:a})}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 Xb(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Yb(t)}};Pm.className="StackedRNNCells";re.registerClass(Pm);function hs(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>Gk(t(),n),i=()=>cp(s,t,a);return!r||r<=1?Kt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Kt(o.clone()))}var W4=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},JI=class extends rr{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 Jt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Fe(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Fe(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}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 D(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=xt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Tr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xt(r)):this.states_=[xt(r)];else if(e==null)Fe(this.states_),this.keptStates!=null&&(Fe(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xt(r)):this.states_[0]=xt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Fe(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Kt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Pa(l,a[0],r,s[0],i[0]),p=Pa(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};JI.className="ConvRNN2D";var zm=class extends xp{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Yt(this.filters,"filters"),this.kernelSize=uu(n,2,"kernelSize"),this.kernelSize.forEach(o=>Yt(o,"kernelSize")),this.strides=uu(a||1,2,"strides"),this.strides.forEach(o=>Yt(o,"strides")),this.padding=r||"valid",na(this.padding),this.dataFormat=s||"channelsLast",Mt(this.dataFormat),this.dilationRate=uu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Yt(o,"dilationRate"))}build(e){var t;e=mt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends ya{apply(u,p){let d=l.apply([c]),h=Ja([c]),m=l.apply([c*2]);return Pb([d,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return D(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=hs({ones:()=>Mn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,ie,ee)=>!ie||!ie[ee]?Q:L(ie[ee],Q),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=hs({ones:()=>Mn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[v,x,N,T]=Pn(this.kernel.read(),i,b),[C,$,F,O]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,v,C,this.padding),u=this.inputConv(u,x,$,this.padding),p=this.inputConv(p,N,F,this.padding),d=this.inputConv(d,T,O,this.padding);let[W,V,H,K]=Pn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,W),f=this.recurrentConv(f,V),g=this.recurrentConv(g,H),y=this.recurrentConv(y,K);let j=this.recurrentActivation.apply(Z(c,m)),Y=this.recurrentActivation.apply(Z(u,f)),J=Z(L(Y,s),L(j,this.activation.apply(Z(p,g)))),ne=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(J));return[ne,ne,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=W4(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=Ft(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ar(r,n,this.dataFormat):r}recurrentConv(e,t){return Ft(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};zm.className="ConvLSTM2DCell";re.registerClass(zm);var _x=class extends JI{constructor(e){let t=new zm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};_x.className="ConvLSTM2D";re.registerClass(_x);var Bm=class extends qe{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 a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return cp(()=>Gk(n,this.rate,r,this.seed),()=>n,a)}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()}};Bm.className="Dropout";re.registerClass(Bm);var Ex=class extends Bm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ex.className="SpatialDropout1D";re.registerClass(Ex);var Ax=class extends qe{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=ds(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=mt(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=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=$k(this.activation.getClassName()),r;return a!=null?r=nr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=nr(n,this.kernel.read()),this.bias!=null&&(r=ar(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ps(this.activation),useBias:this.useBias,kernelInitializer:_t(this.kernelInitializer),biasInitializer:_t(this.biasInitializer),kernelRegularizer:ft(this.kernelRegularizer),biasRegularizer:ft(this.biasRegularizer),activityRegularizer:ft(this.activityRegularizer),kernelConstraint:Ut(this.kernelConstraint),biasConstraint:Ut(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="Dense";re.registerClass(Ax);var Fx=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ls(e,1)]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return dB(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Fx.className="Flatten";re.registerClass(Fx);var $x=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=ds(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.activation.apply(n)})}getConfig(){let e={activation:ps(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};$x.className="Activation";re.registerClass($x);var Dx=class extends qe{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 D(()=>(e=Re(e),cB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Dx.className="RepeatVector";re.registerClass(Dx);var Mx=class extends qe{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.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let l=a[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else r*=l}let i=ls(e);if(s!==null){if(r===0||i%r!=0)throw new B(n);a[s]=i/r}else if(i!==r)throw new B(n);return a}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 D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Mx.className="Reshape";re.registerClass(Mx);var Rx=class extends qe{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=Da(1,e.dims.length+1);if(!k.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 Jt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Ue(Re(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Rx.className="Permute";re.registerClass(Rx);var Px=class extends qe{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=Re(e),a=-1;return Wc(rs(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=-1,r=!0,s=Wc(rs(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};Px.className="Masking";re.registerClass(Px);var Ox=class extends qe{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(vt(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 D(()=>this.maskZero?(e=Re(e),rs(e,Ge(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);return n.dtype!=="int32"&&(n=op(n,"int32")),Uk(this.embeddings.read(),n.as1D()).reshape(mt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:_t(this.embeddingsInitializer),embeddingsRegularizer:ft(this.embeddingsRegularizer),activityRegularizer:ft(this.activityRegularizer),embeddingsConstraint:Ut(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Ox.className="Embedding";re.registerClass(Ox);var qi=class extends qe{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 a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=os(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&os(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=us(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=lp(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(ls(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=Da(1,l).concat([0]);n.push(Ue(o,c)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=Ue(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(Da(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=os(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Zn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=fa(n,t[a]);return n})}},Lx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return t})}};Lx.className="Add";re.registerClass(Lx);var zx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};zx.className="Multiply";re.registerClass(zx);var Bx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=Z(t,e[n]);return L(1/e.length,t)})}};Bx.className="Average";re.registerClass(Bx);var Wx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ca(t,e[n]);return t})}};Wx.className="Maximum";re.registerClass(Wx);var Vx=class extends qi{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ri(t,e[n]);return t})}};Vx.className="Minimum";re.registerClass(Vx);var Ux=class extends qi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return D(()=>Pb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return D(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Mn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(Zn(t[s],-1)):a.push(t[s]);let r=Qe(a,this.axis);return Ch(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ux.className="Concatenate";re.registerClass(Ux);function vp(e,t){for(;e<0;)e+=t;return e}function V4(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(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.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 a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return D(()=>{let i;if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;a>r?l=a+r-3:l=a-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Gx=class extends qi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.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 a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new B(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>vp(r,e[s].shape.length)):a=[vp(this.axes,t.shape.length),vp(this.axes,n.shape.length)],this.normalize&&(t=Nm(t,a[0]),n=Nm(n,a[1])),V4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[vp(this.axes,e.length),vp(this.axes,t.length)],n}computeOutputShape(e){k.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 a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Gx.className="Dot";re.registerClass(Gx);var Hx=class extends qe{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 D(()=>{this.invokeCallHook(e,t);let n=Re(e);return cp(()=>pm(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Hx.className="GaussianNoise";re.registerClass(Hx);var jx=class extends qe{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 D(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.rate>0&&this.rate<1?cp(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(pm(n.shape,1,a))},()=>n,t.training||!1):n})}};jx.className="GaussianDropout";re.registerClass(jx);var qx=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Re(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 D(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return cp(()=>{let a=Re(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=kr(Zl(n),this.rate);o=op(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Re(e),t.training||!1)}return e})}};qx.className="AlphaDropout";re.registerClass(qx);function wp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=p1(e,t,n,a,r,s);else if(e.rank===3)i=d1(e,t,n,a,r,s);else if(e.rank===4)i=h1(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function U4(e,t,n,a,r=.001){return D(()=>{let s=zh(e,a),i=s.mean,o=s.variance;return[wp(e,i,o,n,t,r),i,o]})}function G4(e,t,n,a,r=.001){return D(()=>{let s=zh(e,a),i=s.mean,o=s.variance,l=[];for(let h of Da(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[wp(e,c,u,d,p,r),i,o]})}function H4(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Da(0,e.rank-1))?U4(e,t,n,a,r):G4(e,t,n,a,r)}var Kx=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=kt(e.betaInitializer||"zeros"),this.gammaInitializer=kt(e.gammaInitializer||"ones"),this.movingMeanInitializer=kt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=kt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Gt(e.betaConstraint),this.gammaConstraint=Gt(e.gammaConstraint),this.betaRegularizer=It(e.betaRegularizer),this.gammaRegularizer=It(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Jt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Re(e),r=a.shape,s=r.length,i=Da(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=zi(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,Da(0,s).slice(0,s-1)),p=()=>{if(u){let g=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),b=this.center?this.beta.read().reshape(l):null,v=this.scale?this.gamma.read().reshape(l):null;return wp(a,g,y,b,v,this.epsilon)}else return wp(a,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 p();let[d,h,m]=H4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let v=1-b,x=g.read(),N=x.sub(y).mul(v);g.write(x.sub(N))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),movingMeanInitializer:_t(this.movingMeanInitializer),movingVarianceInitializer:_t(this.movingVarianceInitializer),betaRegularizer:ft(this.betaRegularizer),gammaRegularizer:ft(this.gammaRegularizer),betaConstraint:Ut(this.betaConstraint),gammaConstraint:Ut(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Kx.className="BatchNormalization";re.registerClass(Kx);var Xx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=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=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==os(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Re(e),a=n.shape,r=a.length;return D(()=>{let s=!0,{mean:i,variance:o}=zh(n,this.axis,s),l=zi(1,r);for(let m of this.axis)l[m]=a[m];let c=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(l):m,u=c(this.gamma.read()),p=c(this.beta.read()),d=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(d.push(a[m]),h.push(1)):(d.push(1),h.push(a[m]));return i=i.tile(d),o=o.tile(d),u=u.tile(h),p=p.tile(h),wp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:_t(this.betaInitializer),gammaInitializer:_t(this.gammaInitializer),betaRegularizer:ft(this.betaRegularizer),gammaRegularizer:ft(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="LayerNormalization";re.registerClass(Xx);function j4(e,t,n){return D(()=>{if(e.rank!==4)throw new B(`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 B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Fa()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],ta(e,a)})}var Yx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Fa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=mt(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 D(()=>j4(Re(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Yx.className="ZeroPadding2D";re.registerClass(Yx);function Wm(e,t,n,a,r,s){return D(()=>{Mt(r),Pk(s),na(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Fa()),s==null&&(s="max"),e=bx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=$t(e,t,n,o):i=Jn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function ZI(e,t,n,a,r,s){return D(()=>{Mt(r),Pk(s),na(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Fa()),s==null&&(s="max"),e=jI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=ob(e,t,n,o):i=qy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var QI=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(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 B(`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,na(this.padding),this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=Pa(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return D(()=>{this.invokeCallHook(e,t),e=lp(Re(e),2);let n=this.poolingFunction(Re(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ss(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Jx=class extends QI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),na(a),Wm(e,t,n,a,r,"max")}};Jx.className="MaxPooling1D";re.registerClass(Jx);var Zx=class extends QI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),na(a),Wm(e,t,n,a,r,"avg")}};Zx.className="AveragePooling1D";re.registerClass(Zx);var eN=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),na(this.padding),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Pa(t,this.poolSize[0],this.padding,this.strides[0]),n=Pa(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 D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(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}},Qx=class extends eN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),na(a),Wm(e,t,n,a,r,"max")}};Qx.className="MaxPooling2D";re.registerClass(Qx);var ev=class extends eN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),na(a),Wm(e,t,n,a,r,"avg")}};ev.className="AveragePooling2D";re.registerClass(ev);var tN=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),na(this.padding),this.inputSpec=[new Jt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Pa(t,this.poolSize[0],this.padding,this.strides[0]),n=Pa(n,this.poolSize[1],this.padding,this.strides[1]),a=Pa(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return D(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(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}},tv=class extends tN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),na(a),ZI(e,t,n,a,r,"max")}};tv.className="MaxPooling3D";re.registerClass(tv);var nv=class extends tN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Mt(r),na(a),ZI(e,t,n,a,r,"avg")}};nv.className="AveragePooling3D";re.registerClass(nv);var nN=class extends qe{constructor(e){super(e);this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},av=class extends nN{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return Ct(n,1)})}};av.className="GlobalAveragePooling1D";re.registerClass(av);var rv=class extends nN{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return ea(n,1)})}};rv.className="GlobalMaxPooling1D";re.registerClass(rv);var aN=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Mt(this.dataFormat),this.inputSpec=[new Jt({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}},sv=class extends aN{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};sv.className="GlobalAveragePooling2D";re.registerClass(sv);var iv=class extends aN{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?ea(n,[1,2]):ea(n,[2,3])})}};iv.className="GlobalMaxPooling2D";re.registerClass(iv);var rN=class extends qe{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 a=t.layer,r=Ra(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},ov=class extends rN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=mt(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=mt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return D(()=>(e=Re(e),YI((n,a)=>[Re(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};ov.className="TimeDistributed";re.registerClass(ov);function q4(e){Wi(sB,"BidirectionalMergeMode",e)}var K4="concat",lv=class extends rN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ra(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ra(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?K4:e.mergeMode,q4(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,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Nn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=XI(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new Jt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(a!=null)throw new $e("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ma;for(let l of s)if(l instanceof Ma!==o)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let p=super.apply(l,t);return this.inputSpec=u,p}else return super.apply(e,t)}call(e,t){return D(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Rn(r,1));let i;return this.mergeMode==="concat"?i=Pb([a,r]):this.mergeMode==="sum"?i=Z(a,r):this.mergeMode==="ave"?i=L(.5,Z(a,r)):this.mergeMode==="mul"?i=L(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Vi(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Vi(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 a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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c=I("strides",e,t,n),u=Gm(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[h,m]=I("args",e,t,n),f=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:p,dilations:d,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var jV=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=I("stride",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Eh(I("x",e,t,n),I("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=I("strides",e,t,n),r=Gm(e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Ft(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=MN(e,t,n);return[is.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=MN(e,t,n);return[is.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=I("outputShape",e,t,n),r=I("strides",e,t,n),s=Gm(e,t,n);return[Ah(I("x",e,t,n),I("filter",e,t,n),a,[r[1],r[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let a=I("strides",e,t,n),r=Gm(e,t,n),s=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[vr(I("input",e,t,n),I("filter",e,t,n),[a[1],a[2]],r,i,[s[1],s[2]])]}case"Conv3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[Yy(I("x",e,t,n),I("filter",e,t,n),[a[1],a[2],a[3]],r,s,[i[1],i[2],i[3]])]}case"AvgPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[Jn(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPool":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[$t(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPoolWithArgmax":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:l}=$1(I("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r,i);return[o,l]}case"AvgPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[qy(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"MaxPool3D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("kernelSize",e,t,n);return[ob(I("x",e,t,n),[s[1],s[2],s[3]],[a[1],a[2],a[3]],r)]}case"Dilation2D":{let a=I("strides",e,t,n),r=I("pad",e,t,n),s=I("dilations",e,t,n),i=a[1],o=a[2],l=s[1],c=s[2];return[Zy(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},qV=(e,t,n)=>{switch(e.op){case"Fill":{let a=I("shape",e,t,n),r=I("dtype",e,t,n),s=I("value",e,t,n);return[In(a,s,r)]}case"LinSpace":{let a=I("start",e,t,n),r=I("stop",e,t,n),s=I("num",e,t,n);return[T1(a,r,s)]}case"Multinomial":{let 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a=I("boxes",e,t,n),r=I("scores",e,t,n),s=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var KV=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=wv(e,t,n),c=await Qa.nonMaxSuppressionWithScoreAsync(a,r,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=wv(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await Qa.nonMaxSuppressionPaddedAsync(a,r,s,i,o,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:a,scores:r,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=wv(e,t,n);return[await Qa.nonMaxSuppressionAsync(a,r,s,i,o)]}case"Where":{let a=pe(I("condition",e,t,n),"bool"),r=[await xb(a)];return 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I("x",e,t,n).map(c=>nt(c.shape));case"Size":return[he(I("x",e,t,n).size,"int32")];case"Rank":return[he(I("x",e,t,n).rank,"int32")];case"NoOp":return[he(1)];case"Print":let s=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JV=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=he(0),this.tensorMap=new Map,Kt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(a=>a.dispose()),this.tensorMap.clear(),D(()=>{let a=ht(t),r=n.length,s=a.length;k.assert(r===s,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${s} elements.`);for(let i=0;i<r;i++){let o=n[i],l=a[i];Kt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return D(()=>{let a=[];for(let r=0;r<n.length;r++){let s=n[r],i=this.findWithDefault(s,t);a.push(i)}return Dt(a)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},ZV=async(e,t,n,a)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),s=I("valueDType",e,t,n),i=new JV(r,s);return a.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let 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implemented`)}},eU=(e,t,n)=>{switch(e.op){case"Equal":return[wr(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[rs(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[Qn(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[kr(I("a",e,t,n),I("b",e,t,n))];case"Less":return[Kc(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[as(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[fa(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Xc(I("a",e,t,n))];case"LogicalOr":return[Oh(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[kn(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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a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Jy(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Hc(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function RN(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return D(()=>OV(s,i,o));case"basic_math":return D(()=>LV(s,i,o));case"control":return HV(s,i,o);case"convolution":return D(()=>jV(s,i,o));case"creation":return D(()=>qV(s,i,o));case"dynamic":return KV(s,i,o);case"evaluation":return D(()=>XV(s,i,o));case"image":return D(()=>QV(s,i,o));case"graph":return D(()=>YV(s,i,o));case"logical":return D(()=>eU(s,i,o));case"matrices":return D(()=>tU(s,i,o));case"normalization":return D(()=>nU(s,i,o));case"reduction":return D(()=>aU(s,i,o));case"slice_join":return D(()=>rU(s,i,o));case"spectral":return D(()=>sU(s,i,o));case"transformation":return D(()=>iU(s,i,o));case"hash_table":return ZV(s,i,o,a);case"custom":let l=dN(s.op);if(l&&l.customExecutor)return l.customExecutor(new PV(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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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 LN(e,t,n,a){let r=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Ln(d)[0]),u=[];a!=null&&(u=a.map(d=>Ln(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((ON(d)||oU(d)||lU(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function uU(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(u=>Ln(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{a.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{a.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{a.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(p=>{!l.has(p.name)&&a.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&s.push(p)})}return c}var cU=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],pU=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],dU=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function ON(e){return cU.indexOf(e.op)>=0}function oU(e){return pU.indexOf(e.op)>=0}function lU(e){return dU.indexOf(e.op)>=0}var kv=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 kv(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(a=>a.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(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=LN(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return uU(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 a=n.map(u=>this.graph.nodes[Ln(u)[0]]),r=t.map(u=>Ln(u)[0]),s=r.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return D(()=>{let u=new PN(this.weightMap,l,c,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,g]=Ln(m),y=[];y[g]=e[m],p[f]=y});let d=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let g=RN(f,p,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. 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You can use model.execute() instead.");let y=o.filter(b=>!ON(b)&&!Sn(b.name,h,t)).map(b=>b.name);if(y.length>0){let b="";throw u!=null&&(b=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. 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c}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=_r(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Sn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Sn(l,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[a]=Ln(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},mU="?tfjs-format=file",fU="model.json",zN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new hU}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=qt.browserHTTPRequest(e,this.loadOptions);else{let t=qt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(qt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let a=qt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new kv(FN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=FN.Instance.transformGraph(e.modelInitializer);this.initializer=new kv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=qt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 z)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function gU(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},LU=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Fe(e.value)}return this.upstream.next()}},zU=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},BU=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},WU=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Fe(e.value)}}},VU=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Sa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Sa.getTensorsInContainer(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},UU=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},QN=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Sa.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Sa.getTensorsInContainer(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Tv=class extends Zt{constructor(){super();this.outputQueue=new Iv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},GU=class extends Tv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Sa.getTensorsInContainer(e.value),n=this.transform(e.value),a=Sa.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Sa.isTensorInList(r,a)||r.dispose();return!0}},ZN=class extends Zt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ms;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ms||(ms={}));var RU=class extends Zt{constructor(e,t=ms.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof Zt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await XN(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ms.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ms.SHORTEST:return{value:null,done:!0};case ms.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},eT=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new YN(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},HU=class extends eT{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=TU.alea(n||k.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},cu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),zn(async()=>(await n.iterator()).columnMajorBatch(e,t,jU),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,zn(async()=>(await t.iterator()).filter(a=>D(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return zn(async()=>(await t.iterator()).map(n=>D(()=>e(n))),this.size)}mapAsync(e){let t=this;return zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,zn(async()=>{let a=Nv(async()=>({value:await t.iterator(),done:!1}));return MU(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=NU.alea(t||k.now().toString());return zn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,zn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};cu.MAX_BUFFER_SIZE=1e4;function zn(e,t=null){return new class extends cu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function yU(e){return zn(async()=>JN(e),e.length)}function bU(e){if(!pu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return zn(async()=>{let n=await XN(e,a=>{if(a instanceof cu)return{value:a.iterator(),recurse:!1};if(pu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return PU(n,ms.SHORTEST)},t)}function jU(e){if(e===null)return null;let t=e[0];return EU(t)?{value:qU(e),recurse:!1}:{value:null,recurse:!0}}function qU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof z?Dt(e):Xn(e)}var VN=class extends cu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},jm='"',Ip=Symbol("out"),tT=Symbol("field"),qm=Symbol("quote"),Sv=Symbol("quoteafterquote"),nT=Symbol("quoteinquote"),UN=class extends cu{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new VN(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?a[s]=l:n[s]=l}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=Ip;for(let i=0;i<r;i++)switch(s){case Ip:switch(e.charAt(i)){case jm:a=i+1,s=qm;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Ip;break;default:s=tT,a=i;break}break;case tT:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=Ip,a=i+1;break;default:}break;case qm:switch(e.charAt(i)){case jm:s=Sv;break;default:}break;case Sv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=Ip,a=i+1;break;case jm:s=qm;break;default:s=nT;break}break;case nT:switch(e.charAt(i)){case jm:s=qm;break;default:}break;default:}if(s===Sv?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},aT=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(te().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new aT(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 a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[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(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({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(r),a({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((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),Xn(n,t)}},rT=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=nt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Ea([s,r,o,i],[1,4])}else this.cropBox=Ea([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(te().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 rT(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=Ai.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=e.toFloat().expandDims(0),n;n=Qa.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return n.reshape(a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},sT=class{},iT=class extends Zt{split(e){return new KU(this,e)}},KU=class extends iT{constructor(e,t){super();this.upstream=e,this.impl=new XU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},XU=class extends Tv{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},JU=class extends Zt{decodeUTF8(){return new YU(this)}},YU=class extends iT{constructor(e){super();this.upstream=e,this.impl=new ZU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ZU=class extends Tv{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=sA();this.decoder=new 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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ce=se*l-y,de=ce;for(;de<0;)de+=p;let fe=Math.min(r.inWidth,m+ce),xe=le+se*$,be=b,Se=0,_e=0;for(let Ke=j;Ke<Y;Ke+=c){let Ve=W+Ke*a[1];for(let it=ie;it<ee;it+=u){let ut=Ve+it*a[2];for(let He=de;He<fe;He+=p){let pt=ut+He*a[3],dt=e[pt+V];if(s==="max"&&dt>be?be=dt:s==="avg"&&(Se+=dt,_e++),isNaN(be))break}if(isNaN(be))break}if(isNaN(be))break}let Me=xe+V;x[Me]=s==="avg"?Se/_e:be}}}}return v}function SH(e,t){let n=Le(t.outShape,"int32"),a=t.strideDepth,r=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,m=t.padInfo.left;for(let f=0;f<t.batchSize;++f)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let b=y*a-d,v=b;for(;v<0;)v+=i;let x=Math.min(t.inDepth,c+b);for(let N=0;N<t.outHeight;++N){let T=N*r-h,C=T;for(;C<0;)C+=o;let $=Math.min(t.inHeight,u+T);for(let F=0;F<t.outWidth;++F){let O=F*s-m,W=O;for(;W<0;)W+=l;let V=Math.min(t.inWidth,p+O),H=Number.NEGATIVE_INFINITY,K=-1;for(let j=v;j<x;j+=i){let Y=j-b;for(let J=C;J<$;J+=o){let ne=J-T;for(let Q=W;Q<V;Q+=l){let ie=Q-O,ee=e.get(f,j,J,Q,g);ee>=H&&(H=ee,K=Y*u*p+ne*u+ie)}}}n.set(K,f,y,N,F,g)}}}return n}function CH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Ie(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=E.computePool3DInfo(s.shape,i,o,c,l,u),d=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=p.dilationDepth,v=p.dilationHeight,x=p.dilationWidth,N=p.effectiveFilterDepth,T=p.effectiveFilterHeight,C=p.effectiveFilterWidth,$=N-1-p.padInfo.front,F=C-1-p.padInfo.left,O=T-1-p.padInfo.top,W=Le(s.shape,"float32"),V=1/(f*g*y),H=n.bufferSync(r);for(let K=0;K<p.batchSize;++K)for(let j=0;j<p.inChannels;++j)for(let Y=0;Y<p.inDepth;++Y)for(let J=0;J<p.inHeight;++J)for(let ne=0;ne<p.inWidth;++ne){let Q=Y-$,ie=J-O,ee=ne-F,le=0;for(let se=0;se<N;se+=b){let ce=(Q+se)/d;if(!(ce<0||ce>=p.outDepth||Math.floor(ce)!==ce))for(let de=0;de<T;de+=v){let fe=(ie+de)/h;if(!(fe<0||fe>=p.outHeight||Math.floor(fe)!==fe))for(let xe=0;xe<C;xe+=x){let be=(ee+xe)/m;be<0||be>=p.outWidth||Math.floor(be)!==be||(le+=H.get(K,ce,fe,be,j))}}}W.set(le*V,K,Y,J,ne,j)}return n.makeTensorInfo(W.shape,W.dtype,W.values)}var $H={kernelName:Bd,backendName:"cpu",kernelFunc:FH};function DH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Ie([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=E.computePool2DInfo(i.shape,o,l,1,c),p=u.strideHeight,d=u.strideWidth,h=u.filterHeight,m=u.filterWidth,f=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,b=u.effectiveFilterWidth,v=b-1-u.padInfo.left,x=y-1-u.padInfo.top,N=Le(i.shape,"float32"),T=1/(h*m),C=n.data.get(r.dataId).values,$=Le(r.shape,"float32",C);for(let F=0;F<u.batchSize;++F)for(let O=0;O<u.inChannels;++O)for(let W=0;W<u.inHeight;++W)for(let V=0;V<u.inWidth;++V){let H=W-x,K=V-v,j=0;for(let Y=0;Y<y;Y+=f){let J=(H+Y)/p;if(!(J<0||J>=u.outHeight||Math.floor(J)!==J))for(let ne=0;ne<b;ne+=g){let Q=(K+ne)/d;Q<0||Q>=u.outWidth||Math.floor(Q)!==Q||(j+=$.get(F,J,Q,O))}}N.set(j*T,F,W,V,O)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var MH={kernelName:zd,backendName:"cpu",kernelFunc:DH};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var PH={kernelName:Ks,backendName:"cpu",kernelFunc:RH};function OH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;Ie([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=E.getReshaped(r.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(r.shape,s,o),p=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),h=Nt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=va({inputs:{x:h},backend:n,attrs:{perm:c}}),f=Nt({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Xi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var LH={kernelName:yc,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,c=Cv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var BH={kernelName:Wd,backendName:"cpu",kernelFunc:zH},WH=ot(jr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),VH={kernelName:jr,backendName:"cpu",kernelFunc:WH},UH=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],p=l[c];a[c]=Math.hypot(u,p)}return n.makeOutput(a,t.shape,"float32")},GH={kernelName:bc,backendName:"cpu",kernelFunc:UH};function mu(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var HH={kernelName:th,backendName:"cpu",kernelFunc:mu};function fu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=E.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return 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c.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var jH={kernelName:qo,backendName:"cpu",kernelFunc:fu};function GT(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a;Ie([r,s],"conv2d");let p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,g=d.dilationWidth,y=d.padInfo.left,b=d.padInfo.top,v=d.dataFormat==="channelsLast",x=new zt(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),C=N[0],$=v?N[1]:N[2],F=v?N[2]:1,O=v?1:N[1],W=x.strides[0],V=v?x.strides[1]:x.strides[2],H=v?x.strides[2]:1,K=v?1:x.strides[1],j=n.data.get(r.dataId).values,Y=n.data.get(s.dataId).values,J=x.values;for(let ne=0;ne<d.batchSize;++ne){let Q=ne*C,ie=ne*W;for(let ee=0;ee<d.outHeight;++ee){let le=ie+ee*V,se=ee*d.strideHeight-b;for(let ce=0;ce<h;++ce){let de=se+ce*f;if(de<0||de>=d.inHeight)continue;let fe=ce*T[0],xe=Q+de*$;for(let 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JH={kernelName:Bs,backendName:"cpu",kernelFunc:YH};function ZH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;Ie([r,s],"conv3d");let c=E.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,v=g.top,x=new zt(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,C=x.values,$=k.computeStrides(r.shape),F=k.computeStrides(s.shape);for(let O=0;O<c.batchSize;++O){let W=O*$[0],V=O*x.strides[0];for(let H=0;H<c.outDepth;++H){let K=V+H*x.strides[1],j=H*c.strideDepth-y;for(let Y=0;Y<u;++Y){let J=j+Y*h;if(J<0||J>=c.inDepth)continue;let ne=Y*F[0],Q=W+J*$[1];for(let ie=0;ie<c.outHeight;++ie){let ee=K+ie*x.strides[2],le=ie*c.strideHeight-v;for(let se=0;se<p;++se){let ce=le+se*m;if(ce<0||ce>=c.inHeight)continue;let de=ne+se*F[1],fe=Q+ce*$[2];for(let xe=0;xe<c.outWidth;++xe){let 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n.makeTensorInfo(y.shape,y.dtype,y.values)}var u6={kernelName:Xo,backendName:"cpu",kernelFunc:l6};function c6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;Ie(r,"cumsum");let l=E.getAxesPermutation([s],r.shape.length),c=r;l!=null&&(c=va({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=E.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let p=ha(c.dtype,"int32"),d=k.makeZerosTypedArray(k.sizeFromShape(c.shape),p),h=n.data.get(c.dataId).values,m=c.shape[c.shape.length-1],f=o?(y,b)=>y+m-b-1:(y,b)=>y+b;for(let y=0;y<h.length;y+=m)for(let b=0;b<m;b++){let v=f(y,b);if(b===0)d[v]=i?0:h[v];else{let x=f(y,b-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(c.shape,p,d);if(l!=null){let y=E.getUndoAxesPermutation(l),b=va({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),b}return g}var p6={kernelName:Vs,backendName:"cpu",kernelFunc:c6};function d6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(s.dataId).values,u=Cv(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=dT(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var h6={kernelName:jd,backendName:"cpu",kernelFunc:d6};function m6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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b6={kernelName:qd,backendName:"cpu",kernelFunc:y6};function x6(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a;Ie([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new zt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,v=n.data.get(r.dataId).values,[x,N,T]=p,C=n.data.get(s.dataId).values,[$,F,O]=d,{batchSize:W,filterHeight:V,filterWidth:H,inChannels:K,inHeight:j,inWidth:Y,outChannels:J,outHeight:ne,outWidth:Q,strideHeight:ie,strideWidth:ee}=h,le=V-1-h.padInfo.top,se=H-1-h.padInfo.left,ce=J/K;for(let de=0;de<W;++de)for(let fe=0;fe<K;++fe)for(let xe=0;xe<j;++xe){let be=xe-le,Se=Math.max(0,Math.ceil(be/ie)),_e=Math.min(ne,(V+be)/ie);for(let Me=0;Me<Y;++Me){let Ke=Me-se,Ve=Math.max(0,Math.ceil(Ke/ee)),it=Math.min(Q,(H+Ke)/ee),ut=0;for(let He=Se;He<_e;++He){let pt=He*ie-be;for(let dt=Ve;dt<it;++dt){let 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n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,a=t+e.TEXTURE0;if(a<e.TEXTURE0||a>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function yu(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function bu(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function Hv(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[yu(e),...bu(e)]),t}function o8(e,t=!1){let n=te().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,s)=>s>=e.length-2?k.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=k.squeezeShape(e).newShape);let a=k.sizeFromShape(e);if(e.length<=1&&a<=n)return[1,a];if(e.length===2&&e[0]<=n&&e[1]<=n)return 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Ae=te();Ae.registerFlag("HAS_WEBGL",()=>Ae.getNumber("WEBGL_VERSION")>0);Ae.registerFlag("WEBGL_VERSION",()=>aS(2)?2:aS(1)?1:0);Ae.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ae.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ae.get("WEBGL_VERSION")===2);Ae.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ae.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ae.registerFlag("WEBGL_PACK",()=>Ae.getBool("HAS_WEBGL"));Ae.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_CLIP",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ae.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_PACK_REDUCE",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_CONV_IM2COL",()=>Ae.getBool("WEBGL_PACK"));Ae.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>l8(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>u8(Ae.getNumber("WEBGL_VERSION")));Ae.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
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)
`,l="",c=`
#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",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
#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));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
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:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ji(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function Xv(e){let t=k.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}var rS=`
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;
}
`,f8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Tp.DENSE;let t=Cp(e),n=fn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Ji(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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);
}
${n.output} = result;
}
`}},g8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Tp.DENSE;let t=Cp(e),n=fn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Ji(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[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));
}
${n.output} = result;
}
`}},y8=class{constructor(e){this.variableNames=["A"],this.outTexUsage=aa.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
${rS}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},b8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=aa.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
${rS}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},x8=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=fn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${Xv(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${s};
int c = imod(flatIndex, ${s});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
vec4 values = ${a.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];
}
${a.output} = vec4(${i}, 0., 0., 0.);
}
`}},v8=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=fn(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
localCoords = coords;
if(localCoords[2] + ${c} < ${e[2]}) {
localCoords[2] += ${c};
if(localCoords[1] + ${l} < ${e[1]}) {
localCoords[1] += ${l};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
values = ${a.texture2D}(A, uv);
if(offset == 0) {
result[${u}] = values[0];
} else if(offset == 1) {
result[${u}] = values[1];
} else if(offset == 2) {
result[${u}] = values[2];
} else {
result[${u}] = values[3];
}
}
}
`}this.userCode=`
${Xv(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${a.output} = ${o};
}
`}};function w8(e){let t=fn(),n=`${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 Gq(e,n)}function k8(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 Yq(e,t)}function I8(e){let t=new Uint16Array([0,1,2,2,1,3]);return Jq(e,t)}function Ep(e,t,n,a,r,s){Qq(t,n);let i=Zq(e),o=e.TEXTURE_2D;return Te(e,()=>e.bindTexture(o,i)),Te(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Te(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Te(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Te(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function sS(e){return e.internalFormatFloat}function N8(e,t,n,a){let[r,s]=Sp(t,n);return Ep(e,r,s,sS(a),a.textureFormatFloat,e.FLOAT)}function iS(e){return e.internalFormatHalfFloat}function T8(e,t,n,a){let[r,s]=Sp(t,n);return Ep(e,r,s,iS(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function oS(e){return e.downloadTextureFormat}function S8(e,t,n,a){let[r,s]=Sp(t,n);return Ep(e,r,s,oS(a),e.RGBA,e.UNSIGNED_BYTE)}function lS(e){return e.internalFormatPackedFloat}function C8(e,t,n,a){let[r,s]=gu(t,n);return Ep(e,r,s,lS(a),e.RGBA,e.FLOAT)}function uS(e){return e.internalFormatPackedHalfFloat}function _8(e,t,n,a){let[r,s]=gu(t,n);return Ep(e,r,s,uS(a),e.RGBA,a.textureTypeHalfFloat)}function E8(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Te(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),tS(e,t,"clipSpacePos",n,3,s,a)&&tS(e,t,"uv",n,2,s,r)}function A8(e,t,n,a,r,s){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(r),Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function F8(e,t,n){Te(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Te(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Te(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function $8(e,t,n,a){let r=e.createBuffer();Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Te(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Te(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function D8(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function M8(e,t,n,a){let[r,s]=Sp(t,n),i=4,o=new Uint8Array(Oq(t*n,i));return Te(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function R8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(Lq(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function P8(e,t,n){let a=new Float32Array(t*n*4);return Te(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var L8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=te().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Mq(t,e)):this.gl=or(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(te().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Qm(this.gl,r),wa(this.gl,s))this.textureHalfFloatExtension=Qm(this.gl,s);else if(te().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),wa(this.gl,a))this.colorBufferHalfFloatExtension=Qm(this.gl,a);else if(te().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",wa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(wa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=k8(this.gl),this.indexBuffer=I8(this.gl),this.framebuffer=e8(this.gl),this.textureConfig=Vv(this.gl,this.textureHalfFloatExtension)}get debug(){return te().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. 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this.throwIfDisposed(),_8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),C8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(nS(this.gl,this.framebuffer),this.outputTexture=null),Te(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>M8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return R8(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return D8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=$8(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(te().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>P8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=jq(t,e),a=w8(t),r=Kq(t);return Te(t,()=>t.attachShader(r,a)),Te(t,()=>t.attachShader(r,n)),Xq(t,r),this.debug&&Uv(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=E8(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Te(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Uv(this.gl,this.program),Te(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?a8(this.gl,e,t):r8(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Te(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(),s8(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=gu(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Uv(this.gl,this.program),ef(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Te(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Te(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Qm(this.gl,te().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(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(te().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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a=this.gl;Gv(a,e,this.framebuffer),this.debug&&ef(a),this.outputTexture=e,Te(a,()=>a.viewport(0,0,t,n)),Te(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Te(this.gl,()=>this.gl.scissor(e,t,n,a))}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 O8(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:cS}=E;function q8(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
`),i=e.map(h=>z8(h,t,a)).join(`
`),o=t.texShape,l=fn(),c=V8(l),u,p,d=H8(l);return t.isPacked?(u=B8(t.logicalShape,o),p=G8(l)):(u=W8(t.logicalShape,o),p=U8(l)),a&&(d+=j8),[d,c,p,s,u,i,n].join(`
`)}function xu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return K8(e);case 1:return X8(e);case 2:return Y8(e);case 3:return J8(e);case 4:return Z8(e);case 5:return Q8(e);case 6:return eK(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function pS(e){switch(e.shapeInfo.logicalShape.length){case 0:return tK(e);case 1:return nK(e);case 2:return aK(e);case 3:return rK(e);default:return sK(e)}}function z8(e,t,n=!1){let a="";n?a+=pS(e):a+=xu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=iK(e,t):a+=oK(e,t)),a}function B8(e,t){switch(e.length){case 0:return dS();case 1:return lK(e,t);case 2:return pK(e,t);case 3:return uK(e,t);default:return cK(e,t)}}function W8(e,t){switch(e.length){case 0:return dS();case 1:return dK(e,t);case 2:return yK(e,t);case 3:return hK(e,t);case 4:return mK(e,t);case 5:return fK(e,t);case 6:return gK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function V8(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function U8(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function G8(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function H8(e){return`${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);
}
${bK}
${xK}
${vK}
`}var bK=`
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);
}
`,xK=`
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);
}
`,vK=`
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);
}
`,j8=`
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 dS(){return`
int getOutputCoords() {
return 0;
}
`}function lK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function dK(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function uK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec3(b, r, c);
}
`}function hK(e,t){let n=Ji(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function cK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec${e.length}(${o});
}
`}function mK(e,t){let n=Ji(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function fK(e,t){let n=Ji(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function gK(e,t){let n=Ji(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function pK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let a=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec2(r, c);
}
`}function yK(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Zi(e){return`offset${e}`}function tK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=fn();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function K8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=Zi(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function nK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=fn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${r[0]}, ${r[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function X8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${vu(e)}
}
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=Zi(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
return sampleTexture(${t}, uv);
}
`:r===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function aK(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=fn();if(r!=null&&k.arraysEqual(t,r))return`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],c=Math.ceil(t[1]/2);return`
vec4 ${a}(int row, int col) {
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function Y8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],d=r[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=wu(e,o),d=["row","col"];return`
${xu(p)}
float ${a}(int row, int col) {
return ${a}(${ku(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${vu(e)}
}
`;let l=r[0],c=r[1],u=Zi(n);return c===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${u};
vec2 uv = uvFromFlat(${l}, ${c}, index);
return sampleTexture(${n}, uv);
}
`}function rK(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),d=[1,2],h=wu(e,p),m=["b","row","col"];return`
${pS(h)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${ku(m,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=fn();return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${c}, ${l}, b, row, col);
return ${u.texture2D}(${n}, uv);
}
`}function J8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),l=i;if(l.length<t.length){let m=wu(e,l),f=["row","col","depth"];return`
${xu(m)}
float ${a}(int row, int col, int depth) {
return ${a}(${ku(f,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${r}, ${s}, 1)));
${vu(e)}
}
`;let c=e.shapeInfo.texShape,u=c[0],p=c[1],d=e.shapeInfo.flatOffset;if(p===r&&d==null)return`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;if(p===s&&d==null)return`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${u}.0);
return sampleTexture(${n}, uv);
}
`;let h=Zi(n);return`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r} + col * ${s} + depth + ${h};
vec2 uv = uvFromFlat(${u}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function sK(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),p="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,u*=t[n-m-1],d=`b${m} * ${u} + `+d;let h=fn();return`
vec4 ${r}(${p}) {
int index = ${d};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${h.texture2D}(${a}, uv);
}
`}function Z8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:l}=k.squeezeShape(t);if(o.length<t.length){let m=wu(e,o),f=["row","col","depth","depth2"];return`
${xu(m)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${ku(f,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${r}, 1)));
${vu(e)}
}
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,p=u[0],d=u[1];if(d===i&&c==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(d===r&&c==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let h=Zi(n);return`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${r} + depth2;
vec2 uv = uvFromFlat(${p}, ${d}, index + ${h});
return sampleTexture(${n}, uv);
}
`}function Q8(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=k.squeezeShape(t);if(l.length<t.length){let f=wu(e,l),g=["row","col","depth","depth2","depth3"];return`
${xu(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${ku(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${vu(e)}
}
`;let u=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&u==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${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, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let m=Zi(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${r} + depth3 + ${m};
vec2 uv = uvFromFlat(${d}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function eK(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let g=wu(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${xu(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${ku(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${vu(e)}
}
`;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],m=d[1];if(m===u&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(m===i&&p==null)return`
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${m}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=Zi(n);return`
float ${a}(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 * ${u} + col * ${c} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
vec2 uv = uvFromFlat(${h}, ${m}, index);
return sampleTexture(${n}, uv);
}
`}function vu(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function iK(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=cS(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${p[g+c]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((g,y)=>`coords.${p[y+c]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!f)i===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(g)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${a}(${d});
${h}
}
`}function oK(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=gt(l),u=cS(e.shapeInfo.logicalShape,t.logicalShape),p=l-o,d,h=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(f=>`coords.${h[f+p]} = 0;`).join(`
`);let m="";return l<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,g)=>`coords.${h[g+p]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${d}
return get${a}(${m});
}
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${Xv(e)}
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vec4 result = vec4(0.);
ivec3 thisRC;
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int cols = ${e[2]};
${n}
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float unaryOperation(float x) {
${t}
}
void main() {
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float y = unaryOperation(x);
setOutput(y);
}
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vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
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vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
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vec4 unaryOperation(vec4 x) {
${t}
}
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vec4 y = unaryOperation(x);
setOutput(y);
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vec4 packedInput = getA(${r});
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}`:f=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",v="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${b};
int batchB = ${v};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},ES={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},AS=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.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));
}
`}},FS="return a * b;";function $S(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=E.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new AS(ES.REAL,a.shape,r.shape),u=new AS(ES.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ys({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=zK(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ap(FS,a.shape,r.shape):i=new Nu(FS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var MX={kernelName:ri,backendName:"webgl",kernelFunc:$S};function RX(e,t,n){let a=[yu(e.shape),...bu(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[yu(t),...bu(t)],i=new yS(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function ve(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!nf(r.shape,l)&&!(u.texture!==null&&nf(u.shape,l))?RX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var PX={kernelName:Nl,backendName:"webgl",kernelFunc:ve},DS=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${k.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},OX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,p=`
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 = ${o}(values, minMaxValue);
}
`,d="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${u===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${u===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${l});
}
`}};function LX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Qi(e,t,n,a){let r=LX(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:l,outSize:c}=r[i],u,p;n==="mean"?u=i===0?new DS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new DS({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new OX({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),p=s,s=a.runWebGLProgram(u,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var BX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=gt(this.rank),r=zX(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function zX(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"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var WX=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=gt(this.rank),r=gS("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function of(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WX(e.shape,t):new BX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function VX(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=E.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=of(e,l,a),o=E.getInnerMostAxes(o.length,s)),E.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=E.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=E.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=ve({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=bh(e.dtype),b=Qi(g,y,"sum",a),v=ve({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),v}function Zv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return VX(r,s,i,n)}var UX={kernelName:bi,backendName:"webgl",kernelFunc:Zv};function Cn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Yv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=of(r,s,i);return c}var GX={kernelName:Ii,backendName:"webgl",kernelFunc:Cn},MS=1e3;function lf({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),v=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&v,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let x=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],C=ve({inputs:{x:e},backend:r,attrs:{shape:N}}),$=ve({inputs:{x:t},backend:r,attrs:{shape:T}}),F=[C,$],O=Math.max(y,b),W=n?C.shape[1]:C.shape[2],V=s!=null,H=i!=null,K=l==="leakyrelu",j=l!=null?sf(l,!0):null,Y=V||H||K||j!=null,J;if((h===1||m===1)&&W>MS&&Y===!1){let Q=C,ie=$;n&&(Q=Cn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),F.push(Q)),a&&(ie=Cn({inputs:{x:$},backend:r,attrs:{perm:[0,2,1]}}),F.push(ie));let ee=m!==1,le=m===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:r,attrs:{shape:[O,W,1]}}),F.push(se));let ce=m===1?2:1,de=ie;le&&(de=ve({inputs:{x:ie},backend:r,attrs:{shape:[O,1,W]}}),F.push(de));let fe=$S({inputs:{a:se,b:de},backend:r});J=Zv({inputs:{x:fe},backend:r,attrs:{axis:ce,keepDims:!0}}),F.push(fe)}else{let Q=ha(e.dtype,t.dtype),ie=new _S(N,T,[O,h,m],n,a,V,j,H,K),ee=[C,$];if(s!=null&&ee.push(s),H&&ee.push(i),K){let le=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));ee.push(le),F.push(le)}J=r.runWebGLProgram(ie,ee,Q)}let ne=ve({inputs:{x:J},backend:r,attrs:{shape:x}});F.push(J);for(let Q of F)r.disposeIntermediateTensorInfo(Q);return ne}function HX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return lf({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var jX={kernelName:Ni,backendName:"webgl",kernelFunc:HX},RS="return abs(x);";function qX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=fS(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Iu(a.shape,RS):r=new gs(a.shape,RS),n.runWebGLProgram(r,[a],a.dtype)}var KX={kernelName:Lo,backendName:"webgl",kernelFunc:qX},XX=La+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,YX=Je({opSnippet:XX}),JX={kernelName:zo,backendName:"webgl",kernelFunc:YX},ZX=La+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,QX=Je({opSnippet:ZX}),eY={kernelName:Bo,backendName:"webgl",kernelFunc:QX},PS="return a + b;",tY=sn({opSnippet:PS,packedOpSnippet:PS,supportsComplex:!0,cpuKernelImpl:NK}),nY={kernelName:Hr,backendName:"webgl",kernelFunc:tY},aY=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${a};
setOutput(result);
}
`}},rY=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${a};
setOutput(result);
}
`}};function uf(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Wn({inputs:{x:a[0]},backend:n});if(a.length>te().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=uf({inputs:a.slice(0,o),backend:n}),c=uf({inputs:a.slice(o),backend:n});return uf({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>ha(o,l)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new rY(a[0].shape,s):new aY(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var sY={kernelName:Ms,backendName:"webgl",kernelFunc:uf};function iY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("all",c,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Qi(f,f.dtype,"all",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var oY={kernelName:Od,backendName:"webgl",kernelFunc:iY};function lY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,o)),E.assertAxesAreInnerMostDims("any",c,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Qi(f,f.dtype,"any",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var uY={kernelName:Ld,backendName:"webgl",kernelFunc:lY},cY=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=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 * ${a};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${a}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},pY=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=gt(o),c=gn("coords",o),u,p;if(s===1){p=o+1;let C=gt(p);u=`
${C} sourceLocR = ${C}(${c.join()}, 0);
++${c[o-1]};
${C} sourceLocG = ${C}(${c.join()}, 0);
++${c[o-2]};
${C} sourceLocA = ${C}(${c.join()}, 0);
--${c[o-1]};
${C} sourceLocB = ${C}(${c.join()}, 0);
--${c[o-2]};`}else p=o,u=`
${l} sourceLocR = coords;
++${c[o-1]};
${l} sourceLocG = coords;
++${c[o-2]};
${l} sourceLocA = coords;
--${c[o-1]};
${l} sourceLocB = coords;
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],m=d.map(C=>"int "+C),f=gn("sourceLocR",p-1).concat("inIdx.r"),g=gn("sourceLocG",p-1).concat("inIdx.g"),y=gn("sourceLocB",p-1).concat("inIdx.b"),b=gn("sourceLocA",p-1).concat("inIdx.a"),v=n==="max"?"greaterThan":"lessThan",x=a?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${b.join()})));`,N=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,T=a?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${T}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${N};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${x}
vec4 candidate = ${N};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${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 OS(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=E.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new cY(o,n,a==null),c=[t];a!=null&&c.push(a);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let p=OS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function LS(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=E.computeOptimalWindowSize(s),o=new pY(r,i,n,a==null),l=a==null?[t]:[t,a],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=LS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function zS(e,t,n,a){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!te().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=E.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=ve({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=OS(e,c,a);s.push(u);let p=ve({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return LS(e,t,a)}function dY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Cn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=zS(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var hY={kernelName:Rs,backendName:"webgl",kernelFunc:dY};function mY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=E.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=Cn({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=E.getInnerMostAxes(i.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=zS(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var fY={kernelName:fc,backendName:"webgl",kernelFunc:mY},gY=La+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,yY=Je({opSnippet:gY}),bY={kernelName:Wo,backendName:"webgl",kernelFunc:yY},xY=La+"return log(x + sqrt(x * x + 1.0));",vY=Je({opSnippet:xY}),wY={kernelName:Vo,backendName:"webgl",kernelFunc:vY},kY=La+`
return atan(x);
`,IY=Je({opSnippet:kY}),NY={kernelName:Uo,backendName:"webgl",kernelFunc:IY},TY=$X+`
return atan(a, b);
`,SY=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+DX+`
return result;
`,CY=sn({opSnippet:TY,packedOpSnippet:SY}),_Y={kernelName:Ho,backendName:"webgl",kernelFunc:CY},EY=La+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,AY=Je({opSnippet:EY}),FY={kernelName:Go,backendName:"webgl",kernelFunc:AY},Fp=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${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 < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${C} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${h});
const float initializationValue = ${y};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${T}
}
int xC = xCCorner + ${x};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${T}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${T}
} else if (${N===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${T}
}
}
setOutput(${v});
}
`}},Qv=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let b=t==="avg",v="0.0";if(b||(v="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
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 < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${p}) {
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 ${F} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?`(((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} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let x="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,C=s%4,$=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${x}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
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 < ${d};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${T}; wC += 4) {
int xC = xCCorner + wC * ${p};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
);
${$}
}
int xC = xCCorner + ${T};
if (${C===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${$}
} else if (${C===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${$}
} else if (${C===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
initializationValue
);
${$}
}
}
setOutput(${N});
}
}
`}};function $Y(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;_p(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Fp(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var DY={kernelName:Ps,backendName:"webgl",kernelFunc:$Y};function MY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=E.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new Qv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var RY={kernelName:gc,backendName:"webgl",kernelFunc:MY},PY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${p});
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 < ${o};
wR += ${s}) {
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 < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},OY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${m}, ${f});
const float avgMultiplier = float(${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 < ${u};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${d};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function LY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,p,c,u),h=new OY(d);return n.runWebGLProgram(h,[r],i.dtype)}var zY={kernelName:Bd,backendName:"webgl",kernelFunc:LY};function BY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;_p([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=E.computePool2DInfo(i.shape,o,l,1,c),p=new PY(u);return n.runWebGLProgram(p,[r],i.dtype)}var WY={kernelName:zd,backendName:"webgl",kernelFunc:BY};function VY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return lf({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var UY={kernelName:Os,backendName:"webgl",kernelFunc:VY},GY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},HY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(E.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},jY=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[a,r,s],u=null;i!=null&&(u=i.shape,c.push(i));let p=null;o!=null&&(p=o.shape,c.push(o));let d=te().getBool("WEBGL_PACK_NORMALIZATION")?new HY(a.shape,r.shape,s.shape,u,p,l):new GY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},qY={kernelName:Ks,backendName:"webgl",kernelFunc:jY},XY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank),n=`uniform int start[${this.rank}];`,a=KY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${ew[o]} = start[${o}] + coords.${ew[o]};`);r=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${r}
setOutput(getSource(${a}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},ew=["x","y","z","w","u","v"];function KY(e){if(e===1)return"sourceLoc";if(e<=6)return ew.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var YY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=gt(this.rank),n=gn("coords",this.rank),a=gn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.y = ${s};
--${a[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${a[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${a[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function JY(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.complexParentRefCount=0,i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=dn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,l+1),s}function $p(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,l]=dn.parseSliceParams(r,s,i);if(dn.assertParamsValid(r,o,l),k.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=GK(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=dn.isSliceContinous(r.shape,o,l);if(c||!u){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new YY(l):new XY(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),JY(r,o,l,n)}var ZY={kernelName:_l,backendName:"webgl",kernelFunc:$p},QY=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((b,v)=>b*v),l=E.getReshaped(r.shape,s,o),c=E.getPermuted(l.length,s.length),u=E.getReshapedPermuted(r.shape,s,o),p=E.getSliceBeginCoords(i,s.length),d=E.getSliceSize(u,i,s.length),h=[],m=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Cn({inputs:{x:m},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:f},backend:n,attrs:{shape:u}}),y=$p({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(m),h.push(f),h.push(g),h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y},e7={kernelName:yc,backendName:"webgl",kernelFunc:QY};function t7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),l=n.readSync(s.dataId),c=mS(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var n7={kernelName:Wd,backendName:"webgl",kernelFunc:t7},a7="return float(a != b);",BS=sn({opSnippet:a7,dtype:"bool"}),r7={kernelName:gl,backendName:"webgl",kernelFunc:BS};function Dp(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var s7={kernelName:lh,backendName:"webgl",kernelFunc:Dp},i7="return float(int(x));";function o7(e,t){let n=new gs(e.shape,i7),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function tw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Wn({inputs:{x:r},backend:n});let i=xt(r.shape),o=tw({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ys({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Dp({inputs:{input:r},backend:n}),o=tw({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return o7(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=BS({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var l7={kernelName:Ls,backendName:"webgl",kernelFunc:tw},WS="return ceil(x);",u7=Je({opSnippet:WS,packedOpSnippet:WS,cpuKernelImpl:SK}),c7={kernelName:jo,backendName:"webgl",kernelFunc:u7},p7=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},d7=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function h7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;te().getBool("WEBGL_PACK_CLIP")?o=new d7(r.shape):o=new p7(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var m7={kernelName:jr,backendName:"webgl",kernelFunc:h7},f7=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 VS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function g7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new f7(a.shape),i=[VS(a,r.complexTensorInfos.real),VS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var y7={kernelName:bc,backendName:"webgl",kernelFunc:g7},b7=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},x7=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=gt(a),s=gn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let l=i[t],c=i.slice(-2),u=i.join(),p=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
if (${l} < ${o[m]} && ${l} >= ${o[m-1]}) {
return getChannel(
getT${m}(${cf(i,l,f)}),
vec2(${cf(c,l,f)}));
}`}let d=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${d}(${cf(i,l,h)}),
vec2(${cf(c,l,h)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${p}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[a-1]} = ${s[a-1]} + 1;
if (${s[a-1]} < ${n[a-1]}) {
result.g = getValue(${s});
}
${s[a-2]} = ${s[a-2]} + 1;
if (${s[a-2]} < ${n[a-2]}) {
result.a = getValue(${s});
}
${s[a-1]} = ${s[a-1]} - 1;
if (${s[a-2]} < ${n[a-2]} &&
${s[a-1]} < ${n[a-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function cf(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function pf(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var v7={kernelName:th,backendName:"webgl",kernelFunc:pf};function Tu(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Dp({inputs:{input:m},backend:n})),u=e.map(m=>pf({inputs:{input:m},backend:n})),p=Tu(c,t,n),d=Tu(u,t,n),h=ys({inputs:{real:p,imag:d},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),u.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(a==="string"){let{tensors2D:c,outShape:u}=US(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=CK(p,u,a,d),m=E.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>te().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Tu(e.slice(0,c),t,n),p=Tu(e.slice(c),t,n),d=Tu([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new x7(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=US(e,t,n),i=new b7(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=ve({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function US(e,t,n){let a=E.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ve({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function GS(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=E.computeOutShape(t.map(c=>c.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>k.sizeFromShape(c.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return E.assertParamsConsistent(l,s),Tu(o,s,n)}var w7={kernelName:qo,backendName:"webgl",kernelFunc:GS},HS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",g=f?1:2,y=f?2:3,b=f?3:1,v="",x="";n&&(a?v=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?v=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:v=`
float activation(float x) {
${n}
}
`,x="result = activation(result);");let N=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${v}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${b}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * 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 < ${p}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
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 (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${f}) {
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, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${f}) {
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;
${N}
${x}
setOutput(result);
}
`}},k7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${a});
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 < ${u}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${m===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, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===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);
}
`}},I7=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:p}=n,{left:d,top:h}=o,m=r*a,f=fn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,v="";for(let x=0;x<=1;x++)for(let N=0;N<=1;N++)v+=`
blockIndex = rc.y + ${N};
pos = rc.x + ${x};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${h};
d0 = offsetY + ${u} * (pos / ${m});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${c} * (int(mod(float(pos), ${m}.) / ${r}.));
if(d1 < ${t[b]} && d1 >= 0) {
ch = int(mod(float(pos), ${r}.));
if (${g}) {
innerDims = vec2(d1, ch);
result[${x*2+N}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${x*2+N}] = 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;
${v}
${f.output} = result;
}
`}};function jS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=a.texData.get(e.dataId),u=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,g,y=[],b=(p===1||d===1)&&u>MS,v=l[2]%2!=0&&!!c.isPacked;if(b||!te().getBool("WEBGL_LAZILY_UNPACK")||!te().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!v){let x=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=ve({inputs:{x:e},backend:a,attrs:{shape:[1,x,n.inChannels]}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=lf({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:C},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(C)}else{let x=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(nf(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let C=ve({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let $=lf({a:N,b:C,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),F=a.texData.get($.dataId);k.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,F.shape=n.outShape,g=Wn({inputs:{x:$},backend:a}),g.shape=n.outShape,y.push($)}for(let x of y)a.disposeIntermediateTensorInfo(x);return g}function qS({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:p,outHeight:d,dataFormat:h}=n,m=h==="channelsLast",f=l*c*u,g=d*p,y=[f,g],b=!0,v=!1,x=[],N=ve({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});x.push(N),x.push(T);let C=new I7(y,N.shape,n),$=a.runWebGLProgram(C,[N],"float32"),F=ve({inputs:{x:$},backend:a,attrs:{shape:[1,y[0],y[1]]}});x.push($),x.push(F);let O=r!=null,W=s!=null,V=o==="leakyrelu",H=o?sf(o,!0):null,K=new _S(F.shape,T.shape,[1,g,n.outChannels],b,v,O,H,W,V),j=[F,T];if(r&&j.push(r),W&&j.push(s),V){let Q=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));j.push(Q),x.push(Q)}let Y=a.runWebGLProgram(K,j,"float32"),J=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],ne=ve({inputs:{x:Y},backend:a,attrs:{shape:J}});x.push(Y);for(let Q of x)a.disposeIntermediateTensorInfo(Q);return ne}function N7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=a,p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,s.shape,i,c,o,u,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=jS({x:r,filter:s,convInfo:d,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=qS({x:r,filter:s,convInfo:d,backend:n});else{let f=new HS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ve({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var T7={kernelName:zs,backendName:"webgl",kernelFunc:N7},S7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
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);
}
`}},C7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.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) / ${r}.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 < ${e.outChannels}; d2++) {
if (${s}) {
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);
}
`}},_7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${a} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},E7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.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) / ${s}.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 < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${a} - 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 A7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=a,p=E.convertConv2DDataFormat(l),d=E.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new S7(d);return n.runWebGLProgram(h,[r,s],"float32")}var F7={kernelName:Ud,backendName:"webgl",kernelFunc:A7};function $7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=a,p=E.convertConv2DDataFormat(c),d=E.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new C7(d);return n.runWebGLProgram(h,[r,s],"float32")}var D7={kernelName:Bs,backendName:"webgl",kernelFunc:$7};function M7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=E.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new k7(c);return n.runWebGLProgram(u,[r,s],"float32")}var R7={kernelName:xc,backendName:"webgl",kernelFunc:M7};function P7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=E.computeConv3DInfo(r.shape,l,i,1,o),u=new _7(c);return n.runWebGLProgram(u,[r,s],"float32")}var O7={kernelName:Gd,backendName:"webgl",kernelFunc:P7};function L7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=E.computeConv3DInfo(l,s.shape,o,1,i),u=new E7(c);return n.runWebGLProgram(u,[r,s],"float32")}var z7={kernelName:Hd,backendName:"webgl",kernelFunc:L7},B7=CS+`
return cos(x);
`,W7=Je({opSnippet:B7}),V7={kernelName:Ws,backendName:"webgl",kernelFunc:W7},U7=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,G7=Je({opSnippet:U7}),H7={kernelName:Ko,backendName:"webgl",kernelFunc:G7},j7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,g,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${b});
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 >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${v};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${x};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},q7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new j7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},K7={kernelName:Xo,backendName:"webgl",kernelFunc:q7},YS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${KS(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${gt(a)} coords = getOutputCoords();
int end = ${XS(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${XS(a,"coords")} = idx;
val += getX(${KS(a,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function KS(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 ${e} is not yet supported`)}function XS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function X7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=E.getAxesPermutation([s],l),u=r;c!=null&&(u=Cn({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=E.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=r.shape[p],h=Wn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new YS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new YS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=E.getUndoAxesPermutation(c),f=Cn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var Y7={kernelName:Vs,backendName:"webgl",kernelFunc:X7};function J7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=mS(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=TK(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Z7={kernelName:jd,backendName:"webgl",kernelFunc:J7},Q7=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 eJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=new Q7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var tJ={kernelName:Yo,backendName:"webgl",kernelFunc:eJ},JS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,g="",y="";n&&(a?g=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?g=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:g=`
float activation(float x) {
${n}
}
`,y="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${g}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${f};
int q = d2 - d1 * ${f};
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 < ${h}; wR++) {
int xR = xRCorner + wR * ${p};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${m}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${b}
${y}
setOutput(result);
}
`}},ZS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`
vec4 xTexelR${x}C${N*2} = vec4(0.);
vec4 wR${x}C${N} = vec4(0.);
vec4 xR${x}C${N} = vec4(0.);`;for(let x=0;x<h;x++)for(let N=0;N<f;N++){let T=N*2;if(g+=`
xR = xRCorner + ${x*p};
xC = xCCorner + ${T*d};
`,u===1){if(T<m&&(l%2==1?g+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${x}C${T}.zw = vec2(0.);
}
} else {
xTexelR${x}C${T} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${x}C${T} = vec4(previous.zw, xTexelR${x}C${T}.xy);
} else {
xR${x}C${T} = vec4(0, 0, xTexelR${x}C${T}.xy);
}
`:g+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
xR${x}C${T} = xTexelR${x}C${T};
`,T+1<m)){let C=l%2==0?k.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(g+=`
xCOffset = xC + ${l%2} + ${C};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(g+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
`),g+=`
xR${x}C${T+1} = vec4(
xTexelR${x}C${T}.zw, xTexelR${x}C${T+2}.xy);
`):g+=`
xCOffset = xC + ${C};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xCOffset, d1);
}
xR${x}C${T+1} = xTexelR${x}C${T+2};
`}}else T<m&&(g+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(g+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${x}C${T+2} = vec4(0.);
}
xR${x}C${T} = vec4(
xTexelR${x}C${T}.zw, xTexelR${x}C${T+2}.zw);
`,T+1<m&&(g+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${x}C${T+1} = vec4(xTexelR${x}C${T+2}.xy, final.xy);
`)):(g+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${x}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${x}C${T} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${x}C${T+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${x}C${T+2} = vec4(0.);
}
xR${x}C${T} = vec4(
xTexelR${x}C${T}.xy, xTexelR${x}C${T+2}.xy);
`,T+1<m&&(g+=`
xR${x}C${T+1} = vec4(
xTexelR${x}C${T}.zw, xTexelR${x}C${T+2}.zw);
`)),g+="}");T<m&&(g+=`
vec4 wTexelR${x}C${T} = getW(${x}, ${T}, d1, q);
wR${x}C${T} = vec4(wTexelR${x}C${T}.xz, wTexelR${x}C${T}.xz);
`,T+1<m&&(g+=`
vec4 wTexelR${x}C${T+1} = getW(${x}, ${T+1}, d1, q);
wR${x}C${T+1} =
vec4(wTexelR${x}C${T+1}.xz, wTexelR${x}C${T+1}.xz);`))}for(let x=0;x<h;x++)for(let N=0;N<m;N++)g+=`dotProd += xR${x}C${N} * wR${x}C${N};`;let y="",b="";n&&(a?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`vec4 activation(vec4 x) {
${n}
}`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${c}, ${u});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${g}
vec4 result = dotProd;
${v}
${b}
setOutput(result);
}
`}};function nJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=a,u=l;u==null&&(u=[1,1]),k.assert(E.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=E.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new ZS(p):d=new JS(p),n.runWebGLProgram(d,[r,s],"float32")}var aJ={kernelName:Us,backendName:"webgl",kernelFunc:nJ},rJ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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 * ${s} + 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} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},sJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.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) / ${r}.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 < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function iJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=E.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new rJ(p);return n.runWebGLProgram(d,[r,s],"float32")}var oJ={kernelName:qd,backendName:"webgl",kernelFunc:iJ};function lJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=E.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new sJ(p);return n.runWebGLProgram(d,[r,s],"float32")}var uJ={kernelName:Kd,backendName:"webgl",kernelFunc:lJ},cJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function pJ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=ve({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new cJ(s),l=n.runWebGLProgram(o,[i],i.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var dJ={kernelName:Xd,backendName:"webgl",kernelFunc:pJ},hJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${u}, ${p});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${c};
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 mJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=E.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new hJ(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var fJ={kernelName:vc,backendName:"webgl",kernelFunc:mJ},gJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",yJ=`
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;
`,bJ=Je({opSnippet:gJ,packedOpSnippet:yJ}),xJ={kernelName:Jo,backendName:"webgl",kernelFunc:bJ},vJ="return (b >= 1.0) ? a : a * (b + 1.0);",wJ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,kJ=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ap(wJ,a.shape,r.shape):new Nu(vJ,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},IJ={kernelName:Zd,backendName:"webgl",kernelFunc:kJ},NJ=`
return vec4(equal(a, b));
`,TJ="return float(a == b);",SJ=sn({opSnippet:TJ,packedOpSnippet:NJ,dtype:"bool"}),CJ={kernelName:Qo,backendName:"webgl",kernelFunc:SJ},_J=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${E.ERF_P};
float a1 = ${E.ERF_A1};
float a2 = ${E.ERF_A2};
float a3 = ${E.ERF_A3};
float a4 = ${E.ERF_A4};
float a5 = ${E.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,EJ=Je({opSnippet:_J}),AJ={kernelName:Zo,backendName:"webgl",kernelFunc:EJ},QS="return exp(x);",e2=Je({opSnippet:QS,packedOpSnippet:QS,cpuKernelImpl:_K}),FJ={kernelName:Hs,backendName:"webgl",kernelFunc:e2};function nw(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),ve({inputs:{x:s},backend:a,attrs:{shape:o}})}var $J={kernelName:el,backendName:"webgl",kernelFunc:nw},t2="return exp(x) - 1.0;",DJ=Je({opSnippet:t2,packedOpSnippet:t2,cpuKernelImpl:EK}),MJ={kernelName:tl,backendName:"webgl",kernelFunc:DJ},n2=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${a});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${a}; 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) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function a2(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ve({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new n2("real",l,t),u=new n2("imag",l,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:l},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=ys({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let f=ve({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function RJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return a2(a,!1,n)}var PJ={kernelName:Qd,backendName:"webgl",kernelFunc:RJ},OJ=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function aw(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new OJ(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var LJ={kernelName:wc,backendName:"webgl",kernelFunc:aw},zJ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},BJ={kernelName:nl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new zJ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},r2="return floor(x);",WJ=Je({opSnippet:r2,packedOpSnippet:r2,cpuKernelImpl:AK}),VJ={kernelName:js,backendName:"webgl",kernelFunc:WJ},UJ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,GJ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,HJ=sn({opSnippet:UJ,packedOpSnippet:GJ,dtype:"int32"}),jJ={kernelName:qs,backendName:"webgl",kernelFunc:HJ},qJ=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},KJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}.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);
}
}
${t.output} = result;
}
`}},YJ={kernelName:hh,backendName:"webgl",kernelFunc:XJ},Su;function XJ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(Su==null&&(Su=document.createElement("canvas").getContext("2d")),Su.canvas.width=c,Su.canvas.height=u,Su.drawImage(r,0,0,c,u),r=Su.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=aa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=te().getBool("WEBGL_PACK")?new KJ(d):new qJ(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function JJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=jS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=qS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let x=i!=null,N=o!=null,T=h==="leakyrelu",C=h?sf(h,!1):null,$=new HS(g,x,C,N,T),F=[r,s];if(i&&F.push(i),o&&F.push(o),T){let O=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));F.push(O),b.push(O)}y=n.runWebGLProgram($,F,"float32")}let v=ve({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ZJ={kernelName:Ti,backendName:"webgl",kernelFunc:JJ};function QJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=a,m=[],f=u;f==null&&(f=[1,1]),k.assert(E.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=E.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=te().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?sf(d,y):null,v=[r,s],x=i!=null,N=o!=null,T=d==="leakyrelu";if(x&&v.push(i),N&&v.push(o),T){let F=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));v.push(F),m.push(F)}let C;y?C=new ZS(g,x,b,N,T):C=new JS(g,x,b,N,T);let $=n.runWebGLProgram(C,v,"float32");return m.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var e9={kernelName:Si,backendName:"webgl",kernelFunc:QJ},t9=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=gt(t.length),r=gt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${a} strides = ${a}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function n9(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=E.prepareAndValidate(a,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=ve({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new t9(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=ve({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var a9={kernelName:rl,backendName:"webgl",kernelFunc:n9},s9=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=gt(this.rank),a=r9(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function r9(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function i9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=ve({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),v=n.bufferSync(d),x=FK(v,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,x.dtype,x.values)}let f=new s9(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=ve({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var o9={kernelName:al,backendName:"webgl",kernelFunc:i9},l9="return float(a > b);",u9=`
return vec4(greaterThan(a, b));
`,c9=sn({opSnippet:l9,packedOpSnippet:u9,cpuKernelImpl:$K,dtype:"bool"}),p9={kernelName:sl,backendName:"webgl",kernelFunc:c9},d9="return float(a >= b);",h9=`
return vec4(greaterThanEqual(a, b));
`,m9=sn({opSnippet:d9,packedOpSnippet:h9,dtype:"bool"}),f9={kernelName:Xs,backendName:"webgl",kernelFunc:m9};function g9(e){let{inputs:t,backend:n}=e,{input:a}=t;return a2(a,!0,n)}var y9={kernelName:eh,backendName:"webgl",kernelFunc:g9},b9="return float(!isnan(x) && !isinf(x));",x9=Je({opSnippet:b9,dtype:"bool"}),v9={kernelName:ol,backendName:"webgl",kernelFunc:x9},w9="return float(isinf(x));",k9=Je({opSnippet:w9,dtype:"bool"}),I9={kernelName:ll,backendName:"webgl",kernelFunc:k9},N9="return float(isnan(x));",T9=Je({opSnippet:N9,dtype:"bool"}),S9={kernelName:ul,backendName:"webgl",kernelFunc:T9},C9="return float(a < b);",_9=`
return vec4(lessThan(a, b));
`,E9=sn({opSnippet:C9,packedOpSnippet:_9,cpuKernelImpl:DK,dtype:"bool"}),A9={kernelName:cl,backendName:"webgl",kernelFunc:E9},F9="return float(a <= b);",$9=`
return vec4(lessThanEqual(a, b));
`,D9=sn({opSnippet:F9,packedOpSnippet:$9,dtype:"bool"}),M9={kernelName:pl,backendName:"webgl",kernelFunc:D9};function R9(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=MK(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var P9={kernelName:nh,backendName:"webgl",kernelFunc:R9},O9=`if (x < 0.0) return NAN;
return log(x);`,L9=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,z9=Je({opSnippet:O9,packedOpSnippet:L9,cpuKernelImpl:RK}),B9={kernelName:Js,backendName:"webgl",kernelFunc:z9},W9="return log(1.0 + x);",V9=Je({opSnippet:W9}),U9={kernelName:dl,backendName:"webgl",kernelFunc:V9},G9="return float(a >= 1.0 && b >= 1.0);",H9=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,j9=sn({opSnippet:G9,packedOpSnippet:H9,dtype:"bool"}),q9={kernelName:hl,backendName:"webgl",kernelFunc:j9},K9="return float(!(x >= 1.0));",X9=Je({opSnippet:K9}),Y9={kernelName:kc,backendName:"webgl",kernelFunc:X9},J9="return float(a >= 1.0 || b >= 1.0);",Z9=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Q9=sn({opSnippet:J9,packedOpSnippet:Z9,dtype:"bool"}),eZ={kernelName:Ic,backendName:"webgl",kernelFunc:Q9},tZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},nZ=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${l})`:r===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${r}));`,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 - ${s};
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 = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},aZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=te().getBool("WEBGL_PACK_NORMALIZATION")?new nZ(r.shape,s,i,o,l):new tZ(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},rZ={kernelName:Nc,backendName:"webgl",kernelFunc:aZ},sZ=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${a}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${a})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},iZ=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=a,p=new sZ(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},oZ={kernelName:ah,backendName:"webgl",kernelFunc:iZ};function lZ(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Qi(i,e.dtype,"max",a),l=ve({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function s2(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,v=new Array(o);for(let T=0;T<v.length;T++)v[T]=r.shape[u[T]];let x=Yv(b,r.shape,r.dtype,u,v);h=n.makeTensorInfo(v,r.dtype);let N=n.texData.get(h.dataId);N.values=x}else h=of(r,u,n);c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("max",c,o);let[m,f]=E.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=E.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,v=PK(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let x=n.texData.get(y.dataId);x.values=v}else y=lZ(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var uZ={kernelName:Zs,backendName:"webgl",kernelFunc:s2},cZ=kS+`
return max(a, b);
`,pZ=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+rf+`
return result;
`,dZ=sn({opSnippet:cZ,packedOpSnippet:pZ,cpuKernelImpl:OK}),hZ={kernelName:Qs,backendName:"webgl",kernelFunc:dZ};function mZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;_p(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(E.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&k.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:r},backend:n});let p=new Fp(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var fZ={kernelName:ei,backendName:"webgl",kernelFunc:mZ};function gZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=a,u=[1,1,1],p=E.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new Qv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var yZ={kernelName:Tc,backendName:"webgl",kernelFunc:gZ},bZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,l=r*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
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 < ${r};
wR += ${a}) {
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 < ${s}; 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 = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},xZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${p}, ${d});
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 < ${o};
wD += ${r}) {
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 < ${l};
wR += ${s}) {
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 < ${c};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.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 * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function vZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=a,p=[1,1,1],d=E.computePool3DInfo(i.shape,o,l,p,c,u),h=new Qv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new xZ(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var wZ={kernelName:sh,backendName:"webgl",kernelFunc:vZ};function kZ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;_p([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=E.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new Fp(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new bZ(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var IZ={kernelName:rh,backendName:"webgl",kernelFunc:kZ};function NZ(e,t,n,a){let r=new Fp(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Fp(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var TZ={kernelName:ih,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let c=[1,1];k.assert(E.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=E.computePool2DInfo(a.shape,r,s,c,i),[p,d]=NZ(a,o,u,l);return[p,d]}};function SZ(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Qi(i,"float32","mean",a),l=ve({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var CZ={kernelName:ti,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,l=k.parseAxisParam(s,a.shape),c=l,u=E.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let v=i.texData.get(m.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[u[C]];let N=Yv(v,a.shape,a.dtype,u,x);m=i.makeTensorInfo(x,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=of(a,u,i);h.push(m),c=E.getInnerMostAxes(c.length,o)}E.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=E.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=E.expandShapeToKeepDim(f,l));let b=SZ(m,g,y,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return b}};function _Z(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=k.parseAxisParam(s,r.shape),c=l,u=E.getAxesPermutation(c,o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",c,o);let[d,h]=E.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=Qi(f,f.dtype,"min",n),y;if(i){let b=E.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var EZ={kernelName:ni,backendName:"webgl",kernelFunc:_Z},AZ=kS+`
return min(a, b);
`,FZ=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+rf+`
return result;
`,$Z=sn({opSnippet:AZ,packedOpSnippet:FZ,cpuKernelImpl:LK}),DZ={kernelName:ai,backendName:"webgl",kernelFunc:$Z},MZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let a=e.length,r=gt(a),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),l=n==="reflect"?0:1;if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${a}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},RZ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=gt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=gn("rc",a),l=gn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(a===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;d=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${u});
${o[a-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},PZ=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new RZ(a.shape,r,s):new MZ(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},OZ={kernelName:Sc,backendName:"webgl",kernelFunc:PZ},LZ=`if (b == 0.0) return NAN;
return mod(a, b);`,zZ=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+rf+`
return result;
`,BZ=sn({opSnippet:LZ,packedOpSnippet:zZ}),WZ={kernelName:ml,backendName:"webgl",kernelFunc:BZ},VZ=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},UZ=`
if (a == b) {
return 1.0;
};
return a / b;`,GZ=`
// 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;
`,i2=sn({opSnippet:UZ,packedOpSnippet:GZ,checkOutOfBounds:!0}),HZ={kernelName:Gs,backendName:"webgl",kernelFunc:i2},o2="return a - b;",l2=sn({opSnippet:o2,packedOpSnippet:o2,supportsComplex:!0,cpuKernelImpl:jK}),jZ={kernelName:wi,backendName:"webgl",kernelFunc:l2};function u2(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=s2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=E.expandShapeToKeepDim(o.shape,i),c=ve({inputs:{x:o},backend:n,attrs:{shape:l}}),u=l2({inputs:{a:r,b:c},backend:n}),p=e2({inputs:{x:u},backend:n}),d=Zv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=ve({inputs:{x:d},backend:n,attrs:{shape:l}}),m=i2({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}var qZ={kernelName:xi,backendName:"webgl",kernelFunc:u2};function KZ(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:u2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new VZ(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var XZ={kernelName:oh,backendName:"webgl",kernelFunc:KZ},c2="return -x;";function YZ(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=BK(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Iu(a.shape,c2):r=new gs(a.shape,c2),n.runWebGLProgram(r,[a],a.dtype)}var JZ={kernelName:fl,backendName:"webgl",kernelFunc:YZ},ZZ=er.nonMaxSuppressionV3Impl;function QZ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=a,c=n.readSync(r.dataId),u=n.readSync(s.dataId),{selectedIndices:p}=ZZ(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var eQ={kernelName:yl,backendName:"webgl",kernelFunc:QZ},tQ=er.nonMaxSuppressionV4Impl;function nQ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:d,validOutputs:h}=tQ(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var aQ={kernelName:bl,backendName:"webgl",kernelFunc:nQ},rQ=er.nonMaxSuppressionV5Impl;function sQ(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=a,u=n.readSync(r.dataId),p=n.readSync(s.dataId),d=i,h=o,m=l,f=c,{selectedIndices:g,selectedScores:y}=rQ(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var iQ={kernelName:xl,backendName:"webgl",kernelFunc:sQ},oQ=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${a}), float(${n}),
float(index == coords.y)));
}
`}},lQ=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=k.sizeFromShape(r.shape),c=new oQ(l,s,i,o),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=ve({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},uQ={kernelName:si,backendName:"webgl",kernelFunc:lQ};function df(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Dp({inputs:{input:a},backend:n}),s=df({inputs:{x:r},backend:n}),i=pf({inputs:{input:a},backend:n}),o=df({inputs:{x:i},backend:n}),l=ys({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return aw({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var cQ={kernelName:Ol,backendName:"webgl",kernelFunc:df};function p2(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Dp({inputs:{input:a},backend:n}),s=p2({inputs:{x:r},backend:n}),i=pf({inputs:{input:a},backend:n}),o=df({inputs:{x:i},backend:n}),l=ys({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return aw({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var pQ={kernelName:vl,backendName:"webgl",kernelFunc:p2};function dQ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return nw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{k.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let p=nw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=GS({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var hQ={kernelName:wl,backendName:"webgl",kernelFunc:dQ},mQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let a=e.length,r=gt(a),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(float(${n}));
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(float(${n}));
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},fQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=gt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=gn("rc",a),l=gn("source",a),c=`${o[a-1]} < ${this.outputShape[a-1]}`,u=a===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
if(${c}) {
`,a===1?"":`}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
if(${c}) {`],d=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${p[m]}
if (${d}) {
result[${m}] = float(${n});
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=a===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},d2=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new fQ(r.shape,s,i):new mQ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},gQ={kernelName:ii,backendName:"webgl",kernelFunc:d2},yQ=`
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);
`,bQ=`
// 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));
`+rf+`
return result;
`,xQ=sn({opSnippet:yQ,packedOpSnippet:bQ}),vQ={kernelName:oi,backendName:"webgl",kernelFunc:xQ};function wQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,l=[],c=k.parseAxisParam(s,r.shape),u=c,p=E.getAxesPermutation(u,o),d=r;p!=null&&(d=Cn({inputs:{x:r},backend:n,attrs:{perm:p}}),u=E.getInnerMostAxes(u.length,o),l.push(d)),E.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=WK(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=E.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=bh(r.dtype),v=Qi(y,b,"prod",n);h=ve({inputs:{x:v},backend:n,attrs:{shape:m}}),l.push(y),l.push(v)}if(i){l.push(h);let m=E.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var kQ={kernelName:kl,backendName:"webgl",kernelFunc:wQ},h2=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=VK(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},IQ={kernelName:Cc,backendName:"webgl",kernelFunc:h2},NQ="return 1.0 / x;",TQ=Je({opSnippet:NQ}),SQ={kernelName:Il,backendName:"webgl",kernelFunc:TQ},CQ=La+`
return (x < 0.0) ? 0.0 : x;
`,_Q=`
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;
`,EQ=Je({opSnippet:CQ,packedOpSnippet:_Q}),AQ={kernelName:ui,backendName:"webgl",kernelFunc:EQ},FQ=La+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,$Q=`
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;
`,DQ=Je({opSnippet:FQ,packedOpSnippet:$Q}),MQ={kernelName:pi,backendName:"webgl",kernelFunc:DQ},RQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.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 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);
}
`}},PQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.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 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 < ${l-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 OQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new PQ(r.shape,l,c,s,i):new RQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var LQ={kernelName:ci,backendName:"webgl",kernelFunc:OQ},zQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${m});
// 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 >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${a-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), ${r-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 BQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new zQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var WQ={kernelName:ch,backendName:"webgl",kernelFunc:BQ},VQ=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[a&&t>1?i-1:i,a&&n>1?o-1:o],u=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.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 coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function UQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new VQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var GQ={kernelName:_c,backendName:"webgl",kernelFunc:UQ},HQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${p});
const float invWidthScale = float(${d});
const int winHeight = int(${h});
const int winWidth = int(${m});
// 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 >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function jQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new HQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var qQ={kernelName:uh,backendName:"webgl",kernelFunc:jQ},KQ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=gt(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},XQ=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-${n} tensor is not yet supported`);this.outputShape=e;let a=gn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=gt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(a.slice())};
if(${r}){
result.g = ${l(a.slice())};
}
if(${s}) {
result.b = ${c(a.slice())};
if(${r}) {
result.a = ${u(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,b)=>d(b,h)),f=m.join(","),g=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${g}))`}function d(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function YQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Wn({inputs:{x:r},backend:n});let l=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XQ(r.shape,o):new KQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var JQ={kernelName:di,backendName:"webgl",kernelFunc:YQ},ZQ=class{constructor(e,t,n,a){this.variableNames=["Image"],this.outputShape=[];let r=e[1],s=e[2],i=Math.sin(t).toFixed(3),o=Math.cos(t).toFixed(3);this.outputShape=e;let[l,c]=E.getImageCenter(a,r,s),u=l.toFixed(3),p=c.toFixed(3),d="";typeof n=="number"?d=`float outputValue = ${n.toFixed(2)};`:d=`
vec3 fill = vec3(${n.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) - ${u}) * ${o} - (float(y) - ${p}) * ${i};
float coordYFloat = (float(x) - ${u}) * ${i} + (float(y) - ${p}) * ${o};
int coordX = int(round(coordXFloat + ${u}));
int coordY = int(round(coordYFloat + ${p}));
${d}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${r}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},QQ={kernelName:Ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new ZQ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},eee=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,tee=Je({opSnippet:eee}),nee={kernelName:hi,backendName:"webgl",kernelFunc:tee},aee="return inversesqrt(x);",ree=Je({opSnippet:aee,cpuKernelImpl:UK}),see={kernelName:mi,backendName:"webgl",kernelFunc:ree},m2=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(r.length),l=gt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${r});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function iee(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:p}=E.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=ve({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new m2(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=ve({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var oee={kernelName:Tl,backendName:"webgl",kernelFunc:iee},lee=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);a=o.join(),r=l.join()}let s=gt(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function uee(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new lee(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],ha(r.dtype,s.dtype))}var cee={kernelName:Sl,backendName:"webgl",kernelFunc:uee},pee=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${E.SELU_SCALEALPHA};
float scale = ${E.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,dee=Je({opSnippet:pee}),hee={kernelName:Cl,backendName:"webgl",kernelFunc:dee},mee="return 1.0 / (1.0 + exp(-1.0 * x));",fee=Je({opSnippet:mee}),gee={kernelName:gi,backendName:"webgl",kernelFunc:fee},yee=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,bee=Je({opSnippet:yee}),xee={kernelName:Al,backendName:"webgl",kernelFunc:bee},vee=CS+`
return sin(x);
`,wee=Je({opSnippet:vee}),kee={kernelName:fi,backendName:"webgl",kernelFunc:wee},Iee=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Nee=Je({opSnippet:Iee}),Tee={kernelName:El,backendName:"webgl",kernelFunc:Nee},See=`
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;
`,Cee=Je({opSnippet:See}),_ee={kernelName:Fl,backendName:"webgl",kernelFunc:Cee},Eee=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=d2({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=E.getReshaped(u.shape,s,o,!1),d=E.getPermuted(p.length,s.length,!1),h=E.getReshapedPermuted(u.shape,s,o,!1),m=ve({inputs:{x:u},backend:n,attrs:{shape:p}}),f=Cn({inputs:{x:m},backend:n,attrs:{perm:d}}),g=ve({inputs:{x:f},backend:n,attrs:{shape:h}});return c.push(u),c.push(m),c.push(f),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Aee={kernelName:Ec,backendName:"webgl",kernelFunc:Eee};function Fee(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:l,numUpdates:c,strides:u,outputSize:p}=E.calculateShapes(s,r,o),d=!1,h=new m2(c,l,r.shape.length,s.shape.length,u,[p,1],d),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=ve({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var $ee={kernelName:ph,backendName:"webgl",kernelFunc:Fee};function Dee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=E.prepareSplitSize(r,s,o),c=r.shape.length,u=new Array(c).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[o]=d;let m=$p({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=d,m})}var Mee={kernelName:$l,backendName:"webgl",kernelFunc:Dee},Ree="return sqrt(x);",Pee=Je({opSnippet:Ree}),Oee={kernelName:yi,backendName:"webgl",kernelFunc:Pee},Lee="return x * x;",zee=Je({opSnippet:Lee}),Bee={kernelName:Ac,backendName:"webgl",kernelFunc:zee},f2="return (a - b) * (a - b);",Wee=sn({opSnippet:f2,packedOpSnippet:f2}),Vee={kernelName:vi,backendName:"webgl",kernelFunc:Wee};function Uee({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=La+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new gs(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Gee={kernelName:Kr,backendName:"webgl",kernelFunc:Uee},Hee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=gt(n.length),s=gt(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function jee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:p,shrinkAxisMask:d}=a,{nonStrided:h,$begin:m,$strides:f,size:g,newShape:y,outShape:b}=dn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),v=ve({inputs:{x:r},backend:n,attrs:{shape:y}}),x;if(h){let T=$p({inputs:{x:v},backend:n,attrs:{begin:m,size:g}});x=ve({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))x=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([v])){let T=n.texData.get(v.dataId).values,C=Le(v.shape,v.dtype,T),$=HK(b,C,f,m);x=n.makeTensorInfo(b,v.dtype,$.values)}else{let T=new Hee(m,f,b);x=n.runWebGLProgram(T,[v],v.dtype)}let N=ve({inputs:{x},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(x),N}var qee={kernelName:Dl,backendName:"webgl",kernelFunc:jee},Kee="return tan(x);",Xee=Je({opSnippet:Kee}),Yee={kernelName:Ml,backendName:"webgl",kernelFunc:Xee},Jee=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Zee=Je({opSnippet:Jee}),Qee={kernelName:ki,backendName:"webgl",kernelFunc:Zee},tte=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=gt(this.rank),r=ete(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function ete(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function g2(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"){let o=n.readSync(r.dataId).map(u=>k.decodeString(u)),l=Le(r.shape,r.dtype,o),c=qK(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new tte(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var nte={kernelName:qr,backendName:"webgl",kernelFunc:g2};function ate(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,c]=KK(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var rte={kernelName:Rl,backendName:"webgl",kernelFunc:ate};function ste(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;_p(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:l,indices:c}=XK(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var ite={kernelName:dh,backendName:"webgl",kernelFunc:ste};function ote(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,l=r.shape[s],c=new Array(o-1),u=0;for(let f=0;f<o;f++)f!==s&&(c[u++]=i.shape[f]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(l);for(let f=0;f<m.length;f++){d[s]=f;let g=$p({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),y=ve({inputs:{x:g},backend:n,attrs:{shape:c}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var lte={kernelName:Pl,backendName:"webgl",kernelFunc:ote},ute=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,p=`
sumValue += dot(values, segFilter);
`,d="";r%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${d}
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(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${p}
}
int inIdx = inOffset + ${c};
if (${u===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
);
${p}
} else if (${u===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
);
${p}
} else if (${u===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
);
${p}
}
setOutput(${l});
}
`}};function cte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],o),p=r;u!=null&&(p=Cn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=E.getInnerMostAxes(1,o)[0]);let d=E.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=bh(r.dtype),g=(x,N,T,C,$)=>{let F=x.shape[0],O=x.shape[1],W=E.segment_util.segOpComputeOptimalWindowSize(O,$),V={windowSize:W,inSize:O,batchSize:F,numSegments:$},H=new ute(V,N),K=n.compileAndRun(H,[x,T],C);if(l.push(K),K.shape[1]===$)return K;let j=h2({backend:n,attrs:{start:0,stop:$,step:1,dtype:"float32"}}),Y=g2({inputs:{x:j},backend:n,attrs:{reps:[O/W]}});return l.push(j),l.push(Y),g(K,N,Y,C,$)},y=g(m,"unsortedSegmentSum",s,f,i),b=ve({inputs:{x:y},backend:n,attrs:{shape:d}}),v=b;if(u!=null){l.push(b);let x=E.getUndoAxesPermutation(u);v=Cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return 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Mp;(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"})(Mp||(Mp={}));var y2;function fte(e){y2=e.wasm.cwrap(Ni,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function gte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let $=n.dataIdMap.get(i.dataId);if($.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${$.shape.length}.`);m=$.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Mp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let 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Got input batch dimensions of (${m}) and (${f}).`);let v=(g>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,h]);k.assert(u===p,()=>`Error in matMul: inner shapes (${u}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,u,d]:[g,d,u],N=o?[y,h,p]:[y,p,h],T=za({inputs:{x:r},backend:n,attrs:{shape:x}}),C=za({inputs:{x:s},backend:n,attrs:{shape:N}}),$=n.dataIdMap.get(T.dataId).id,F=n.dataIdMap.get(C.dataId).id,O=i?T.shape[2]:T.shape[1],W=o?C.shape[1]:C.shape[2],V=Math.max(g,y),H=n.makeOutput([V,O,W],T.dtype),K=n.dataIdMap.get(H.dataId).id,j=new Uint8Array(new Int32Array(T.shape).buffer),Y=new Uint8Array(new Int32Array(C.shape).buffer);return k2($,j,T.shape.length,F,Y,C.shape.length,i,o,K),H.shape=v,H}var Lte={kernelName:Os,backendName:"wasm",setupFunc:Pte,kernelFunc:Ote};function ff(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var zte={kernelName:Ls,backendName:"wasm",kernelFunc:ff},I2;function Bte(e){I2=e.wasm.cwrap(jr,null,["number","number","number","number"])}function Wte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return I2(o,s,i,c),l}var Vte={kernelName:jr,backendName:"wasm",setupFunc:Bte,kernelFunc:Wte};function N2(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return hf({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(E.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(v=>{let x=k.sizeFromShape(v.shape.slice(a));return za({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),m=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=E.computeOutShape(h.map(v=>v.shape),1);let f=h[0].shape[0]===1,g=_v(m,r,t[0].dtype,f),y=E.computeOutShape(s.map(v=>v.shape),a);i.shape=y;let b=n.dataIdMap.get(i.dataId);return b.stringBytes=E.fromStringArrayToUint8(g),i}let l=k.sizeFromShape(s[0].shape.slice(0,a)),c=0,u=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return c+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<l;h++){let m=h*c;for(let f=0;f<p.length;f++){let g=u[f],y=h*g,b=p[f].subarray(y,y+g);d.set(b,m),m+=g}}return i}var Ute={kernelName:qo,backendName:"wasm",kernelFunc:N2},T2;function Gte(e){T2=e.wasm.cwrap(zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hte(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p,dataFormat:d}=n,h=E.convertConv2DDataFormat(d),m=E.computeConv2DInfo(r.shape,s.shape,l,c,u,p,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.padInfo.top,b=m.padInfo.right,v=m.padInfo.bottom,x=m.padInfo.left,N=m.dilationHeight,T=m.dilationWidth,C=m.strideHeight,$=m.strideWidth,F=m.inChannels,O=m.outChannels,W=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. 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Please use 'channelsLast'.`);let W=a.makeOutput(h.outShape,"float32"),V=a.dataIdMap.get(W.dataId).id;return A2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,v,O,x,N,T,C,$,F,V),W}var lne={kernelName:Us,backendName:"wasm",setupFunc:ine,kernelFunc:one},une=!1,cne=yn(Qo,une,"bool"),pne=Un(Hs);function sw(e){let{inputs:t,attrs:n,backend:a}=e,{input:r}=t,{dim:s}=n,i=r.shape.length,o=r.shape.slice(),l=s;return s<0&&(k.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),za({inputs:{x:r},backend:a,attrs:{shape:o}})}var dne={kernelName:el,backendName:"wasm",kernelFunc:sw};function hne(e){let{attrs:{shape:t,value:n,dtype:a},backend:r}=e,s=r.makeOutput(t,a);return r.typedArrayFromHeap(s).fill(n),s}var mne={kernelName:wc,backendName:"wasm",kernelFunc:hne},F2;function fne(e){F2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function gne(e){let{inputs:t,backend:n}=e,{image:a}=t,r=n.makeOutput(a.shape,a.dtype),s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,l,c,u]=a.shape;return F2(s,o,l,c,u,i),r}var yne={kernelName:nl,backendName:"wasm",kernelFunc:gne,setupFunc:fne},bne=Un(js),xne=!1,vne=yn(qs,xne),$2;function wne(e){$2=e.wasm.cwrap(Ks,null,["number","number","number","number","number","number","number"])}function kne(e){let{backend:t,inputs:n,attrs:a}=e,{varianceEpsilon:r}=a,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,m=c!=null?t.dataIdMap.get(c.dataId).id:0,f=t.makeOutput(s.shape,s.dtype);if(k.sizeFromShape(s.shape)===0)return f;let g=t.dataIdMap.get(f.dataId).id;return $2(u,p,d,h,m,r,g),f}var Ine={kernelName:Ks,backendName:"wasm",setupFunc:wne,kernelFunc:kne},D2;function Nne(e){D2=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Tne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Mp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return D2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Sne={kernelName:Ti,backendName:"wasm",setupFunc:Nne,kernelFunc:Tne},M2;function Cne(e){M2=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _ne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=E.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Mp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,b=a.dataIdMap.get(s.dataId).id,v=f.outChannels,x=0;if(i!=null){let se=a.dataIdMap.get(i.dataId);if(se.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${se.shape.length}.`);if(se.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${se.shape}) does not match the number of output channels (${v})`);x=se.id}let N=f.filterHeight,T=f.filterWidth,C=f.padInfo.top,$=f.padInfo.right,F=f.padInfo.bottom,O=f.padInfo.left,W=f.dilationHeight,V=f.dilationWidth,H=f.strideHeight,K=f.strideWidth,j=f.inChannels,Y=f.padInfo.type==="SAME"?1:0,J=f.batchSize,ne=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),ee=a.dataIdMap.get(ie.dataId).id,le=o==null?0:a.dataIdMap.get(o.dataId).id;return M2(y,J,ne,Q,b,N,T,x,C,$,F,O,Y,W,V,H,K,j,v,g,le,m||0,ee),ie}var Ene={kernelName:Si,backendName:"wasm",setupFunc:Cne,kernelFunc:_ne},R2;function Ane(e){R2=e.wasm.cwrap(rl,null,["number","number","number","number","number","number","array","number"])}function Fne(e){let{backend:t,inputs:n}=e,{params:a,indices:r}=n,[s,i,o,l]=Ay.prepareAndValidate(a,r),c=t.makeOutput(s,a.dtype);if(i===0)return c;let u=r.shape,p=u[u.length-1],d=t.dataIdMap.get(a.dataId).id,h=t.dataIdMap.get(r.dataId).id,m=new Uint8Array(new Int32Array(l).buffer),f=t.dataIdMap.get(c.dataId).id;return R2(d,Vn[a.dtype],h,i,p,o,m,f),c}var $ne={kernelName:rl,backendName:"wasm",setupFunc:Ane,kernelFunc:Fne},P2;function Dne(e){P2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Mne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=za({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=za({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,v=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return P2(g,Vn[r.dtype],v,f,y,c.batchSize,x,b),m.shape=c.outputShape,m}var Rne={kernelName:al,backendName:"wasm",setupFunc:Dne,kernelFunc:Mne},Pne=!1,One=yn(sl,Pne,"bool"),Lne=!1,zne=yn(Xs,Lne,"bool"),O2;function Bne(e){O2=e.wasm.cwrap(Ys,null,["number","number","number"])}function Wne(e){let{inputs:{x:t},attrs:{alpha:n},backend:a}=e,r=a.dataIdMap.get(t.dataId).id,s=a.makeOutput(t.shape,t.dtype);if(k.sizeFromShape(t.shape)!==0){let i=a.dataIdMap.get(s.dataId).id;O2(r,n,i)}return s}var Vne={kernelName:Ys,backendName:"wasm",setupFunc:Bne,kernelFunc:Wne},Une=!1,Gne=yn(cl,Une,"bool"),Hne=!1,jne=yn(pl,Hne,"bool"),qne=Un(Js),Kne=!1,Xne=yn(hl,Kne,"bool"),L2;function Yne(e){L2=e.wasm.cwrap(Zs,null,["number, number, number"])}function Jne(e){let{backend:t,inputs:n,attrs:a}=e,{reductionIndices:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:u,originalAxes:p,inputWasTransposed:d}=Cu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;E.assertAxesAreInnerMostDims("max",u,h);let[m,f]=E.computeOutAndReduceShapes(l.shape,u),g=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(l.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;L2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Zne={kernelName:Zs,backendName:"wasm",setupFunc:Yne,kernelFunc:Jne},Qne=!1,eae=yn(Qs,Qne),z2;function tae(e){z2=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nae(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,b=u.dilationWidth,v=u.strideHeight,x=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. 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Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var V2;function fae(e){V2=e.wasm.cwrap(yl,"number",["number","number","number","number","number"])}function gae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,p=V2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=iw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var yae={kernelName:yl,backendName:"wasm",setupFunc:fae,kernelFunc:gae},U2;function bae(e){U2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","bool"])}function xae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=U2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=iw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var vae={kernelName:bl,backendName:"wasm",setupFunc:bae,kernelFunc:xae},G2;function wae(e){G2=e.wasm.cwrap(xl,"number",["number","number","number","number","number","number"])}function kae(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(c.dataId).id,d=G2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=iw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var Iae={kernelName:xl,backendName:"wasm",setupFunc:wae,kernelFunc:kae},Nae=!1,Tae=yn(gl,Nae,"bool"),H2;function Sae(e){H2=e.wasm.cwrap(si,null,["number","number","number","number","number"])}function Cae(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,l=n.makeOutput([...r.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(r.dataId).id;return H2(u,s,i,o,c),l}var _ae={kernelName:si,backendName:"wasm",setupFunc:Sae,kernelFunc:Cae};function Eae(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var Aae={kernelName:vl,backendName:"wasm",kernelFunc:Eae};function Fae(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return sw({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(l=>{k.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===l.dtype,()=>"All tensors passed to stack must have matching 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zae(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return q2(s,i,l),o}var Bae={kernelName:li,backendName:"wasm",setupFunc:Lae,kernelFunc:zae},K2;function Wae(e){K2=e.wasm.cwrap(kl,null,["number","number","number","number"])}function Vae(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:p,originalAxes:d,inputWasTransposed:h}=Cu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=E.getInnerMostAxes(m.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",m,c.shape.length);let[f,g]=E.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(b.dataId).id;K2(l,y,Vn[b.dtype],v)}if(h&&t.disposeData(u.dataId),s){let v=E.expandShapeToKeepDim(b.shape,d);b.shape=v}return b}var Uae={kernelName:kl,backendName:"wasm",setupFunc:Wae,kernelFunc:Vae},Gae=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=Fv(a,r,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Hae={kernelName:Cc,backendName:"wasm",kernelFunc:Gae},jae=!0,qae=yn(Gs,jae),Kae=Un(ui),Xae=Un(pi),X2;function Yae(e){X2=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number","number","number","number"])}function Jae(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,[u,p,d,h]=r.shape,m=[u,l,c,h],f=t.dataIdMap.get(r.dataId),g;f.dtype!=="float32"&&(g=ff({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(g.dataId));let y=f.id,b=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return b;let v=t.dataIdMap.get(b.dataId).id;return X2(y,u,p,d,h,l,c,s?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),b}var Zae={kernelName:ci,backendName:"wasm",setupFunc:Yae,kernelFunc:Jae},Y2;function Qae(e){Y2=e.wasm.cwrap(di,null,["number","array","number","array","number","number"])}function ere(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return hf({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);return Y2(l,u,i.length,p,r.shape.length,c),za({inputs:{x:o},attrs:{shape:r.shape},backend:n})}var tre={kernelName:di,backendName:"wasm",kernelFunc:ere,setupFunc:Qae},J2;function nre(e){J2=e.wasm.cwrap(Ll,null,["number","number","number","number","number","number","number","number","array","number","number"])}function are(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[p,d,h,m]=r.shape,[f,g]=E.getImageCenter(o,d,h),y=i===0,b=255,v=typeof i=="number"?[i,i,i,y?0:b]:[...i,b],x=new Uint8Array(new Int32Array(v).buffer);return J2(c,p,d,h,m,s,f,g,x,v.length,u),l}var rre={kernelName:Ll,backendName:"wasm",kernelFunc:are,setupFunc:nre},sre=Un(hi),ire=Un(mi),Z2;function ore(e){Z2=e.wasm.cwrap(Tl,null,["number","number","number","number","number","number","array","number","number"])}function lre(e){let{backend:t,inputs:n,attrs:a}=e,{indices:r,updates:s}=n,{shape:i}=a,o=t.makeOutput(i,s.dtype);if(k.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:p,outputSize:d}=Fy.calculateShapes(s,r,i),h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,f=new Uint8Array(new Int32Array(p).buffer),g=t.dataIdMap.get(o.dataId).id;return Z2(h,m,Vn[s.dtype],l,c,u,f,d,g),o}var ure={kernelName:Tl,backendName:"wasm",setupFunc:ore,kernelFunc:lre},Q2;function cre(e){Q2=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function pre(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,p=a.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:k.sizeFromShape(r.shape.slice(1));return Q2(i,o,l,h,u),c}var dre={kernelName:Sl,backendName:"wasm",kernelFunc:pre,setupFunc:cre},eC;function hre(e){eC=e.wasm.cwrap(gi,null,["number","number"])}function mre(e){let{backend:t,inputs:{x:n}}=e,a=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(r.dataId).id;return k.sizeFromShape(r.shape)===0||eC(a,s),r}var fre={kernelName:"Sigmoid",backendName:"wasm",setupFunc:hre,kernelFunc:mre},gre=Un(fi);function gf(e){let{inputs:{x:t},attrs:{begin:n,size:a},backend:r}=e,[s,i]=dn.parseSliceParams(t,n,a),o=dn.isSliceContinous(t.shape,s,i),l=r.readSync(t.dataId),c=r.makeOutput(i,t.dtype),u=k.computeStrides(t.shape),p=r.dataIdMap.get(c.dataId);if(o){let m=dn.computeFlatOffset(s,u);return t.dtype==="string"?p.stringBytes=l.slice(m,m+k.sizeFromShape(i)):r.typedArrayFromHeap(c).set(l.subarray(m,m+k.sizeFromShape(i))),c}if(t.dtype==="string"){let m=Km(l,s,i,t.shape,t.dtype);return p.stringBytes=m,c}let d=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)yre(l,u[0],d,s,i);else if(h===3)bre(l,u[0],u[1],d,s,i);else if(h===4)xre(l,u[0],u[1],u[2],d,s,i);else{let m=Km(l,s,i,t.shape,t.dtype);d.set(m)}return c}function yre(e,t,n,a,r){let s=0,i=a[0],o=a[1],l=i+r[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+r[1]),s),s+=r[1]}}function bre(e,t,n,a,r,s){let i=0,o=r[0],l=r[1],c=r[2],u=o+s[0],p=l+s[1];for(let d=o;d<u;d++)for(let h=l;h<p;h++){let m=d*t+h*n+c;a.set(e.subarray(m,m+s[2]),i),i+=s[2]}}function 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lt({x:s,y:i,width:a,height:r})}padAtBorders(t,n){let a=this.width+1,r=this.height+1,s=1,i=1,o=a,l=r,c=this.left,u=this.top,p=this.right,d=this.bottom;return p>n&&(o=-p+n+a,p=n),d>t&&(l=-d+t+r,d=t),c<1&&(l=2-c,c=1),u<1&&(l=2-u,u=1),{dy:i,edy:l,dx:s,edx:o,y:u,ey:d,x:c,ex:p,w:a,h:r}}calibrate(t){return new lt({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var ao=class extends lt{constructor(t,n,a,r,s=!1){super({left:t,top:n,right:a,bottom:r},s)}};var Dr=class{constructor(t,n,a,r,s){this._imageDims=new un(s.width,s.height),this._score=t,this._classScore=n,this._className=a,this._box=new lt(r).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new lt(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Dr(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var bt=class extends Dr{constructor(t,n,a){super(t,t,"",n,a)}forSize(t,n){let{score:a,relativeBox:r,imageDims:s}=super.forSize(t,n);return new bt(a,r,s)}};function wf(e,t,n=!0){let a=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),r=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),s=a*r;return n?s/(e.area+t.area-s):s/Math.min(e.area,t.area)}function kf(e){let t=e.map(o=>o.x),n=e.map(o=>o.y),a=t.reduce((o,l)=>l<o?l:o,Infinity),r=n.reduce((o,l)=>l<o?l:o,Infinity),s=t.reduce((o,l)=>o<l?l:o,0),i=n.reduce((o,l)=>o<l?l:o,0);return new ao(a,r,s,i)}function If(e,t,n,a=!0){let r=t.map((i,o)=>({score:i,boxIndex:o})).sort((i,o)=>i.score-o.score).map(i=>i.boxIndex),s=[];for(;r.length>0;){let i=r.pop();s.push(i);let o=r,l=[];for(let c=0;c<o.length;c++){let u=o[c],p=e[i],d=e[u];l.push(wf(p,d,a))}r=r.filter((c,u)=>l[u]<=n)}return s}function ka(e,t){return D(()=>{let[n,a,r]=t,s=In([...e.shape.slice(0,3),1],n,"float32"),i=In([...e.shape.slice(0,3),1],a,"float32"),o=In([...e.shape.slice(0,3),1],r,"float32"),l=Qe([s,i,o],3);return ge(e,l)})}function Nf(e,t=!1){return D(()=>{let[n,a]=e.shape.slice(1);if(n===a)return e;let r=Math.abs(n-a),s=Math.round(r*(t?.5:1)),i=n>a?2:1,o=d=>{let h=e.shape.slice();return h[i]=d,In(h,0,"float32")},l=o(s),c=r-l.shape[i],p=[t&&c?o(c):null,e,l].filter(d=>!!d).map(d=>pe(d,"float32"));return Qe(p,i)})}function cC(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let a=Math.floor(Math.random()*(n+1)),r=t[n];t[n]=t[a],t[a]=r}return t}function Eu(e){return 1/(1+Math.exp(-e))}function pC(e){return Math.log(e/(1-e))}var ro=class extends lt{constructor(t,n,a,r,s=!1){super({x:t,y:n,width:a,height:r},s)}};var cse=.5,pse=.43,dse=.45,Gn=class{constructor(t,n,a=new De(0,0)){let{width:r,height:s}=n;this._imgDims=new un(r,s),this._shift=a,this._positions=t.map(i=>i.mul(new De(r,s)).add(a))}get shift(){return new De(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 De(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 De(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let s=t instanceof bt?t.box.floor():new lt(t);return this.shiftBy(s.x,s.y).align(null,n)}let{useDlibAlignment:a,minBoxPadding:r}={useDlibAlignment:!1,minBoxPadding:.2,...n};return a?this.alignDlib():this.alignMinBbox(r)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,a,r]=t,s=p=>r.sub(p).magnitude(),i=(s(n)+s(a))/2,o=Math.floor(i/dse),l=no(t),c=Math.floor(Math.max(0,l.x-cse*o)),u=Math.floor(Math.max(0,l.y-pse*o));return new ro(c,u,Math.min(o,this.imageWidth+c),Math.min(o,this.imageHeight+u))}alignMinBbox(t){let n=kf(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var dw=class extends Gn{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],no([t[3],t[4]])]}};var so=class extends Gn{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(no)}};var Au=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${to(this.distance)})`:""}`}};var Fu=class extends lt{static assertIsValidLabeledBox(t,n){if(lt.assertIsValidBox(t,n),!Ba(t.label))throw new Error(`${n} - expected property label (${t.label}) to be a number`)}constructor(t,n){super(t);this._label=n}get label(){return this._label}};var ur=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(a=>!(a instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(a=>new Float32Array(a));return new ur(t.label,n)}};var hw=class extends Fu{static assertIsValidPredictedBox(t,n){if(Fu.assertIsValidLabeledBox(t,n),!_u(t.score)||!_u(t.classScore))throw new Error(`${n} - expected properties score (${t.score}) and (${t.classScore}) to be a number between [0, 1]`)}constructor(t,n,a,r){super(t,n);this._score=a,this._classScore=r}get score(){return this._score}get classScore(){return this._classScore}};function Wa(e){return e.detection instanceof bt}function bs(e,t){return{...e,...{detection:t}}}function mw(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser environment");let t=()=>{throw new Error("readFile - filesystem not available for browser 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isBrowser()");let{Canvas:t=en.Canvas,Image:n=en.Image}=e;en.Canvas=t,en.Image=n,en.createCanvasElement=e.createCanvasElement||(()=>new t),en.createImageElement=e.createImageElement||(()=>new n),en.ImageData=e.ImageData||en.ImageData,en.Video=e.Video||en.Video,en.fetch=e.fetch||en.fetch,en.readFile=e.readFile||en.readFile}var st={getEnv:fse,setEnv:bw,initialize:xw,createBrowserEnv:mw,createFileSystem:Tf,createNodejsEnv:fw,monkeyPatch:gse,isBrowser:gw,isNodejs:yw.isNodejs};xw();function xs(e){return!st.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function bn(e){let{Canvas:t,CanvasRenderingContext2D:n}=st.getEnv();if(e instanceof n)return e;let a=xs(e);if(!(a instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let r=a.getContext("2d");if(!r)throw new Error("resolveContext2d - canvas 2d context is null");return r}var cr;(function(e){e.TOP_LEFT="TOP_LEFT",e.TOP_RIGHT="TOP_RIGHT",e.BOTTOM_LEFT="BOTTOM_LEFT",e.BOTTOM_RIGHT="BOTTOM_RIGHT"})(cr||(cr={}));var Lp=class{constructor(t={}){let{anchorPosition:n,backgroundColor:a,fontColor:r,fontSize:s,fontStyle:i,padding:o}=t;this.anchorPosition=n||cr.TOP_LEFT,this.backgroundColor=a||"rgba(0, 0, 0, 0.5)",this.fontColor=r||"rgba(255, 255, 255, 1)",this.fontSize=s||14,this.fontStyle=i||"Georgia",this.padding=o||4}},vs=class{constructor(t,n,a={}){this.text=typeof t=="string"?[t]:t instanceof vs?t.text:t,this.anchor=n,this.options=new Lp(a)}measureWidth(t){let{padding:n}=this.options;return this.text.map(a=>t.measureText(a).width).reduce((a,r)=>a<r?r:a,0)+2*n}measureHeight(){let{fontSize:t,padding:n}=this.options;return this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:a}=this.options,r=a===cr.BOTTOM_RIGHT||a===cr.TOP_RIGHT,s=a===cr.BOTTOM_LEFT||a===cr.BOTTOM_RIGHT,i=this.measureWidth(t),o=this.measureHeight(),l=r?this.anchor.x-i:this.anchor.x,c=s?this.anchor.y-o:this.anchor.y;if(n){let{width:u,height:p}=n,d=Math.max(Math.min(l,u-i),0),h=Math.max(Math.min(c,p-o),0);return{x:d,y:h}}return{x:l,y:c}}draw(t){let n=xs(t),a=bn(n),{backgroundColor:r,fontColor:s,fontSize:i,fontStyle:o,padding:l}=this.options;a.font=`${i}px ${o}`;let c=this.measureWidth(a),u=this.measureHeight();a.fillStyle=r;let p=this.getUpperLeft(a,n);a.fillRect(p.x,p.y,c,u),a.fillStyle=s,this.text.forEach((d,h)=>{let m=l+p.x,f=l+p.y+(h+1)*i;a.fillText(d,m,f)})}};var vw=class{constructor(t={}){let{boxColor:n,lineWidth:a,label:r,drawLabelOptions:s}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=a||2,this.label=r;let i={anchorPosition:cr.BOTTOM_LEFT,backgroundColor:this.boxColor};this.drawLabelOptions=new Lp({...i,...s})}},Sf=class{constructor(t,n={}){this.box=new lt(t),this.options=new vw(n)}draw(t){let n=bn(t),{boxColor:a,lineWidth:r}=this.options,{x:s,y:i,width:o,height:l}=this.box;n.strokeStyle=a,n.lineWidth=r,n.strokeRect(s,i,o,l);let{label:c}=this.options;c&&new vs([c],{x:s-r/2,y:i},this.options.drawLabelOptions).draw(t)}};function yse(e,t){(Array.isArray(t)?t:[t]).forEach(a=>{let r=a instanceof bt?a.score:Wa(a)?a.detection.score:void 0,s=a instanceof bt?a.box:Wa(a)?a.detection.box:new lt(a),i=r?`${to(r)}`:void 0;new Sf(s,{label:i}).draw(e)})}function $u(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function Cf(e){return new Promise((t,n)=>{if(e instanceof st.getEnv().Canvas||$u(e))return t(null);function a(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),n(s))}function r(s){!s.currentTarget||(s.currentTarget.removeEventListener("load",r),s.currentTarget.removeEventListener("error",a),t(s))}e.addEventListener("load",r),e.addEventListener("error",a)})}function _f(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let a=new FileReader;a.onload=()=>{typeof a.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let r=st.getEnv().createImageElement();r.onload=()=>t(r),r.onerror=n,r.src=a.result},a.onerror=n,a.readAsDataURL(e)})}function ws(e){let{Image:t,Video:n}=st.getEnv();return e instanceof t?new un(e.naturalWidth,e.naturalHeight):e instanceof n?new un(e.videoWidth,e.videoHeight):new un(e.width,e.height)}function io({width:e,height:t}){let{createCanvasElement:n}=st.getEnv(),a=n();return a.width=e,a.height=t,a}function Du(e,t){let{ImageData:n}=st.getEnv();if(!(e instanceof n)&&!$u(e))throw new Error("createCanvasFromMedia - media has not finished 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expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((a,r)=>{if($r(a)){this._imageTensors[r]=a,this._inputDimensions[r]=a.shape;return}if(ra(a)){let i=a.shape[0];if(i!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${i} passed, but not supported in input array`);this._imageTensors[r]=a,this._inputDimensions[r]=a.shape.slice(1);return}let s=a instanceof st.getEnv().Canvas?a:Du(a);this._canvases[r]=s,this._inputDimensions[r]=[s.height,s.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 lr(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),a=this.getInputHeight(t);return pw({width:n,height:a},this.inputSize)}toBatchTensor(t,n=!0){return this._inputSize=t,D(()=>{let a=lr(this.batchSize,0,1).map(s=>{let i=this.getInput(s);if(i instanceof z){let o=ra(i)?i:i.expandDims();return o=Nf(o,n),(o.shape[1]!==t||o.shape[2]!==t)&&(o=Qa.resizeBilinear(o,[t,t])),o.as3D(t,t,3)}if(i instanceof st.getEnv().Canvas)return Ai.fromPixels(Af(i,t,n));throw new Error(`toBatchTensor - at batchIdx ${s}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${i}`)});return 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dr;(function(e){e.FEMALE="female",e.MALE="male"})(dr||(dr={}));var jp=class extends tn{constructor(t=new Nw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof pr?this.faceFeatureExtractor.forwardInput(t):t,r=Jn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=Vp(r,n.fc.age).as1D(),i=Vp(r,n.fc.gender);return{age:s,gender:i}})}forwardInput(t){return D(()=>{let{age:n,gender:a}=this.runNet(t);return{age:n,gender:_a(a)}})}async forward(t){return this.forwardInput(await yt(t))}async predictAgeAndGender(t){let n=await yt(t),a=await this.forwardInput(n),r=ht(a.age),s=ht(a.gender),i=r.map((l,c)=>({ageTensor:l,genderTensor:s[c]})),o=await Promise.all(i.map(async({ageTensor:l,genderTensor:c})=>{let u=(await l.data())[0],p=(await c.data())[0],d=p>.5,h=d?dr.MALE:dr.FEMALE,m=d?p:1-p;return 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Fse(e){let t=ht(Ue(e,[1,0])),n=[ge(t[2],t[0]),ge(t[3],t[1])],a=[Z(t[0],we(n[0],he(2))),Z(t[1],we(n[1],he(2)))];return{sizes:n,centers:a}}function $se(e,t){let{sizes:n,centers:a}=Fse(e),r=ht(Ue(t,[1,0])),s=we(L(hn(we(r[2],he(5))),n[0]),he(2)),i=Z(L(we(r[0],he(10)),n[0]),a[0]),o=we(L(hn(we(r[3],he(5))),n[1]),he(2)),l=Z(L(we(r[1],he(10)),n[1]),a[1]);return Ue(Dt([ge(i,s),ge(l,o),Z(i,s),Z(l,o)]),[1,0])}function BC(e,t,n){return D(()=>{let a=e.shape[0],r=$se(q(Xa(n.extra_dim,[a,1,1]),[-1,4]),q(e,[-1,4]));r=q(r,[a,r.shape[0]/a,4]);let s=ma(We(t,[0,0,1],[-1,-1,-1])),i=We(s,[0,0,0],[-1,-1,1]);i=q(i,[a,i.shape[1]]);let o=ht(r),l=ht(i);return{boxes:o,scores:l}})}function mo(e,t){return D(()=>{let n=e.shape[0],a=q(uo(e,t.box_encoding_predictor),[n,-1,1,4]),r=q(uo(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:a,classPrediction:r}})}function WC(e,t,n){return D(()=>{let a=Ia(e,n.conv_0,[1,1]),r=Ia(a,n.conv_1,[2,2]),s=Ia(r,n.conv_2,[1,1]),i=Ia(s,n.conv_3,[2,2]),o=Ia(i,n.conv_4,[1,1]),l=Ia(o,n.conv_5,[2,2]),c=Ia(l,n.conv_6,[1,1]),u=Ia(c,n.conv_7,[2,2]),p=mo(t,n.box_predictor_0),d=mo(e,n.box_predictor_1),h=mo(r,n.box_predictor_2),m=mo(i,n.box_predictor_3),f=mo(l,n.box_predictor_4),g=mo(u,n.box_predictor_5),y=Qe([p.boxPredictionEncoding,d.boxPredictionEncoding,h.boxPredictionEncoding,m.boxPredictionEncoding,f.boxPredictionEncoding,g.boxPredictionEncoding],1),b=Qe([p.classPrediction,d.classPrediction,h.classPrediction,m.classPrediction,f.classPrediction,g.classPrediction],1);return{boxPredictions:y,classPredictions:b}})}var sa=class{constructor({minConfidence:t,maxResults:n}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=t||.5,this._maxResults=n||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}};var Ns=class extends tn{constructor(){super("SsdMobilenetv1")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("SsdMobilenetv1 - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(512,!1),"float32"),r=ge(L(a,he(.007843137718737125)),he(1)),s=LC(r,n.mobilenetv1),{boxPredictions:i,classPredictions:o}=WC(s.out,s.conv11,n.prediction_layer);return BC(i,o,n.output_layer)})}async forward(t){return this.forwardInput(await yt(t))}async locateFaces(t,n={}){let{maxResults:a,minConfidence:r}=new sa(n),s=await yt(t),{boxes:i,scores:o}=this.forwardInput(s),l=i[0],c=o[0];for(let v=1;v<i.length;v++)i[v].dispose(),o[v].dispose();let u=Array.from(await c.data()),d=zC(l,u,a,.5,r),h=s.getReshapedInputDimensions(0),m=s.inputSize,f=m/h.width,g=m/h.height,y=l.arraySync(),b=d.map(v=>{let[x,N]=[Math.max(0,y[v][0]),Math.min(1,y[v][2])].map($=>$*g),[T,C]=[Math.max(0,y[v][1]),Math.min(1,y[v][3])].map($=>$*f);return new bt(u[v],new ro(T,x,C-T,N-x),{height:s.getInputHeight(0),width:s.getInputWidth(0)})});return l.dispose(),c.dispose(),b}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return OC(t)}extractParams(t){return PC(t)}};function Ew(e){let t=new Ns;return t.extractWeights(e),t}function VC(e){return Ew(e)}var Aw=class extends Ns{};var UC=.4,GC=[new De(.738768,.874946),new De(2.42204,2.65704),new De(4.30971,7.04493),new De(10.246,4.59428),new De(12.6868,11.8741)],HC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],jC=[117.001,114.697,97.404],qC="tiny_yolov2_model",KC="tiny_yolov2_separable_conv_model";var Hf=e=>typeof e=="number";function jf(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(!Hf(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=>Hf(t.x)&&Hf(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(Hf)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(e.meanRgb)}`)}function Ou(e){return D(()=>{let t=L(e,he(.10000000149011612));return Z(Ye(ge(e,t)),t)})}function Rr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Ft(n,t.conv.filters,[1,1],"valid"),n=ge(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Ou(n)})}function Pr(e,t){return D(()=>{let n=ta(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Pi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Ou(n)})}function Dse(e,t){let n=Mu(e,t);function a(i,o){let l=nt(e(i)),c=nt(e(i));return t.push({paramPath:`${o}/sub`},{paramPath:`${o}/truediv`}),{sub:l,truediv:c}}function r(i,o,l){let c=n(i,o,3,`${l}/conv`),u=a(o,`${l}/bn`);return{conv:c,bn:u}}let s=Ru(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function XC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=vn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=Dse(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,v]=a,x=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),C=c(m,f,"conv3"),$=c(f,g,"conv4"),F=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,W=v?c(b,v,"conv7"):void 0,V=o(v||b||y,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}else{let[p,d,h,m,f,g,y,b,v]=a,x=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),C=l(m,f,"conv3"),$=l(f,g,"conv4"),F=l(g,y,"conv5"),O=l(y,b,"conv6"),W=l(b,v,"conv7"),V=o(v,5*n,1,"conv8");u={conv0:x,conv1:N,conv2:T,conv3:C,conv4:$,conv5:F,conv6:O,conv7:W,conv8:V}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Mse(e,t){let n=Hn(e,t);function a(o){let l=n(`${o}/sub`,1),c=n(`${o}/truediv`,1);return{sub:l,truediv:c}}function r(o){let l=n(`${o}/filters`,4),c=n(`${o}/bias`,1);return{filters:l,bias:c}}function s(o){let l=r(`${o}/conv`),c=a(`${o}/bn`);return{conv:l,bn:c}}let i=Pu(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function YC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Mse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return xn(e,n),{params:i,paramMappings:n}}var Ua=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var Fw=class extends tn{constructor(t){super("TinyYolov2");jf(t),this._config=t}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(t,n){let a=Rr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Rr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=Rr(a,n.conv6),a=Rr(a,n.conv7),uo(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Ou(uo(t,n.conv0,"valid",!1)):Pr(t,n.conv0);return a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv1),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv2),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv3),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv4),a=$t(a,[2,2],[2,2],"same"),a=Pr(a,n.conv5),a=$t(a,[2,2],[1,1],"same"),a=n.conv6?Pr(a,n.conv6):a,a=n.conv7?Pr(a,n.conv7):a,uo(a,n.conv8,"valid",!1)}forwardInput(t,n){let{params:a}=this;if(!a)throw new Error("TinyYolov2 - load model before inference");return D(()=>{let r=pe(t.toBatchTensor(n,!1),"float32");return r=this.config.meanRgb?ka(r,this.config.meanRgb):r,r=r.div(he(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await yt(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Ua(n),s=await yt(t),i=await this.forwardInput(s,a),o=D(()=>ht(i)[0].expandDims()),l={width:s.getInputWidth(0),height:s.getInputHeight(0)},c=await this.extractBoxes(o,s.getReshapedInputDimensions(0),r);i.dispose(),o.dispose();let u=c.map(g=>g.box),p=c.map(g=>g.score),d=c.map(g=>g.classScore),h=c.map(g=>this.config.classes[g.label]);return If(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Dr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeightMap(t){return YC(t,this.config)}extractParams(t){let n=this.config.filterSizes||Fw.DEFAULT_FILTER_SIZES,a=n?n.length:void 0;if(a!==7&&a!==8&&a!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${a} filterSizes in config`);return XC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),v=y.slice([0,0,0,4],[c,c,u,1]),x=this.withClassScores?_a(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):he(0);return[b,v,x]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let v=0;v<u;v++){let x=Eu(f[y][b][v][0]);if(!a||x>a){let N=(b+Eu(g[y][b][v][0]))/c*o,T=(y+Eu(g[y][b][v][1]))/c*l,C=Math.exp(g[y][b][v][2])*this.config.anchors[v].x/c*o,$=Math.exp(g[y][b][v][3])*this.config.anchors[v].y/c*l,F=N-C/2,O=T-$/2,W={row:y,col:b,anchor:v},{classScore:V,label:H}=this.withClassScores?await this.extractPredictedClass(h,W):{classScore:1,label:0};m.push({box:new ao(F,O,F+C,O+$),score:x,classScore:x*V,label:H,...W})}}return p.dispose(),d.dispose(),h.dispose(),m}async extractPredictedClass(t,n){let{row:a,col:r,anchor:s}=n,i=await t.array();return Array(this.config.classes.length).fill(0).map((o,l)=>i[a][r][s][l]).map((o,l)=>({classScore:o,label:l})).reduce((o,l)=>o.classScore>l.classScore?o:l)}},Lu=Fw;Lu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var fo=class extends Lu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:UC,classes:["face"],...t?{anchors:HC,meanRgb:jC}:{anchors:GC,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(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?KC:qC}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function JC(e,t=!0){let n=new fo(t);return n.extractWeights(e),n}var Qp=class extends Ua{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var ia=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function go(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Is(l)?r(l):l.detection),i=a||(t instanceof z?await lo(t,s):await oo(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function zu(e,t,n,a,r){return go([e],t,async s=>n(s[0]),a,r)}var ZC=.4,QC=[new De(1.603231,2.094468),new De(6.041143,7.080126),new De(2.882459,3.518061),new De(4.266906,5.178857),new De(9.041765,10.66308)],e_=[117.001,114.697,97.404];var yo=class extends Lu{constructor(){let t={withSeparableConvs:!0,iouThreshold:ZC,classes:["face"],anchors:QC,meanRgb:e_,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(r=>new bt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new Ns,tinyFaceDetector:new yo,tinyYolov2:new fo,faceLandmark68Net:new po,faceLandmark68TinyNet:new Kp,faceRecognitionNet:new ho,faceExpressionNet:new Gp,ageGenderNet:new jp},$w=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),t_=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),n_=(e,t)=>et.tinyYolov2.locateFaces(e,t),Dw=e=>et.faceLandmark68Net.detectLandmarks(e),a_=e=>et.faceLandmark68TinyNet.detectLandmarks(e),r_=e=>et.faceRecognitionNet.computeFaceDescriptor(e),s_=e=>et.faceExpressionNet.predictExpressions(e),i_=e=>et.ageGenderNet.predictAgeAndGender(e),Mw=e=>et.ssdMobilenetv1.load(e),o_=e=>et.tinyFaceDetector.load(e),l_=e=>et.tinyYolov2.load(e),u_=e=>et.faceLandmark68Net.load(e),c_=e=>et.faceLandmark68TinyNet.load(e),p_=e=>et.faceRecognitionNet.load(e),d_=e=>et.faceExpressionNet.load(e),h_=e=>et.ageGenderNet.load(e),m_=Mw,f_=$w,g_=Dw;var Rw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Vu=class extends Rw{async run(){let t=await this.parentTask,n=await go(t,this.input,async a=>Promise.all(a.map(r=>et.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Hp(a,n[r]))}withAgeAndGender(){return new Bu(this,this.input)}},Uu=class extends Rw{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Hp(t,n)}withAgeAndGender(){return new Wu(this,this.input)}},vo=class extends Vu{withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},wo=class extends Uu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var Pw=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Bu=class extends Pw{async run(){let t=await this.parentTask,n=await go(t,this.input,async a=>Promise.all(a.map(r=>et.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Jp(Zp(a,i,o),s)})}withFaceExpressions(){return new Vu(this,this.input)}},Wu=class extends Pw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await zu(t,this.input,s=>et.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Jp(Zp(t,a,r),n)}withFaceExpressions(){return new Uu(this,this.input)}},bo=class extends Bu{withFaceExpressions(){return new vo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},xo=class extends Wu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var ed=class extends ia{constructor(t,n){super();this.parentTask=t;this.input=n}},Or=class extends ed{async run(){let t=await this.parentTask;return(await go(t,this.input,a=>Promise.all(a.map(r=>et.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>Yp(t[r],a))}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}},Lr=class extends ed{async run(){let t=await this.parentTask;if(!t)return;let n=await zu(t,this.input,a=>et.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return Yp(t,n)}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}};var td=class extends ia{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},nd=class extends td{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await lo(this.input,n):await oo(this.input,n),r=await Promise.all(a.map(s=>this.landmarkNet.detectLandmarks(s)));return a.forEach(s=>s instanceof z&&s.dispose()),t.map((s,i)=>co(s,r[i]))}withFaceExpressions(){return new vo(this,this.input)}withAgeAndGender(){return new bo(this,this.input)}withFaceDescriptors(){return new Or(this,this.input)}},ad=class extends td{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await lo(this.input,[n]):await oo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),co(t,r)}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptor(){return new Lr(this,this.input)}};var rd=class extends ia{constructor(t,n=new sa){super();this.input=t;this.options=n}},Gu=class extends rd{async run(){let{input:t,options:n}=this,a=n instanceof Qp?r=>et.tinyFaceDetector.locateFaces(r,n):n instanceof sa?r=>et.ssdMobilenetv1.locateFaces(r,n):n instanceof Ua?r=>et.tinyYolov2.locateFaces(r,n):null;if(!a)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return a(t)}runAndExtendWithFaceDetections(){return new Promise(async t=>{let n=await this.run();t(n.map(a=>bs({},a)))})}withFaceLandmarks(t=!1){return new nd(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Vu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Bu(this.runAndExtendWithFaceDetections(),this.input)}},sd=class extends rd{async run(){let t=await new Gu(this.input,this.options),n=t[0];return t.forEach(a=>{a.score>n.score&&(n=a)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?bs({},n):void 0)})}withFaceLandmarks(t=!1){return new ad(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetection(),this.input)}};function y_(e,t=new sa){return new sd(e,t)}function id(e,t=new sa){return new Gu(e,t)}async function Ow(e,t){return id(e,new sa(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function b_(e,t={}){return id(e,new Ua(t)).withFaceLandmarks().withFaceDescriptors()}var x_=Ow;function qf(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),a=Array.from(t);return Math.sqrt(n.map((r,s)=>r-a[s]).reduce((r,s)=>r+s**2,0))}var od=class{constructor(t,n=.6){this._distanceThreshold=n;let a=Array.isArray(t)?t:[t];if(!a.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let r=1,s=()=>`person ${r++}`;this._labeledDescriptors=a.map(i=>{if(i instanceof ur)return i;if(i instanceof Float32Array)return new ur(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new ur(s(),[i.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(a=>qf(a,t)).reduce((a,r)=>a+r,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:a})=>new Au(a,this.computeMeanDistance(t,n))).reduce((n,a)=>n.distance<a.distance?n:a)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this.distanceThreshold?n:new Au("unknown",n.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(a=>ur.fromJSON(a));return new od(n,t.distanceThreshold)}};function v_(e){let t=new yo;return t.extractWeights(e),t}function Lw(e,t){let{width:n,height:a}=new un(t.width,t.height);if(n<=0||a<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:a})}`);if(Array.isArray(e))return e.map(r=>Lw(r,{width:n,height:a}));if(Is(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return co(bs(e,r),s)}return Wa(e)?bs(e,e.detection.forSize(n,a)):e instanceof Gn||e instanceof bt?e.forSize(n,a):e}var Pse=typeof process!="undefined",Ose=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",w_={faceapi:wC,node:Pse,browser:Ose};return Rse;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=face-api.js.map
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