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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 this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t);a.disposeData(t),n.backend=e,e.move(t,r,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to 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this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e,t,n,a,r,s,i){let o,l=[],c=this.isTapeOn();a==null&&(a=this.state.activeScope!=null?this.state.activeScope.name:"");let u=this.state.numBytes,p=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let d;this.backendName==null&&this.backend;let h=ly(a,this.backendName),m;if(h!=null)d=()=>{let g=this.backend.numDataIds();m=h.kernelFunc({inputs:t,attrs:r,backend:this.backend});let y=Array.isArray(m)?m:[m];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(a,g,y);let b=y.map(x=>{if(x.rank!=null)return x;let{dataId:v,shape:N,dtype:T}=x;return this.makeTensorFromDataId(v,N,T)});if(c){let x=this.getTensorsForGradient(a,t,b);if(x==null){i==null&&(i=[]);let v=b.filter((N,T)=>i[T]);x=(s||[]).slice().concat(v)}l=this.saveTensorsForBackwardMode(x)}return b};else{if(e==null)throw new Error(`Error running ${a}: Neither modular kernel nor forward func passed`);let g=y=>{!c||(l=y.map(b=>this.keep(this.clone(b))))};d=()=>{let y=this.backend.numDataIds();m=this.tidy(()=>e(this.backend,g));let b=Array.isArray(m)?m:[m];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(a,y,b),b}}let f;return 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=uy(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(F(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 o=n.filter((l,c)=>s[c]);return i.concat(o)}return null}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Hr(e[0])&&(r=e.map(o=>Ac(o)));let s=a.write(r,t,n),i=new z(t,n,s,this.nextTensorId());if(this.incRef(i,a),n==="string"){let o=this.state.tensorInfo.get(s),l=r0(r);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new z(t,n,e,this.nextTensorId());return this.incRef(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Yr(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}incRef(e,t){let 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*a0(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 Yr||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)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of 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s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof z,()=>"The result y returned by f() must be a tensor.");let s=zF(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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u?j(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var kr=R({depthwiseConv2d_:$D});function DD(e){let t={x:C(e,"x","diag")};return P.runKernel(Yd,t)}var MD=R({diag_:DD});function RD(e,t,n,a,r=[1,1],s="NHWC"){let i=C(e,"x","dilation2d"),o=C(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=j(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},p={strides:n,pad:a,dilations:r},d=P.runKernel(yc,u,p);return c?j(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Jy=R({dilation2d_:RD});function PD(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let s=n-1-r,i=e[s]||1;(t[t.length-1-r]||1)>1&&i===1&&a.unshift(s)}return a}function Vt(e,t){let n=[];for(let a=0;a<t.length;a++){let 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n=C(e,"labels","sigmoidCrossEntropyWithLogits"),a=C(t,"logits","sigmoidCrossEntropyWithLogits");rt(n.shape,a.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Xe(a),s=L(a,n),i=Rh(mn(St(Wt(a))));return Z(fe(r,s),i)}function VO(e,t,n,a=0,r=fn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=C(n,"weights","sigmoidCrossEntropy")),rt(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let c=de(a),u=de(1),p=de(.5);s=Z(L(s,fe(u,c)),L(p,c))}let l=WO(s,i);return Tr(l,o,r)}var UO=R({sigmoidCrossEntropy_:VO});function GO(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return Xa((a,r,s)=>{let i=sb(r,[n],!0),o=fe(pe(r,"float32"),i);s([a,o]);let l=St(L(o,a));return{value:Ce(l,[n]),gradFunc:(c,u)=>{let[p,d]=u,h=Ri(c.shape,[n]);return[L(j(c,h),fe(pe(p,"float32"),mn(d))),L(j(c,h),fe(mn(d),pe(p,"float32")))]}}})(e,t)}function HO(e,t,n,a=0,r=fn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"onehotLabels","softmaxCrossEntropy"),i=C(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=C(n,"weights","softmaxCrossEntropy")),rt(s.shape,i.shape,"Error in softmaxCrossEntropy: "),a>0){let c=de(a),u=de(1),p=de(s.shape[1]);s=Z(L(s,fe(u,c)),ve(c,p))}let l=GO(s,i);return Tr(l,o,r)}var jO=R({softmaxCrossEntropy_:HO}),qO={fft:Jc,ifft:tu,rfft:Zc,irfft:Xh},KO={hammingWindow:HP,hannWindow:i1,frame:o1,stft:XP},Za={flipLeftRight:QP,resizeNearestNeighbor:h1,resizeBilinear:d1,rotateWithOffset:tO,cropAndResize:JP,nonMaxSuppression:aO,nonMaxSuppressionAsync:pO,nonMaxSuppressionWithScore:hO,nonMaxSuppressionWithScoreAsync:fO,nonMaxSuppressionPadded:yO,nonMaxSuppressionPaddedAsync:xO},f1={bandPart:IO,gramSchmidt:TO,qr:CO},XO={absoluteDifference:FO,computeWeightedLoss:Tr,cosineDistance:$O,hingeLoss:MO,huberLoss:PO,logLoss:LO,meanSquaredError:BO,sigmoidCrossEntropy:UO,softmaxCrossEntropy:jO},Sr=class extends ek{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 Ae(r),t?a:(a.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Tk(e,t)}dispose(){this.iterations_!=null&&Ae(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:de(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Sr,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var am=class extends Sr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:D(()=>He(a).variable(r))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:D(()=>He(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;D(()=>{let l=Z(L(i,this.rho),L(lt(s),1-this.rho)),c=L(ve(rn(Z(o,this.epsilon)),rn(Z(i,this.epsilon))),s),u=Z(L(o,this.rho),L(lt(c),1-this.rho));i.assign(l),o.assign(u);let p=Z(L(c,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};am.className="Adadelta";ts(am);var rm=class extends Sr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:D(()=>Nn(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,lt(r));s.assign(i);let o=Z(L(ve(r,rn(Z(i,P.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ae(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)}};rm.className="Adagrad";ts(rm);var sm=class extends Sr{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=de(t).variable(),this.accBeta2=de(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=fe(1,this.accBeta1),a=fe(1,this.accBeta2);t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:D(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:D(()=>He(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,p=Z(L(c,this.beta1),L(l,1-this.beta1)),d=Z(L(u,this.beta2),L(lt(l),1-this.beta2)),h=ve(p,n),m=ve(d,a);c.assign(p),u.assign(d);let f=Z(L(ve(h,Z(rn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),D(()=>{this.accBeta1.assign(Ja(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ja(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};sm.className="Adam";ts(sm);var im=class extends Sr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],D(()=>{this.iteration=de(0).variable(),this.accBeta1=de(t).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=fe(1,this.accBeta1),a=ve(-this.learningRate,Z(L(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:He(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:He(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,p=Z(L(c,this.beta1),L(l,1-this.beta1)),d=L(u,this.beta2),h=Wt(l),m=Sa(d,h);c.assign(p),u.assign(m);let f=Z(L(ve(a,n),ve(p,Z(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(Z(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new 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)}};im.className="Adamax";ts(im);var ep=class extends Sr{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=Xt(de(-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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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};om.className="Momentum";ts(om);var lm=class extends Sr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=P.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:D(()=>He(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:D(()=>He(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:D(()=>He(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(lt(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=ve(L(s,this.learningRate),rn(fe(l,Z(lt(u),this.epsilon)))),d=Z(L(o,this.momentum),p);i.assign(l),c.assign(u),o.assign(d);let h=fe(a,d);a.assign(h)}else{let c=Z(L(i,this.decay),L(lt(s),1-this.decay)),u=Z(L(o,this.momentum),ve(L(s,this.learningRate),rn(Z(c,this.epsilon))));i.assign(c),o.assign(u);let p=fe(a,u);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ae(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ae(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ae(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)}};lm.className="RMSProp";ts(lm);var Li=class{static sgd(e){return new ep(e)}static momentum(e,t,n=!1){return new om(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new lm(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new sm(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new am(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new im(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}}},$W=0,qe=class extends se.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=$W++,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=Er(n)+"_"+wm(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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extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=wm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],ls(this.inputs).length!==this.inputs.length)throw new B(`The list of inputs passed to the model is redundant. 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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,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(b),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let b=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;er(x===0,"input layer has >1 nodes"),er(v===0,"input layer has >1 tensors"),this.inputLayers.push(b),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}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 iu))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,x,v,N,T)=>{(v==null||N==null||T==null)&&(v=y.sourceLayer,N=y.nodeIndex,T=y.tensorIndex);let E=v.inboundNodes[N];if(x.indexOf(E)!==-1)throw new Aa(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(b.indexOf(E)!==-1)return;this.containerNodes.add(rr.nodeKey(v,N)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(E)===-1&&x.push(E);let A=E.inboundLayers.length;for(let $=0;$<A;$++){let O=E.inputTensors[$],V=E.inboundLayers[$],W=E.nodeIndices[$],H=E.tensorIndices[$];o(O,b,x,V,W,H)}for(b.push(E);x.indexOf(E)>=0;)x.splice(x.indexOf(E),1);i.push(E)},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],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];b=Math.max(b,x),a[y.outboundLayer.id]=b,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=b;for(let v=0;v<y.inboundLayers.length;v++){let N=y.inboundLayers[v],T=y.nodeIndices[v],E=N.inboundNodes[T],A=t[E.id]==null?0:t[E.id];t[E.id]=Math.max(b+1,A),n[E.id]=E}}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(cm);this.layers=[];for(let y of h){let b=d[y];b.sort((x,v)=>{let N=s[x.id],T=s[v.id];return N<T?-1:N>T?1:0});for(let x of b)x instanceof rr&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(cm);let m=this.inputs.slice(),f=[];for(let y of h)for(let b of p[y]){let x=b.outboundLayer;if(x!=null){for(let v of b.inputTensors)if(m.indexOf(v)===-1)throw new Aa(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of b.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let b=g.filter(x=>x===y).length;if(b!==1)throw new Aa(`The name "${y}" is used ${b} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Nm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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}`)}Xb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dm}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=nx(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return D(()=>{e=xt(e);let n=new ji;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return pp(this.outputs,n,t)})}computeMask(e,t){return D(()=>{e=xt(e);let n;return t==null?n=Bi(null,e.length):n=xt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=km(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(cm);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(c.id)!==-1)continue;let u=[];for(let m=0;m<l.inboundLayers.length;m++){let f=l.inboundLayers[m],g=l.nodeIndices[m],y=l.tensorIndices[m],b=`${f.name}_${g}_${y}`,x=n[b];u.push(x)}let p=c.computeOutputShape(Tn(u)),d=km(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];er(o in n),r.push(n[o])}return Tn(r)}runInternalGraph(e,t){t==null&&(t=Bi(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(cm);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[x,v]=h[0];m.mask==null&&(m.mask=v),y=xt(u.call(x,m)),b=xt(u.computeMask(x,v)),f=[x],g=[v]}else f=h.map(x=>x[0]),g=h.map(x=>x[1]),m.mask==null&&(m.mask=g),y=xt(u.call(f,m)),b=xt(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 x=0;x<d.length;++x){let v=d[x],N=y[x],T=b[x];n[v.id]=[N,T]}}}}let r=[],s=[],i=[];for(let o of this.outputs){er(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 rr?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=rr.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=rr.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=rr.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],x=rr.nodeKey(g,y),v=t[x];v==null&&(v=0),m.push([g.name,v,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=rr.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=rr.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 x of g){let v=x[0],N=x[1],T=x[2];if(b=x[3]==null?{}:x[3],!(v in r)){i(f,g);return}let E=r[v];if(E.inboundNodes.length<=N){i(f,g);return}let A=E.inboundNodes[N];y.push(A.outputTensors[T])}y.length>0&&f.apply(Tn(y),b)}function l(f){let g=f.name,y=Ma(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(;!Oz(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];er(g in r);let x=r[g].inboundNodes[y].outputTensors;p.push(x[b])}let m=t.outputLayers;for(let f of m){let g=f[0],y=f[1],b=f[2];er(g in r);let x=r[g].inboundNodes[y].outputTensors;d.push(x[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 d4(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: ${JSON.stringify(e)}`)}function vI(e,t){return d4(e,t,"classWeight")}async function wI(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=D(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());Ae(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])}),tt(i,"float32")}else return null}function h4(e,t){return L(e,t)}var m4=32;function II(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=kI("input",e.inputNames,n),i=kI("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 kI(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 f4(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 y4(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(NI(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=f4(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=lI(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=uI(u,p,n.epochs,null,null,g4(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 x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!lB(r))throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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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=Fh(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=nr(o,n)),o})}function jI(e,t,n,a=[1,1],r="valid",s,i,o=null){return D(()=>{if(s==null&&(s=Fa()),Rt(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=yx(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=os.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 L4(e,t,n,a=[1,1,1],r="valid",s,i){return D(()=>{if(s==null&&(s=Fa()),Rt(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=HI(e,s);if(r==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Xy(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=nr(o,n)),s==="channelsFirst"&&(o=Ue(o,[0,4,1,2,3])),o})}var bx=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",bx.verifyArgs(t),this.rank=e,Jt(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=cu(t.kernelSize,e,"kernelSize"),this.strides=cu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,aa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Rt(this.dataFormat),this.activation=hs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=wt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ht(t.biasConstraint),this.biasRegularizer=kt(t.biasRegularizer),this.activityRegularizer=kt(t.activityRegularizer),this.dilationRate=cu(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(er("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Cb(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:ds(this.activation),useBias:this.useBias,biasInitializer:Et(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:Gt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},mp=class extends bx{constructor(e,t){super(e,t);this.kernel=null,mp.verifyArgs(t),this.filters=t.filters,Jt(this.filters,"filters"),this.kernelInitializer=wt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ht(t.kernelConstraint),this.kernelRegularizer=kt(t.kernelRegularizer)}build(e){e=dt(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=A1(this.activation.getClassName());if(r!=null&&this.rank===2)n=jI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=O4(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=jI(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=L4(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=dt(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=Ra(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:Et(this.kernelInitializer),kernelRegularizer:ht(this.kernelRegularizer),kernelConstraint:Gt(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)}`)}},fp=class extends mp{constructor(e){super(2,e);fp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Cb(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)}.`)}};fp.className="Conv2D";se.registerClass(fp);var Rm=class extends mp{constructor(e){super(3,e);Rm.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)}.`)}};Rm.className="Conv3D";se.registerClass(Rm);var xx=class extends fp{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=dt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return D(()=>{let n=Re(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Mm(o,p,c,this.padding),m=Mm(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=nr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=dt(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]=Mm(t[a],o,s,this.padding),t[r]=Mm(t[r],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};xx.className="Conv2DTranspose";se.registerClass(xx);var qI=class extends mp{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=kt(t.depthwiseRegularizer),this.depthwiseConstraint=Ht(t.depthwiseConstraint),this.pointwiseInitializer=wt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=kt(t.pointwiseRegularizer),this.pointwiseConstraint=Ht(t.pointwiseConstraint)}build(e){if(e=dt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${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 Zt({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=Oi(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=nr(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=Et(this.depthwiseInitializer),e.pointwiseInitializer=Et(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=Gt(this.depthwiseConstraint),e.pointwiseConstraint=Gt(this.pointwiseConstraint),e}};qI.className="SeparableConv";var vx=class extends qI{constructor(e){super(2,e)}};vx.className="SeparableConv2D";se.registerClass(vx);var Pm=class extends mp{constructor(e){super(1,e);Pm.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"&&!Cb(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)}.`)}};Pm.className="Conv1D";se.registerClass(Pm);var wx=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=pm(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return pm(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=pm(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return pm(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}};wx.className="Cropping2D";se.registerClass(wx);var kx=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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sB(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}};kx.className="UpSampling2D";se.registerClass(kx);function z4(e,t,n=[1,1],a="valid",r,s){return D(()=>{r==null&&(r=Fa()),Rt(r);let i=yx(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=kr(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}var Ix=class extends bx{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=wt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ht(e.depthwiseConstraint),this.depthwiseRegularizer=kt(e.depthwiseRegularizer)}build(e){if(e=dt(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=z4(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=nr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=dt(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=Ra(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ra(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=Et(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=Gt(this.depthwiseRegularizer),e}};Ix.className="DepthwiseConv2D";se.registerClass(Ix);function KI(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 XI(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 ${l}D.`);let c=[1,0].concat($a(2,l));if(t=Ue(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===l-1&&(r=Qn(r,-1)),r=Ue(r,c)),a&&(t=Rn(t,0),r!=null&&(r=Rn(r,0)));let u=[],p,d=n,h=t.shape[0],m=pt(t),f;r!=null&&(f=pt(r));for(let y=0;y<h;++y){let b=m[y],x=D(()=>e(b,d));if(r==null)p=x[0],d=x[1];else{let v=D(()=>{let N=f[y],T=Mn(N).sub(N),E=x[0].mul(N).add(d[0].mul(T)),A=d.map(($,O)=>x[1][O].mul(N).add($.mul(T)));return{output:E,newStates:A}});p=v.output,d=v.newStates}o&&u.push(p)}let g;return o&&(g=Mt(u,1)),[p,g,d]})}var ar=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 Om({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 Zt({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 $a(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){qb(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.");qb(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Zt({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 Zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Cr("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=>bt([n,a])):this.states_=[bt([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>bt([n,a])):this.states_[0]=bt([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()):Ae(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=>Xt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=KI(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 Zt({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 Da){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=XI((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=bt(e.shape);return t=Ce(t,[1,2]),t=sp(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Db(t,[1,n]):t):this.cell.stateSize>1?[Db(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()===ar.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ma(a,n);return new e(Object.assign(t,{cell:r}))}};ar.className="RNN";se.registerClass(ar);var lp=class extends qe{},Lm=class extends lp{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,Jt(this.units,"units"),this.activation=hs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=su([1,cs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=su([1,cs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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=ms({ones:()=>Mn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ms({ones:()=>Mn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=tr(L(e,s),this.kernel.read()):r=tr(e,this.kernel.read()),this.bias!=null&&(r=nr(r,this.bias.read())),i!=null&&(n=L(n,i));let o=Z(r,tr(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:ds(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Lm.className="SimpleRNNCell";se.registerClass(Lm);var Nx=class extends ar{constructor(e){e.cell=new Lm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};Nx.className="SimpleRNN";se.registerClass(Nx);var zm=class extends lp{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,Jt(this.units,"units"),this.activation=hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=hs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=su([1,cs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=su([1,cs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=dt(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=ms({ones:()=>Mn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ms({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=tr(e,this.kernel.read());this.useBias&&(c=nr(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=tr(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 x=tr(L(o,a),d);l=this.activation.apply(Z(g,x));let v=Z(L(i,a),L(Z(1,St(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:ds(this.activation),recurrentActivation:ds(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};zm.className="GRUCell";se.registerClass(zm);var Tx=class extends ar{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 zm(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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)}};Tx.className="GRU";se.registerClass(Tx);var gp=class extends lp{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,Jt(this.units,"units"),this.activation=hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=hs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=wt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=kt(e.kernelRegularizer),this.recurrentRegularizer=kt(e.recurrentRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.kernelConstraint=Ht(e.kernelConstraint),this.recurrentConstraint=Ht(e.recurrentConstraint),this.biasConstraint=Ht(e.biasConstraint),this.dropout=su([1,cs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=su([1,cs([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=dt(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 fa{apply(i,o){let l=r.apply([s]),c=new hm().apply([s]),u=r.apply([s*2]);return W1(W1(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=ms({ones:()=>Mn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ms({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=tr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=L(a,i[0])),p=Z(p,tr(a,this.recurrentKernel.read())),this.useBias&&(p=nr(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:ds(this.activation),recurrentActivation:ds(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),recurrentInitializer:Et(this.recurrentInitializer),biasInitializer:Et(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),recurrentConstraint:Gt(this.recurrentConstraint),biasConstraint:Gt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};gp.className="LSTMCell";se.registerClass(gp);var Sx=class extends ar{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 gp(e),super(e)}call(e,t){return D(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,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="LSTM";se.registerClass(Sx);var Om=class extends lp{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){qb(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ui(`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(Ma(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 Kb(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]])}Xb(t)}};Om.className="StackedRNNCells";se.registerClass(Om);function ms(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>U1(t(),n),i=()=>op(s,t,a);return!r||r<=1?Xt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Xt(o.clone()))}var B4=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},YI=class extends ar{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 Zt({ndim:5})]}call(e,t){return D(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new 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=bt(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){D(()=>{if(!this.stateful)throw new Cr("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(()=>bt(r)):this.states_=[bt(r)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>bt(r)):this.states_[0]=bt(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()):Ae(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=>Xt(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=Ra(l,a[0],r,s[0],i[0]),p=Ra(c,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,p]:[u,p,n]]}};YI.className="ConvRNN2D";var Bm=class extends gp{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,Jt(this.filters,"filters"),this.kernelSize=cu(n,2,"kernelSize"),this.kernelSize.forEach(o=>Jt(o,"kernelSize")),this.strides=cu(a||1,2,"strides"),this.strides.forEach(o=>Jt(o,"strides")),this.padding=r||"valid",aa(this.padding),this.dataFormat=s||"channelsLast",Rt(this.dataFormat),this.dilationRate=cu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Jt(o,"dilationRate"))}build(e){var t;e=dt(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 fa{apply(u,p){let d=l.apply([c]),h=Ya([c]),m=l.apply([c*2]);return Rb([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 ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ms({ones:()=>Mn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Q,ie,re)=>!ie||!ie[re]?Q:L(ie[re],Q),c=l(a,o,0),u=l(a,o,1),p=l(a,o,2),d=l(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ms({ones:()=>Mn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=l(r,h,0),f=l(r,h,1),g=l(r,h,2),y=l(r,h,3),b=3,[x,v,N,T]=Pn(this.kernel.read(),i,b),[E,A,$,O]=this.useBias?Pn(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,E,this.padding),u=this.inputConv(u,v,A,this.padding),p=this.inputConv(p,N,$,this.padding),d=this.inputConv(d,T,O,this.padding);let[V,W,H,X]=Pn(this.recurrentKernel.read(),i,b);m=this.recurrentConv(m,V),f=this.recurrentConv(f,W),g=this.recurrentConv(g,H),y=this.recurrentConv(y,X);let q=this.recurrentActivation.apply(Z(c,m)),K=this.recurrentActivation.apply(Z(u,f)),J=Z(L(K,s),L(q,this.activation.apply(Z(p,g)))),te=L(this.recurrentActivation.apply(Z(d,y)),this.activation.apply(J));return[te,te,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=B4(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=$t(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?nr(r,n,this.dataFormat):r}recurrentConv(e,t){return $t(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Bm.className="ConvLSTM2DCell";se.registerClass(Bm);var Cx=class extends YI{constructor(e){let t=new Bm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Cx.className="ConvLSTM2D";se.registerClass(Cx);var Wm=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 op(()=>U1(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()}};Wm.className="Dropout";se.registerClass(Wm);var Ex=class extends Wm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ex.className="SpatialDropout1D";se.registerClass(Ex);var _x=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,Jt(this.units,"units"),this.activation=hs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=wt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=wt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ht(e.kernelConstraint),this.biasConstraint=Ht(e.biasConstraint),this.kernelRegularizer=kt(e.kernelRegularizer),this.biasRegularizer=kt(e.biasRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=dt(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=dt(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=A1(this.activation.getClassName()),r;return a!=null?r=tr(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=tr(n,this.kernel.read()),this.bias!=null&&(r=nr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:ds(this.activation),useBias:this.useBias,kernelInitializer:Et(this.kernelInitializer),biasInitializer:Et(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Gt(this.kernelConstraint),biasConstraint:Gt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};_x.className="Dense";se.registerClass(_x);var Fx=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=dt(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],us(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 pB(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Fx.className="Flatten";se.registerClass(Fx);var Ax=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=hs(e.activation)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.activation.apply(n)})}getConfig(){let e={activation:ds(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="Activation";se.registerClass(Ax);var $x=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return D(()=>(e=Re(e),uB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};$x.className="RepeatVector";se.registerClass($x);var Dx=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=us(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}};Dx.className="Reshape";se.registerClass(Dx);var Mx=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 ${e.dims} instead.`);let t=$a(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 Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=dt(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}};Mx.className="Permute";se.registerClass(Mx);var Rx=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 Lc(ss(n,this.maskValue),a)}call(e,t){return D(()=>{this.invokeCallHook(e,t);let n=Re(e),a=-1,r=!0,s=Lc(ss(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};Rx.className="Masking";se.registerClass(Rx);var Px=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(xt(e.inputLength))}this.inputDim=e.inputDim,Jt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Jt(this.outputDim,"outputDim"),this.embeddingsInitializer=wt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=kt(e.embeddingsRegularizer),this.activityRegularizer=kt(e.activityRegularizer),this.embeddingsConstraint=Ht(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),ss(e,He(e))):null)}computeOutputShape(e){if(e=dt(e),this.inputLength==null)return[...e,this.outputDim];let t=xt(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=rp(n,"int32")),V1(this.embeddings.read(),n.as1D()).reshape(dt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Et(this.embeddingsInitializer),embeddingsRegularizer:ht(this.embeddingsRegularizer),activityRegularizer:ht(this.activityRegularizer),embeddingsConstraint:Gt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Px.className="Embedding";se.registerClass(Px);var Ki=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=[dt(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=ls(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&ls(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return D(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=cs(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=sp(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],p=c.slice(1).concat([u]),d=o.reshape([u].concat(us(c.slice(1))));d=Ue(d,[1,0]),d=d.reshape(p),n.push(d),r=!0}else if(l>1){let c=$a(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($a(0,i-1));s=Ue(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=ls(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return D(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:Qn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ha(n,t[a]);return n})}},Ox=class extends Ki{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})}};Ox.className="Add";se.registerClass(Ox);var Lx=class extends Ki{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})}};Lx.className="Multiply";se.registerClass(Lx);var zx=class extends Ki{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)})}};zx.className="Average";se.registerClass(zx);var Bx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Sa(t,e[n]);return t})}};Bx.className="Maximum";se.registerClass(Bx);var Wx=class extends Ki{constructor(e){super(e)}mergeFunction(e){return D(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Pi(t,e[n]);return t})}};Wx.className="Minimum";se.registerClass(Wx);var Vx=class extends Ki{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(()=>Rb(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(Qn(t[s],-1)):a.push(t[s]);let r=Ze(a,this.axis);return Eh(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Vx.className="Concatenate";se.registerClass(Vx);function yp(e,t){for(;e<0;)e+=t;return e}function W4(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 Ux=class extends Ki{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)=>yp(r,e[s].shape.length)):a=[yp(this.axes,t.shape.length),yp(this.axes,n.shape.length)],this.normalize&&(t=Tm(t,a[0]),n=Tm(n,a[1])),W4(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[yp(this.axes,e.length),yp(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}};Ux.className="Dot";se.registerClass(Ux);var Gx=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 op(()=>dm(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Gx.className="GaussianNoise";se.registerClass(Gx);var Hx=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?op(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(dm(n.shape,1,a))},()=>n,t.training||!1):n})}};Hx.className="GaussianDropout";se.registerClass(Hx);var jx=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 op(()=>{let a=Re(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Nr(Ql(n),this.rate);o=rp(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})}};jx.className="AlphaDropout";se.registerClass(jx);function bp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=ck(e,t,n,a,r,s);else if(e.rank===3)i=pk(e,t,n,a,r,s);else if(e.rank===4)i=dk(e,t,n,a,r,s);else throw new $e(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function V4(e,t,n,a,r=.001){return D(()=>{let s=Bh(e,a),i=s.mean,o=s.variance;return[bp(e,i,o,n,t,r),i,o]})}function U4(e,t,n,a,r=.001){return D(()=>{let s=Bh(e,a),i=s.mean,o=s.variance,l=[];for(let h of $a(0,e.rank))a.indexOf(h)!==-1?l.push(1):l.push(e.shape[h]);let c=i.reshape(l),u=o.reshape(l),p=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[bp(e,c,u,d,p,r),i,o]})}function G4(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),$a(0,e.rank-1))?V4(e,t,n,a,r):U4(e,t,n,a,r)}var qx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.movingMeanInitializer=wt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=wt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ht(e.betaConstraint),this.gammaConstraint=Ht(e.gammaConstraint),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer)}build(e){e=dt(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 ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return D(()=>{let n=t.training==null?!1:t.training,a=Re(e),r=a.shape,s=r.length,i=$a(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Bi(1,s);l[o]=r[o];let c=i.slice();c.sort();let u=!k.arraysEqual(c,$a(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,x=this.scale?this.gamma.read().reshape(l):null;return bp(a,g,y,b,x,this.epsilon)}else return bp(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]=G4(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(g,y,b)=>{D(()=>{let x=1-b,v=g.read(),N=v.sub(y).mul(x);g.write(v.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:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),movingMeanInitializer:Et(this.movingMeanInitializer),movingVarianceInitializer:Et(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:Gt(this.betaConstraint),gammaConstraint:Gt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};qx.className="BatchNormalization";se.registerClass(qx);var Kx=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=wt(e.betaInitializer||"zeros"),this.gammaInitializer=wt(e.gammaInitializer||"ones"),this.betaRegularizer=kt(e.betaRegularizer),this.gammaRegularizer=kt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=dt(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!==ls(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}=Bh(n,this.axis,s),l=Bi(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),bp(n,i,o,p,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Et(this.betaInitializer),gammaInitializer:Et(this.gammaInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Kx.className="LayerNormalization";se.registerClass(Kx);function H4(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: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],na(e,a)})}var Xx=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Fa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 Zt({ndim:4})]}computeOutputShape(e){e=dt(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(()=>H4(Re(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Xx.className="ZeroPadding2D";se.registerClass(Xx);function Vm(e,t,n,a,r,s){return D(()=>{Rt(r),R1(s),aa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Fa()),s==null&&(s="max"),e=yx(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Dt(e,t,n,o):i=Zn(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,3,1,2])),i})}function JI(e,t,n,a,r,s){return D(()=>{Rt(r),R1(s),aa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Fa()),s==null&&(s="max"),e=HI(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=ib(e,t,n,o):i=jy(e,t,n,o),r==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var ZI=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(Jt(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)}`);Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,aa(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=dt(e);let t=Ra(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=sp(Re(e),2);let n=this.poolingFunction(Re(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return is(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Yx=class extends ZI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),Vm(e,t,n,a,r,"max")}};Yx.className="MaxPooling1D";se.registerClass(Yx);var Jx=class extends ZI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),Vm(e,t,n,a,r,"avg")}};Jx.className="AveragePooling1D";se.registerClass(Jx);var QI=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];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),aa(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=dt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(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}},Zx=class extends QI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),Vm(e,t,n,a,r,"max")}};Zx.className="MaxPooling2D";se.registerClass(Zx);var Qx=class extends QI{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),Vm(e,t,n,a,r,"avg")}};Qx.className="AveragePooling2D";se.registerClass(Qx);var eN=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];Jt(this.poolSize,"poolSize"),Jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),aa(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=dt(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=Ra(t,this.poolSize[0],this.padding,this.strides[0]),n=Ra(n,this.poolSize[1],this.padding,this.strides[1]),a=Ra(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}},ev=class extends eN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),JI(e,t,n,a,r,"max")}};ev.className="MaxPooling3D";se.registerClass(ev);var tv=class extends eN{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Rt(r),aa(a),JI(e,t,n,a,r,"avg")}};tv.className="AveragePooling3D";se.registerClass(tv);var tN=class extends qe{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new $e}},nv=class extends tN{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return Ct(n,1)})}};nv.className="GlobalAveragePooling1D";se.registerClass(nv);var av=class extends tN{constructor(e){super(e||{})}call(e,t){return D(()=>{let n=Re(e);return ta(n,1)})}};av.className="GlobalMaxPooling1D";se.registerClass(av);var nN=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new Zt({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}},rv=class extends nN{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};rv.className="GlobalAveragePooling2D";se.registerClass(rv);var sv=class extends nN{call(e,t){return D(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?ta(n,[1,2]):ta(n,[2,3])})}};sv.className="GlobalMaxPooling2D";se.registerClass(sv);var aN=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=Ma(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},iv=class extends aN{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=dt(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=dt(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),XI((n,a)=>[Re(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};iv.className="TimeDistributed";se.registerClass(iv);function j4(e){Vi(rB,"BidirectionalMergeMode",e)}var q4="concat",ov=class extends aN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ma(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ma(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?q4:e.mergeMode,j4(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()):Tn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=KI(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 Zt({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 Da;for(let l of s)if(l instanceof Da!==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 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l.customExecutor(new RV(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 ON(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((PN(d)||iU(d)||oU(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 lU(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 uU=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],cU=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],pU=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function PN(e){return uU.indexOf(e.op)>=0}function iU(e){return cU.indexOf(e.op)>=0}function oU(e){return pU.indexOf(e.op)>=0}var wv=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 wv(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=ON(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 lU(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 RN(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=MN(f,p,u,this._resourceManager);if(k.isPromise(g))throw new Error(`The execution of the op '${f.op}' returned a promise. 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this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},hU="?tfjs-format=file",mU="model.json",LN=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new dU}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Kt.browserHTTPRequest(e,this.loadOptions);else{let t=Kt.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Kt.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=Kt.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new wv(FN.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=FN.Instance.transformGraph(e.modelInitializer);this.initializer=new wv(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Kt.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof 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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function fU(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},OU=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},LU=class extends Qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zU=class extends Qt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},BU=class extends Qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Ae(e.value)}}},WU=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ta.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ta.getTensorsInContainer(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},VU=class extends Qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},ZN=class extends Qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ta.getTensorsInContainer(e.value),n=await this.transform(e.value),a=Ta.getTensorsInContainer(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},Nv=class extends Qt{constructor(){super();this.outputQueue=new kv,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},UU=class extends Nv{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ta.getTensorsInContainer(e.value),n=this.transform(e.value),a=Ta.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)Ta.isTensorInList(r,a)||r.dispose();return!0}},JN=class extends Qt{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}},fs;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(fs||(fs={}));var MU=class extends Qt{constructor(e,t=fs.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 Qt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await KN(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case fs.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case fs.SHORTEST:return{value:null,done:!0};case fs.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},QN=class extends Qt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new XN(t)}summary(){return`${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()}},GU=class extends QN{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=NU.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}}},pu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),zn(async()=>(await n.iterator()).columnMajorBatch(e,t,HU),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,zn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,zn(async()=>(await t.iterator()).filter(a=>D(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return zn(async()=>(await t.iterator()).map(n=>D(()=>e(n))),this.size)}mapAsync(e){let t=this;return zn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return zn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,zn(async()=>{let a=Iv(async()=>({value:await t.iterator(),done:!1}));return DU(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,zn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=IU.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()}};pu.MAX_BUFFER_SIZE=1e4;function zn(e,t=null){return new class extends pu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function gU(e){return zn(async()=>YN(e),e.length)}function yU(e){if(!du(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 KN(e,a=>{if(a instanceof pu)return{value:a.iterator(),recurse:!1};if(du(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return RU(n,fs.SHORTEST)},t)}function HU(e){if(e===null)return null;let t=e[0];return EU(t)?{value:jU(e),recurse:!1}:{value:null,recurse:!0}}function jU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof z?Mt(e):Yn(e)}var WN=class extends pu{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))}},qm='"',vp=Symbol("out"),eT=Symbol("field"),Km=Symbol("quote"),Tv=Symbol("quoteafterquote"),tT=Symbol("quoteinquote"),VN=class extends pu{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 WN(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=vp;for(let i=0;i<r;i++)switch(s){case vp:switch(e.charAt(i)){case qm:a=i+1,s=Km;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=vp;break;default:s=eT,a=i;break}break;case eT:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=vp,a=i+1;break;default:}break;case Km:switch(e.charAt(i)){case qm:s=Tv;break;default:}break;case Tv:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=vp,a=i+1;break;case qm:s=Km;break;default:s=tT;break}break;case tT:switch(e.charAt(i)){case qm:s=Km;break;default:}break;default:}if(s===Tv?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}},nT=class extends Qt{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(ee().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new nT(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),Yn(n,t)}},aT=class extends Qt{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=tt([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(ee().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 aT(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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return D(()=>{let t=e.toFloat().expandDims(0),n;n=Za.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return n.reshape(a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},rT=class{},sT=class extends Qt{split(e){return new qU(this,e)}},qU=class extends sT{constructor(e,t){super();this.upstream=e,this.impl=new KU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},KU=class extends Nv{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},YU=class extends Qt{decodeUTF8(){return new XU(this)}},XU=class extends sT{constructor(e){super();this.upstream=e,this.impl=new JU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},JU=class extends Nv{constructor(e){super();if(this.upstream=e,ee().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=rF();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return ee().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},iT=class extends YU{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(ee().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof 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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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qG={kernelName:ui,backendName:"cpu",kernelFunc:OT},LT=it(ci,e=>Math.max(0,e)),KG={kernelName:ci,backendName:"cpu",kernelFunc:LT},zT=it(di,e=>Math.min(Math.max(0,e),6)),XG={kernelName:di,backendName:"cpu",kernelFunc:zT};function Mv(e,t,n,a,r){if(n==="linear")return sr({inputs:{x:t},backend:e});if(n==="relu")return LT({inputs:{x:t},backend:e});if(n==="elu")return RT({inputs:{x:t},backend:e});if(n==="relu6")return zT({inputs:{x:t},backend:e});if(n==="prelu")return OT({inputs:{x:t,alpha:a},backend:e});if(n==="leakyrelu")return PT({inputs:{x:t},backend:e,attrs:{alpha:r}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function It(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=k.sizeFromShape(r.shape),o=k.inferFromImplicitShape(s,i),l=k.sizeFromShape(o);k.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${r.shape}) has ${i} elements. 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uH={kernelName:zd,backendName:"cpu",kernelFunc:lH};function cH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ke(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=ba({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],_.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,p]=_.computeOutAndReduceShapes(l.shape,i),d=k.sizeFromShape(u),h=k.makeZerosTypedArray(d,"int32"),m=k.sizeFromShape(p),f=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*m,b=f[y],x=0;for(let v=0;v<m;++v){let N=f[y+v];N>b&&(b=N,x=v)}h[g]=x}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var pH={kernelName:Ps,backendName:"cpu",kernelFunc:cH};function dH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ke(r,"argMin");let 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q=X-E,K=f.get(g,W,X,y);K>O&&(O=K,r?V=s?((g*a.inHeight+W)*a.inWidth+X)*a.inChannels+y:(W*a.inWidth+X)*a.inChannels+y:V=H*d+q)}}i.set(V,g,b,T,y)}}return i}function VT(e,t,n,a,r,s){let i=r.strideDepth,o=r.strideHeight,l=r.strideWidth,c=r.dilationDepth,u=r.dilationHeight,p=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,m=r.effectiveFilterWidth,f=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Le(r.outShape,n),v=x.values,N=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[2]*r.outShape[3]*r.outShape[4],E=r.outShape[3]*r.outShape[4],A=r.outShape[4];for(let $=0;$<r.batchSize;++$){let O=$*N,V=$*a[0];for(let W=0;W<r.inChannels;++W)for(let H=0;H<r.outDepth;++H){let X=H*i-f,q=X;for(;q<0;)q+=c;let K=Math.min(r.inDepth,d+X),J=O+H*T;for(let te=0;te<r.outHeight;++te){let Q=te*o-g,ie=Q;for(;ie<0;)ie+=u;let re=Math.min(r.inHeight,h+Q),ae=J+te*E;for(let oe=0;oe<r.outWidth;++oe){let he=oe*l-y,ce=he;for(;ce<0;)ce+=p;let ge=Math.min(r.inWidth,m+he),be=ae+oe*A,Ie=b,Te=0,Ee=0;for(let Je=q;Je<K;Je+=c){let nt=V+Je*a[1];for(let Ve=ie;Ve<re;Ve+=u){let ut=nt+Ve*a[2];for(let Ge=ce;Ge<ge;Ge+=p){let ct=ut+Ge*a[3],gt=e[ct+W];if(s==="max"&&gt>Ie?Ie=gt:s==="avg"&&(Te+=gt,Ee++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let Me=be+W;v[Me]=s==="avg"?Te/Ee:Ie}}}}return x}function TH(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,x=b;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+b);for(let N=0;N<t.outHeight;++N){let T=N*r-h,E=T;for(;E<0;)E+=o;let A=Math.min(t.inHeight,u+T);for(let $=0;$<t.outWidth;++$){let O=$*s-m,V=O;for(;V<0;)V+=l;let 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p=_.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,x=p.dilationHeight,v=p.dilationWidth,N=p.effectiveFilterDepth,T=p.effectiveFilterHeight,E=p.effectiveFilterWidth,A=N-1-p.padInfo.front,$=E-1-p.padInfo.left,O=T-1-p.padInfo.top,V=Le(s.shape,"float32"),W=1/(f*g*y),H=n.bufferSync(r);for(let X=0;X<p.batchSize;++X)for(let q=0;q<p.inChannels;++q)for(let K=0;K<p.inDepth;++K)for(let J=0;J<p.inHeight;++J)for(let te=0;te<p.inWidth;++te){let Q=K-A,ie=J-O,re=te-$,ae=0;for(let oe=0;oe<N;oe+=b){let he=(Q+oe)/d;if(!(he<0||he>=p.outDepth||Math.floor(he)!==he))for(let ce=0;ce<T;ce+=x){let ge=(ie+ce)/h;if(!(ge<0||ge>=p.outHeight||Math.floor(ge)!==ge))for(let be=0;be<E;be+=v){let Ie=(re+be)/m;Ie<0||Ie>=p.outWidth||Math.floor(Ie)!==Ie||(ae+=H.get(X,he,ge,Ie,q))}}}V.set(ae*W,X,K,J,te,q)}return n.makeTensorInfo(V.shape,V.dtype,V.values)}var AH={kernelName:Wd,backendName:"cpu",kernelFunc:FH};function $H(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ke([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.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,x=b-1-u.padInfo.left,v=y-1-u.padInfo.top,N=Le(i.shape,"float32"),T=1/(h*m),E=n.data.get(r.dataId).values,A=Le(r.shape,"float32",E);for(let $=0;$<u.batchSize;++$)for(let O=0;O<u.inChannels;++O)for(let V=0;V<u.inHeight;++V)for(let W=0;W<u.inWidth;++W){let H=V-v,X=W-x,q=0;for(let K=0;K<y;K+=f){let J=(H+K)/p;if(!(J<0||J>=u.outHeight||Math.floor(J)!==J))for(let te=0;te<b;te+=g){let Q=(X+te)/d;Q<0||Q>=u.outWidth||Math.floor(Q)!==Q||(q+=A.get($,J,Q,O))}}N.set(q*T,$,V,W,O)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var DH={kernelName:Bd,backendName:"cpu",kernelFunc:$H};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var RH={kernelName:Xs,backendName:"cpu",kernelFunc:MH};function PH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ke([r],"batchToSpaceND");let o=s.reduce((y,b)=>y*b),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=It({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ba({inputs:{x:h},backend:n,attrs:{perm:c}}),f=It({inputs:{x:m},backend:n,attrs:{shape:u}}),g=Yi({inputs:{x:f},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),g}var OH={kernelName:mc,backendName:"cpu",kernelFunc:PH};function LH(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=Sv(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var zH={kernelName:Vd,backendName:"cpu",kernelFunc:LH},BH=it(Kr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),WH={kernelName:Kr,backendName:"cpu",kernelFunc:BH},VH=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")},UH={kernelName:fc,backendName:"cpu",kernelFunc:VH};function fu(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 GH={kernelName:nh,backendName:"cpu",kernelFunc:fu};function gu(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.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 HH={kernelName:qo,backendName:"cpu",kernelFunc:gu};function UT(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;ke([r,s],"conv2d");let p=_.convertConv2DDataFormat(l),d=_.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,x=d.dataFormat==="channelsLast",v=new Bt(d.outShape,r.dtype),N=k.computeStrides(r.shape),T=k.computeStrides(s.shape),E=N[0],A=x?N[1]:N[2],$=x?N[2]:1,O=x?1:N[1],V=v.strides[0],W=x?v.strides[1]:v.strides[2],H=x?v.strides[2]:1,X=x?1:v.strides[1],q=n.data.get(r.dataId).values,K=n.data.get(s.dataId).values,J=v.values;for(let te=0;te<d.batchSize;++te){let Q=te*E,ie=te*V;for(let re=0;re<d.outHeight;++re){let ae=ie+re*W,oe=re*d.strideHeight-b;for(let he=0;he<h;++he){let ce=oe+he*f;if(ce<0||ce>=d.inHeight)continue;let ge=he*T[0],be=Q+ce*A;for(let 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YH={kernelName:Ws,backendName:"cpu",kernelFunc:XH};function JH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a;ke([r,s],"conv3d");let c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:g}=c,y=g.front,b=g.left,x=g.top,v=new Bt(c.outShape,r.dtype),N=n.data.get(r.dataId).values,T=n.data.get(s.dataId).values,E=v.values,A=k.computeStrides(r.shape),$=k.computeStrides(s.shape);for(let O=0;O<c.batchSize;++O){let V=O*A[0],W=O*v.strides[0];for(let H=0;H<c.outDepth;++H){let X=W+H*v.strides[1],q=H*c.strideDepth-y;for(let K=0;K<u;++K){let J=q+K*h;if(J<0||J>=c.inDepth)continue;let te=K*$[0],Q=V+J*A[1];for(let ie=0;ie<c.outHeight;++ie){let re=X+ie*v.strides[2],ae=ie*c.strideHeight-x;for(let oe=0;oe<p;++oe){let he=ae+oe*m;if(he<0||he>=c.inHeight)continue;let ce=te+oe*$[1],ge=Q+he*A[2];for(let be=0;be<c.outWidth;++be){let 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y6={kernelName:Kd,backendName:"cpu",kernelFunc:g6};function b6(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;ke([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),d=k.computeStrides(s.shape),h=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),m=new Bt(h.inShape,"float32"),f=m.values,[g,y,b]=m.strides,x=n.data.get(r.dataId).values,[v,N,T]=p,E=n.data.get(s.dataId).values,[A,$,O]=d,{batchSize:V,filterHeight:W,filterWidth:H,inChannels:X,inHeight:q,inWidth:K,outChannels:J,outHeight:te,outWidth:Q,strideHeight:ie,strideWidth:re}=h,ae=W-1-h.padInfo.top,oe=H-1-h.padInfo.left,he=J/X;for(let ce=0;ce<V;++ce)for(let ge=0;ge<X;++ge)for(let be=0;be<q;++be){let Ie=be-ae,Te=Math.max(0,Math.ceil(Ie/ie)),Ee=Math.min(te,(W+Ie)/ie);for(let Me=0;Me<K;++Me){let Je=Me-oe,nt=Math.max(0,Math.ceil(Je/re)),Ve=Math.min(Q,(H+Je)/re),ut=0;for(let Ge=Te;Ge<Ee;++Ge){let ct=Ge*ie-Ie;for(let gt=nt;gt<Ve;++gt){let 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XT(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=Xi({inputs:{input:a},backend:n}),s=XT({inputs:{x:r},backend:n}),i=fu({inputs:{input:a},backend:n}),o=Qm({inputs:{x:i},backend:n}),l=Bn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return zv({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var y5={kernelName:vl,backendName:"cpu",kernelFunc:XT};function YT(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Jm({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 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t.makeTensorInfo([o.length],s,o)}var N5={kernelName:Nc,backendName:"cpu",kernelFunc:I5},T5=it(Il,e=>1/e),S5={kernelName:Il,backendName:"cpu",kernelFunc:T5};function C5(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ke(r,"resizeBilinear");let l=k.computeStrides(r.shape),[c,u]=o,[p,d,h,m]=r.shape,f=n.data.get(r.dataId).values,g=new Float32Array(k.sizeFromShape([p,c,u,m])),y=[s&&c>1?d-1:d,s&&u>1?h-1:h],b=[s&&c>1?c-1:c,s&&u>1?u-1:u],x=0,v=y[0]/b[0],N=y[1]/b[1];for(let T=0;T<p;T++)for(let E=0;E<c;E++){let A;i?A=v*(E+.5)-.5:A=v*E;let $=Math.max(0,Math.floor(A)),O=A-$,V=Math.min(d-1,Math.ceil(A)),W=T*l[0]+$*l[1],H=T*l[0]+V*l[1];for(let X=0;X<u;X++){let q;i?q=N*(X+.5)-.5:q=N*X;let K=Math.max(0,Math.floor(q)),J=q-K,te=Math.min(h-1,Math.ceil(q)),Q=W+K*l[2],ie=H+K*l[2],re=W+te*l[2],ae=H+te*l[2];for(let oe=0;oe<m;oe++){let he=f[Q+oe],ce=f[ie+oe],ge=f[re+oe],be=f[ae+oe],Ie=he+(ge-he)*J,Te=ce+(be-ce)*J,Ee=Ie+(Te-Ie)*O;g[x++]=Ee}}}return n.makeTensorInfo([p,c,u,m],"float32",g)}var E5={kernelName:pi,backendName:"cpu",kernelFunc:C5};function _5(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ke([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[l,c,u,p]=r.shape,[,d,h]=s.shape,m=new Float32Array(l*c*u*p),f=[i&&d>1?c-1:c,i&&h>1?u-1:u],g=[i&&d>1?d-1:d,i&&h>1?h-1:h],y=f[0]/g[0],b=f[1]/g[1],x=n.data.get(s.dataId).values,v=0;for(let N=0;N<l;N++){let T=N*o[0];for(let E=0;E<d;E++){let A=E*y,$=Math.floor(A),O=Math.min(Math.ceil(A),c-1),V=T+$*o[1],W=T+O*o[1],H=A-$,X=1-H;for(let q=0;q<h;q++){let K=q*b,J=Math.floor(K),te=Math.min(Math.ceil(K),u-1),Q=K-J,ie=1-Q,re=V+J*o[2],ae=V+te*o[2],oe=W+J*o[2],he=W+te*o[2],ce=X*ie,ge=X*Q,be=H*ie,Ie=H*Q;for(let Te=0;Te<p;Te++){let Ee=x[v++];m[re+Te]+=Ee*ce,m[ae+Te]+=Ee*ge,m[oe+Te]+=Ee*be,m[he+Te]+=Ee*Ie}}}}return n.makeTensorInfo([l,u,c,p],"float32",m)}var F5={kernelName:ph,backendName:"cpu",kernelFunc:_5};function 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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 bu(e,t=2){return k.sizeFromShape(e.slice(0,e.length-t))}function xu(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 Gv(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[bu(e),...xu(e)]),t}function i8(e,t=!1){let n=ee().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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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)));
}
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int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}var aS=`
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;
}
`,m8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=kp.DENSE;let t=Np(e),n=gn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Zi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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;
}
`}},f8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=kp.DENSE;let t=Np(e),n=gn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${Zi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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;
}
`}},g8=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ra.DOWNLOAD;let t=gn();this.outputShape=e,this.userCode=`
${aS}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},y8=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ra.DOWNLOAD;let t=gn();this.outputShape=e,this.userCode=`
${aS}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},b8=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=gn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${Kv(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.);
}
`}},x8=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=gn(),[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=`
${Kv(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${a.output} = ${o};
}
`}};function v8(e){let t=gn(),n=`${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 Uq(e,n)}function w8(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 Xq(e,t)}function k8(e){let t=new Uint16Array([0,1,2,2,1,3]);return Yq(e,t)}function Sp(e,t,n,a,r,s){Zq(t,n);let i=Jq(e),o=e.TEXTURE_2D;return Se(e,()=>e.bindTexture(o,i)),Se(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Se(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Se(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),Se(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Se(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function rS(e){return e.internalFormatFloat}function I8(e,t,n,a){let[r,s]=Ip(t,n);return Sp(e,r,s,rS(a),a.textureFormatFloat,e.FLOAT)}function sS(e){return e.internalFormatHalfFloat}function N8(e,t,n,a){let[r,s]=Ip(t,n);return Sp(e,r,s,sS(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function iS(e){return e.downloadTextureFormat}function T8(e,t,n,a){let[r,s]=Ip(t,n);return Sp(e,r,s,iS(a),e.RGBA,e.UNSIGNED_BYTE)}function oS(e){return e.internalFormatPackedFloat}function S8(e,t,n,a){let[r,s]=yu(t,n);return Sp(e,r,s,oS(a),e.RGBA,e.FLOAT)}function lS(e){return e.internalFormatPackedHalfFloat}function C8(e,t,n,a){let[r,s]=yu(t,n);return Sp(e,r,s,lS(a),e.RGBA,a.textureTypeHalfFloat)}function E8(e,t,n){let a=0,r=3*4,s=3*4+2*4;return Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),eS(e,t,"clipSpacePos",n,3,s,a)&&eS(e,t,"uv",n,2,s,r)}function _8(e,t,n,a,r,s){Se(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),Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,a,0,e.RGBA,o,i)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function F8(e,t,n){Se(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Se(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Se(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function A8(e,t,n,a){let r=e.createBuffer();Se(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return Se(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),Se(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Se(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function $8(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 D8(e,t,n,a){let[r,s]=Ip(t,n),i=4,o=new Uint8Array(Pq(t*n,i));return Se(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function M8(e,t,n,a,r,s,i,o){let l=e,c=new Float32Array(Oq(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 R8(e,t,n){let a=new Float32Array(t*n*4);return Se(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var O8=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=ee().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Dq(t,e)):this.gl=ir(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(ee().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=ef(this.gl,r),xa(this.gl,s))this.textureHalfFloatExtension=ef(this.gl,s);else if(ee().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),xa(this.gl,a))this.colorBufferHalfFloatExtension=ef(this.gl,a);else if(ee().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",xa(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(xa(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=w8(this.gl),this.indexBuffer=k8(this.gl),this.framebuffer=Qq(this.gl),this.textureConfig=Wv(this.gl,this.textureHalfFloatExtension)}get debug(){return ee().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Se(e,()=>e.finish()),Se(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Se(e,()=>e.deleteFramebuffer(this.framebuffer)),Se(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Se(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Se(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),I8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),N8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),T8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),F8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),_8(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),C8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),S8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(tS(this.gl,this.framebuffer),this.outputTexture=null),Se(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>D8(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return M8(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return $8(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=A8(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(ee().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 ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>R8(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Hq(t,e),a=v8(t),r=qq(t);return Se(t,()=>t.attachShader(r,a)),Se(t,()=>t.attachShader(r,n)),Kq(t,r),this.debug&&Vv(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&&Se(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Vv(this.gl,this.program),Se(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?n8(this.gl,e,t):a8(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Se(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(),r8(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=yu(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&&Vv(this.gl,this.program),tf(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Se(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Se(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=ef(this.gl,ee().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(ee().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(ee().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 k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,ee().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=P8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Uv(this.gl,e,this.framebuffer),this.debug&&tf(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Uv(this.gl,this.outputTexture,this.framebuffer),this.debug&&tf(this.gl)):tS(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Uv(a,e,this.framebuffer),this.debug&&tf(a),this.outputTexture=e,Se(a,()=>a.viewport(0,0,t,n)),Se(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),Se(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 P8(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:uS}=_;function j8(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=>L8(h,t,a)).join(`
`),o=t.texShape,l=gn(),c=W8(l),u,p,d=G8(l);return t.isPacked?(u=z8(t.logicalShape,o),p=U8(l)):(u=B8(t.logicalShape,o),p=V8(l)),a&&(d+=H8),[d,c,p,s,u,i,n].join(`
`)}function vu(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return q8(e);case 1:return K8(e);case 2:return X8(e);case 3:return Y8(e);case 4:return J8(e);case 5:return Z8(e);case 6:return Q8(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function cS(e){switch(e.shapeInfo.logicalShape.length){case 0:return eK(e);case 1:return tK(e);case 2:return nK(e);case 3:return aK(e);default:return rK(e)}}function L8(e,t,n=!1){let a="";n?a+=cS(e):a+=vu(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=sK(e,t):a+=iK(e,t)),a}function z8(e,t){switch(e.length){case 0:return pS();case 1:return oK(e,t);case 2:return cK(e,t);case 3:return lK(e,t);default:return uK(e,t)}}function B8(e,t){switch(e.length){case 0:return pS();case 1:return pK(e,t);case 2:return gK(e,t);case 3:return dK(e,t);case 4:return hK(e,t);case 5:return mK(e,t);case 6:return fK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function W8(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function V8(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function U8(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function G8(e){return`${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);
}
${yK}
${bK}
${xK}
`}var yK=`
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);
}
`,bK=`
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);
}
`,xK=`
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);
}
`,H8=`
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 pS(){return`
int getOutputCoords() {
return 0;
}
`}function oK(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 pK(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 * ${t[1]} + resTexRC.y;
}
`}function lK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${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, ${a}) * 2;
return ivec3(b, r, c);
}
`}function dK(e,t){let n=Zi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function uK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${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}(${o});
}
`}function hK(e,t){let n=Zi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function mK(e,t){let n=Zi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function fK(e,t){let n=Zi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function cK(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${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, ${a}) * 2;
return ivec2(r, c);
}
`}function gK(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
return ivec2(r, c);
}
`}function Qi(e){return`offset${e}`}function eK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=gn();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function q8(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=Qi(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function tK(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=gn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${r[0]}, ${r[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function K8(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${wu(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=Qi(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 nK(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=gn();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 X8(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=ku(e,o),d=["row","col"];return`
${vu(p)}
float ${a}(int row, int col) {
return ${a}(${Iu(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${wu(e)}
}
`;let l=r[0],c=r[1],u=Qi(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 aK(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=ku(e,p),m=["b","row","col"];return`
${cS(h)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${Iu(m,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=gn();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 Y8(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=ku(e,l),f=["row","col","depth"];return`
${vu(m)}
float ${a}(int row, int col, int depth) {
return ${a}(${Iu(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)));
${wu(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=Qi(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 rK(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=gn();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 J8(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=ku(e,o),f=["row","col","depth","depth2"];return`
${vu(m)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${Iu(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)));
${wu(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=Qi(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 Z8(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=ku(e,l),g=["row","col","depth","depth2","depth3"];return`
${vu(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${Iu(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;
${wu(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=Qi(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 Q8(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=ku(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${vu(g)}
float ${a}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${a}(${Iu(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)));
${wu(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=Qi(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 wu(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 ${t}[i];
}
}
`}function sK(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=uS(e.shapeInfo.logicalShape,t.logicalShape),l=mt(i),c=i-s,u,p=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(g=>`coords.${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 iK(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=mt(l),u=uS(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}(${m});
}
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${Kv(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
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int cols = ${e[2]};
${n}
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${t}
}
void main() {
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float y = unaryOperation(x);
setOutput(y);
}
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return (x < 0.0) ? 0.0 : min(6.0, x);
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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;
`,dX=`
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) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
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vec4 packedInput = getA(${r});
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}
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Please use tf.complex(real, imag).");let a={};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:ra.UPLOAD,refCount:1,complexParentRefCount:0}),a}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a){if(ee().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. 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vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
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result.r = isNaN.r > 0. ? NAN : result.r;
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${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",x="rc.x";e[0]<t[0]?b=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`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 = ${x};
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;"},_S=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=_.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},FS="return a * b;";function AS(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=_.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),c=new _S(ES.REAL,a.shape,r.shape),u=new _S(ES.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(c,p,"float32"),h=n.runWebGLProgram(u,p,"float32"),m=bs({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),l=n.texData.get(r.dataId),[c,u]=LK(a.shape,r.shape,o.values,l.values,s),p=n.makeTensorInfo(u,s),d=n.texData.get(p.dataId);return d.values=c,p}let i;return ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Cp(FS,a.shape,r.shape):i=new Tu(FS,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var DX={kernelName:si,backendName:"webgl",kernelFunc:AS};function MX(e,t,n){let a=[bu(e.shape),...xu(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[bu(t),...xu(t)],i=new gS(s,a),o=!0,l=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),l=k.inferFromImplicitShape(s,o),c=k.sizeFromShape(l);k.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(r.dataId);return u.isPacked&&!af(r.shape,l)&&!(u.texture!==null&&af(u.shape,l))?MX(r,l,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var RX={kernelName:Nl,backendName:"webgl",kernelFunc:xe},$S=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);
${l}
}
setOutput(sumValue);
}
`}},PX=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 OX(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=_.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function eo(e,t,n,a){let r=OX(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 $S({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new $S({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new PX({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 zX=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=mt(this.rank),r=LX(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function LX(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 BX=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 ${this.rank} is not yet supported.`);let a=mt(this.rank),r=fS("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=r[c];let i=`vec2(${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] = ${l};
}
}
setOutput(result);
}
`}};function lf(e,t,n){let a=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new BX(e.shape,t):new zX(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function WX(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,l=_.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=lf(e,l,a),o=_.getInnerMostAxes(o.length,s)),_.assertAxesAreInnerMostDims("sum",o,s);let[p,d]=_.computeOutAndReduceShapes(u.shape,o),h=p;n&&(h=_.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(d),f=k.sizeFromShape(e.shape)/m,g=xe({inputs:{x:u},attrs:{shape:[f,m]},backend:a}),y=xh(e.dtype),b=eo(g,y,"sum",a),x=xe({inputs:{x:b},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(b),c&&a.disposeIntermediateTensorInfo(u),x}function Jv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return WX(r,s,i,n)}var VX={kernelName:xi,backendName:"webgl",kernelFunc:Jv};function En(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=r.shape[s[u]];let c;if(i.shouldExecuteOnCPU([r])){let u=i.texData.get(r.dataId).values,p=Xv(u,r.shape,r.dtype,s,l);c=i.makeTensorInfo(l,r.dtype);let d=i.texData.get(c.dataId);d.values=p}else c=lf(r,s,i);return c}var UX={kernelName:Ni,backendName:"webgl",kernelFunc:En},DS=1e3;function uf({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,p=n?e.shape[c-2]:e.shape[c-1],d=a?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],m=a?t.shape[u-2]:t.shape[u-1],f=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=k.sizeFromShape(f),b=k.sizeFromShape(g),x=y===b||y===1||b===1;k.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${g}).`);let v=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let N=n?[y,p,h]:[y,h,p],T=a?[b,m,d]:[b,d,m],E=xe({inputs:{x:e},backend:r,attrs:{shape:N}}),A=xe({inputs:{x:t},backend:r,attrs:{shape:T}}),$=[E,A],O=Math.max(y,b),V=n?E.shape[1]:E.shape[2],W=s!=null,H=i!=null,X=l==="leakyrelu",q=l!=null?of(l,!0):null,K=W||H||X||q!=null,J;if((h===1||m===1)&&V>DS&&K===!1){let Q=E,ie=A;n&&(Q=En({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),$.push(Q)),a&&(ie=En({inputs:{x:A},backend:r,attrs:{perm:[0,2,1]}}),$.push(ie));let re=m!==1,ae=m===1,oe=Q;re&&(oe=xe({inputs:{x:Q},backend:r,attrs:{shape:[O,V,1]}}),$.push(oe));let he=m===1?2:1,ce=ie;ae&&(ce=xe({inputs:{x:ie},backend:r,attrs:{shape:[O,1,V]}}),$.push(ce));let ge=AS({inputs:{a:oe,b:ce},backend:r});J=Jv({inputs:{x:ge},backend:r,attrs:{axis:he,keepDims:!0}}),$.push(ge)}else{let Q=pa(e.dtype,t.dtype),ie=new CS(N,T,[O,h,m],n,a,W,q,H,X),re=[E,A];if(s!=null&&re.push(s),H&&re.push(i),X){let ae=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));re.push(ae),$.push(ae)}J=r.runWebGLProgram(ie,re,Q)}let te=xe({inputs:{x:J},backend:r,attrs:{shape:v}});$.push(J);for(let Q of $)r.disposeIntermediateTensorInfo(Q);return te}function GX(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:p}=a;return uf({a:r,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:u})}var HX={kernelName:Ti,backendName:"webgl",kernelFunc:GX},MS="return abs(x);";function jX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=mS(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Nu(a.shape,MS):r=new ys(a.shape,MS),n.runWebGLProgram(r,[a],a.dtype)}var qX={kernelName:Lo,backendName:"webgl",kernelFunc:jX},KX=Oa+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,XX=Ye({opSnippet:KX}),YX={kernelName:zo,backendName:"webgl",kernelFunc:XX},JX=Oa+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,ZX=Ye({opSnippet:JX}),QX={kernelName:Bo,backendName:"webgl",kernelFunc:ZX},RS="return a + b;",eY=on({opSnippet:RS,packedOpSnippet:RS,supportsComplex:!0,cpuKernelImpl:IK}),tY={kernelName:qr,backendName:"webgl",kernelFunc:eY},nY=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 = ${a};
setOutput(result);
}
`}},aY=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${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 cf(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Wn({inputs:{x:a[0]},backend:n});if(a.length>ee().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),l=cf({inputs:a.slice(0,o),backend:n}),c=cf({inputs:a.slice(o),backend:n});return cf({inputs:[l,c],backend:n})}let r=a.map(o=>o.dtype).reduce((o,l)=>pa(o,l)),s=a.map(o=>o.shape),i=ee().getBool("WEBGL_PACK")?new aY(a[0].shape,s):new nY(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var rY={kernelName:Rs,backendName:"webgl",kernelFunc:cf};function sY(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=_.getAxesPermutation(c,o),p=r;u!=null&&(p=En({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("all",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=eo(f,f.dtype,"all",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=xe({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=xe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var iY={kernelName:Ld,backendName:"webgl",kernelFunc:sY};function oY(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=_.getAxesPermutation(c,o),p=r;u!=null&&(p=En({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,o)),_.assertAxesAreInnerMostDims("any",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=eo(f,f.dtype,"any",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=xe({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=xe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var lY={kernelName:zd,backendName:"webgl",kernelFunc:oY},uY=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 ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},cY=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${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=mt(o),c=yn("coords",o),u,p;if(s===1){p=o+1;let E=mt(p);u=`
${E} sourceLocR = ${E}(${c.join()}, 0);
++${c[o-1]};
${E} sourceLocG = ${E}(${c.join()}, 0);
++${c[o-2]};
${E} sourceLocA = ${E}(${c.join()}, 0);
--${c[o-1]};
${E} sourceLocB = ${E}(${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(E=>"int "+E),f=yn("sourceLocR",p-1).concat("inIdx.r"),g=yn("sourceLocG",p-1).concat("inIdx.g"),y=yn("sourceLocB",p-1).concat("inIdx.b"),b=yn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=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;
${v}
vec4 candidate = ${N};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(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 PS(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=_.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},l=new uY(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=PS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),p}function OS(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=_.computeOptimalWindowSize(s),o=new cY(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=OS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function LS(e,t,n,a){let r=[n];if(_.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!ee().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=_.computeOutAndReduceShapes(t.shape,r),l=k.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=PS(e,c,a);s.push(u);let p=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),p}return OS(e,t,a)}function pY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=En({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=LS(n,l,i[0],"max");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var dY={kernelName:Ps,backendName:"webgl",kernelFunc:pY};function hY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=_.getAxesPermutation(i,r.shape.length),l=r,c=[];o!=null&&(l=En({inputs:{x:r},backend:n,attrs:{perm:o}}),c.push(l),i=_.getInnerMostAxes(i.length,l.shape.length)),_.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=LS(n,l,i[0],"min");return c.forEach(p=>n.disposeIntermediateTensorInfo(p)),u}var mY={kernelName:dc,backendName:"webgl",kernelFunc:hY},fY=Oa+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,gY=Ye({opSnippet:fY}),yY={kernelName:Wo,backendName:"webgl",kernelFunc:gY},bY=Oa+"return log(x + sqrt(x * x + 1.0));",xY=Ye({opSnippet:bY}),vY={kernelName:Vo,backendName:"webgl",kernelFunc:xY},wY=Oa+`
return atan(x);
`,kY=Ye({opSnippet:wY}),IY={kernelName:Uo,backendName:"webgl",kernelFunc:kY},NY=AX+`
return atan(a, b);
`,TY=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+$X+`
return result;
`,SY=on({opSnippet:NY,packedOpSnippet:TY}),CY={kernelName:Ho,backendName:"webgl",kernelFunc:SY},EY=Oa+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_Y=Ye({opSnippet:EY}),FY={kernelName:Go,backendName:"webgl",kernelFunc:_Y},Ep=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) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
const ivec2 strides = ivec2(${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 ${E} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:g:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=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 < ${v}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${T}
}
int xC = xCCorner + ${v};
if (${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
);
${T}
}
}
setOutput(${x});
}
`}},Zv=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",x="0.0";if(b||(x="-1.0 / 1e-20"),n){let $=">=";this.userCode=`
const ivec3 strides =
ivec3(${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 ${$} 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 * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let v="max",N=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(N="avgValue / count");let T=Math.floor(s/4)*4,E=s%4,A=`
if (${b}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${v}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${f}, ${g}, ${y});
const float initializationValue = ${x};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
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)
);
${A}
}
int xC = xCCorner + ${T};
if (${E===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${A}
} else if (${E===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${A}
} else if (${E===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
);
${A}
}
}
setOutput(${N});
}
}
`}};function AY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Tp(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.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 Ep(u,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var $Y={kernelName:Os,backendName:"webgl",kernelFunc:AY};function DY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=a,u=[1,1,1],p=_.computePool3DInfo(r.shape,s,i,u,o,l,c),d=new Zv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var MY={kernelName:hc,backendName:"webgl",kernelFunc:DY},RY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},PY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=u-1-e.padInfo.front,m=p-1-e.padInfo.top,f=d-1-e.padInfo.left,g=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${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 OY(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=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new PY(d);return n.runWebGLProgram(h,[r],i.dtype)}var LY={kernelName:Wd,backendName:"webgl",kernelFunc:OY};function zY(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Tp([r,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=a,u=_.computePool2DInfo(i.shape,o,l,1,c),p=new RY(u);return n.runWebGLProgram(p,[r],i.dtype)}var BY={kernelName:Bd,backendName:"webgl",kernelFunc:zY};function WY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return uf({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var VY={kernelName:Ls,backendName:"webgl",kernelFunc:WY},UY=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(_.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)));
}
`}},GY=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],_.assertAndGetBroadcastShape(e,t),_.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(_.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(_.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);
}
`}},HY=({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=ee().getBool("WEBGL_PACK_NORMALIZATION")?new GY(a.shape,r.shape,s.shape,u,p,l):new UY(a.shape,r.shape,s.shape,u,p,l);return t.runWebGLProgram(d,c,c[0].dtype)},jY={kernelName:Xs,backendName:"webgl",kernelFunc:HY},KY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=mt(this.rank),n=`uniform int start[${this.rank}];`,a=qY(this.rank),r,s=e.map((i,o)=>`sourceLoc.${Qv[o]} = start[${o}] + coords.${Qv[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)}}},Qv=["x","y","z","w","u","v"];function qY(e){if(e===1)return"sourceLoc";if(e<=6)return Qv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var XY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=mt(this.rank),n=yn("coords",this.rank),a=yn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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 YY(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.shape=n,i.dtype=e.dtype;let o=hn.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]=hn.parseSliceParams(r,s,i);if(hn.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=UK(p.values,o,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:c}=n.texData.get(r.dataId),u=hn.isSliceContinous(r.shape,o,l);if(c||!u){let p=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XY(l):new KY(l),d=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),YY(r,o,l,n)}var JY={kernelName:El,backendName:"webgl",kernelFunc:_p},ZY=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,x)=>b*x),l=_.getReshaped(r.shape,s,o),c=_.getPermuted(l.length,s.length),u=_.getReshapedPermuted(r.shape,s,o),p=_.getSliceBeginCoords(i,s.length),d=_.getSliceSize(u,i,s.length),h=[],m=xe({inputs:{x:r},backend:n,attrs:{shape:l}}),f=En({inputs:{x:m},backend:n,attrs:{perm:c}}),g=xe({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},QY={kernelName:mc,backendName:"webgl",kernelFunc:ZY};function e7(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=hS(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var t7={kernelName:Vd,backendName:"webgl",kernelFunc:e7},n7="return float(a != b);",zS=on({opSnippet:n7,dtype:"bool"}),a7={kernelName:gl,backendName:"webgl",kernelFunc:zS};function Fp(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 r7={kernelName:uh,backendName:"webgl",kernelFunc:Fp},s7="return float(int(x));";function i7(e,t){let n=new ys(e.shape,s7),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function ew(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=bt(r.shape),o=ew({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=bs({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(r.dtype==="complex64"){let i=Fp({inputs:{input:r},backend:n}),o=ew({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 i7(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=zS({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 o7={kernelName:zs,backendName:"webgl",kernelFunc:ew},BS="return ceil(x);",l7=Ye({opSnippet:BS,packedOpSnippet:BS,cpuKernelImpl:TK}),u7={kernelName:jo,backendName:"webgl",kernelFunc:l7},c7=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)}}},p7=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 d7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;ee().getBool("WEBGL_PACK_CLIP")?o=new p7(r.shape):o=new c7(r.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,l)}var h7={kernelName:Kr,backendName:"webgl",kernelFunc:d7},m7=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 WS(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function f7(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new m7(a.shape),i=[WS(a,r.complexTensorInfos.real),WS(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var g7={kernelName:fc,backendName:"webgl",kernelFunc:f7},y7=class{constructor(e){this.outputShape=[],this.outputShape=_.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;
${n.join(`
`)}
}
`}},b7=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=_.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=mt(a),s=yn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${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}(${pf(i,l,f)}),
vec2(${pf(c,l,f)}));
}`}let d=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${d}(${pf(i,l,h)}),
vec2(${pf(c,l,h)}));`,this.userCode=`
float getValue(${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 pf(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function df(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 x7={kernelName:nh,backendName:"webgl",kernelFunc:df};function Su(e,t,n){let a=e[0].dtype;if(a==="complex64"){let c=e.map(m=>Fp({inputs:{input:m},backend:n})),u=e.map(m=>df({inputs:{input:m},backend:n})),p=Su(c,t,n),d=Su(u,t,n),h=bs({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}=VS(e,t,n),p=c.map(g=>({vals:n.readSync(g.dataId),shape:g.shape})),d=c[0].shape[0]===1,h=SK(p,u,a,d),m=_.computeOutShape(e.map(g=>g.shape),t),f=n.makeTensorInfo(m,a,h);return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),f}if(e.length>ee().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Su(e.slice(0,c),t,n),p=Su(e.slice(c),t,n),d=Su([u,p],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),d}if(ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new b7(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,a)}let{tensors2D:r,outShape:s}=VS(e,t,n),i=new y7(r.map(c=>c.shape)),o=n.runWebGLProgram(i,r,a);r.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function VS(e,t,n){let a=_.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>xe({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function US(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=_.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 _.assertParamsConsistent(l,s),Su(o,s,n)}var v7={kernelName:qo,backendName:"webgl",kernelFunc:US},GS=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,x="",v="";n&&(a?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,v="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=`
${x}
const ivec2 strides = ivec2(${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}
${v}
setOutput(result);
}
`}},w7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${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);
}
`}},k7=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=gn(),g=p==="channelsLast",y=g?0:1,b=g?1:2,x="";for(let v=0;v<=1;v++)for(let N=0;N<=1;N++)x+=`
blockIndex = rc.y + ${N};
pos = rc.x + ${v};
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[${v*2+N}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${v*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;
${x}
${f.output} = result;
}
`}};function HS({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>DS,x=l[2]%2!=0&&!!c.isPacked;if(b||!ee().getBool("WEBGL_LAZILY_UNPACK")||!ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],N=xe({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),E=uf({a:N,b:T,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=xe({inputs:{x:E},backend:a,attrs:{shape:n.outShape}}),y.push(N),y.push(T),y.push(E)}else{let v=h?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),N={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},T=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,k.assert(af(c.shape,N.shape),()=>`packed reshape ${c.shape} to ${N.shape} isn't free`);let E=xe({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(E);let A=uf({a:N,b:E,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),$=a.texData.get(A.dataId);k.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=T,$.shape=n.outShape,g=Wn({inputs:{x:A},backend:a}),g.shape=n.outShape,y.push(A)}for(let v of y)a.disposeIntermediateTensorInfo(v);return g}function jS({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,x=!1,v=[],N=xe({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(N),v.push(T);let E=new k7(y,N.shape,n),A=a.runWebGLProgram(E,[N],"float32"),$=xe({inputs:{x:A},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(A),v.push($);let O=r!=null,V=s!=null,W=o==="leakyrelu",H=o?of(o,!0):null,X=new CS($.shape,T.shape,[1,g,n.outChannels],b,x,O,H,V,W),q=[$,T];if(r&&q.push(r),V&&q.push(s),W){let Q=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));q.push(Q),v.push(Q)}let K=a.runWebGLProgram(X,q,"float32"),J=m?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],te=xe({inputs:{x:K},backend:a,attrs:{shape:J}});v.push(K);for(let Q of v)a.disposeIntermediateTensorInfo(Q);return te}function I7(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=_.convertConv2DDataFormat(l),d=_.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=HS({x:r,filter:s,convInfo:d,backend:n});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=jS({x:r,filter:s,convInfo:d,backend:n});else{let f=new GS(d);h=n.runWebGLProgram(f,[r,s],"float32")}let m=xe({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),m}var N7={kernelName:Bs,backendName:"webgl",kernelFunc:I7},T7=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);
}
`}},S7=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);
}
`}},C7=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 _7(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=_.convertConv2DDataFormat(l),d=_.computeConv2DInfo(r.shape,u,i,1,o,c,!1,p),h=new T7(d);return n.runWebGLProgram(h,[r,s],"float32")}var F7={kernelName:Gd,backendName:"webgl",kernelFunc:_7};function A7(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=_.convertConv2DDataFormat(c),d=_.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),h=new S7(d);return n.runWebGLProgram(h,[r,s],"float32")}var $7={kernelName:Ws,backendName:"webgl",kernelFunc:A7};function D7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeConv3DInfo(r.shape,s.shape,i,l,o),u=new w7(c);return n.runWebGLProgram(u,[r,s],"float32")}var M7={kernelName:gc,backendName:"webgl",kernelFunc:D7};function R7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:l}=a,c=_.computeConv3DInfo(r.shape,l,i,1,o),u=new C7(c);return n.runWebGLProgram(u,[r,s],"float32")}var P7={kernelName:Hd,backendName:"webgl",kernelFunc:R7};function O7(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:l}=a,c=_.computeConv3DInfo(l,s.shape,o,1,i),u=new E7(c);return n.runWebGLProgram(u,[r,s],"float32")}var L7={kernelName:jd,backendName:"webgl",kernelFunc:O7},z7=SS+`
return cos(x);
`,B7=Ye({opSnippet:z7}),W7={kernelName:Vs,backendName:"webgl",kernelFunc:B7},V7=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,U7=Ye({opSnippet:V7}),G7={kernelName:Ko,backendName:"webgl",kernelFunc:U7},H7=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,p]=n;this.outputShape=[c,u,p,l];let d=a==="bilinear"?1:0,[h,m]=[`${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) * ${h}`],[b,x,v]=p>1?[`${(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 = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${v};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},j7=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=a,u=new H7(r.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[r,s,i],"float32")},q7={kernelName:Xo,backendName:"webgl",kernelFunc:j7},XS=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${qS(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() {
${mt(a)} coords = getOutputCoords();
int end = ${KS(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${KS(a,"coords")} = idx;
val += getX(${qS(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 qS(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 ${e} is not yet supported`)}function KS(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function K7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length,c=_.getAxesPermutation([s],l),u=r;c!=null&&(u=En({inputs:{x:r},backend:n,attrs:{perm:c}}));let p=_.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let d=r.shape[p],h=Wn({inputs:{x:u},backend:n});for(let m=0;m<=Math.ceil(Math.log2(d))-1;m++){let f=new XS(u.shape,!1,o),g=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(i){let m=new XS(u.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(c!=null){let m=_.getUndoAxesPermutation(c),f=En({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),f}return h}var X7={kernelName:Us,backendName:"webgl",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(s.dataId),u=hS(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(s),u=NK(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var J7={kernelName:qd,backendName:"webgl",kernelFunc:Y7},Z7=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 Q7(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 Z7(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var eJ={kernelName:Yo,backendName:"webgl",kernelFunc:Q7},YS=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}
${y}
setOutput(result);
}
`}},JS=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,p=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=m,g="int xR; int xC; int xCOffset;";for(let v=0;v<h;v++)for(let N=0;N<m;N++)g+=`
vec4 xTexelR${v}C${N*2} = vec4(0.);
vec4 wR${v}C${N} = vec4(0.);
vec4 xR${v}C${N} = vec4(0.);`;for(let v=0;v<h;v++)for(let N=0;N<f;N++){let T=N*2;if(g+=`
xR = xRCorner + ${v*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 < ${i}) {
xTexelR${v}C${T} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${v}C${T}.zw = vec2(0.);
}
} else {
xTexelR${v}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 >= ${i}) {
previous.zw = vec2(0.);
}
xR${v}C${T} = vec4(previous.zw, xTexelR${v}C${T}.xy);
} else {
xR${v}C${T} = vec4(0, 0, xTexelR${v}C${T}.xy);
}
`:g+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${v}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${v}C${T} = vec4(0.);
}
xR${v}C${T} = xTexelR${v}C${T};
`,T+1<m)){let E=l%2==0?k.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(g+=`
xCOffset = xC + ${l%2} + ${E};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${T+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(g+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${T} = vec4(0.);
}
`),g+=`
xR${v}C${T+1} = vec4(
xTexelR${v}C${T}.zw, xTexelR${v}C${T+2}.xy);
`):g+=`
xCOffset = xC + ${E};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${T+2} = getX(batch, xR, xCOffset, d1);
}
xR${v}C${T+1} = xTexelR${v}C${T+2};
`}}else T<m&&(g+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(g+=`
xCOffset = xC + 1 - ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${T} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${T} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${v}C${T+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${v}C${T+2} = vec4(0.);
}
xR${v}C${T} = vec4(
xTexelR${v}C${T}.zw, xTexelR${v}C${T+2}.zw);
`,T+1<m&&(g+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${v}C${T+1} = vec4(xTexelR${v}C${T+2}.xy, final.xy);
`)):(g+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${v}C${T} = getX(batch, xR, xC, d1);
} else {
xTexelR${v}C${T} = vec4(0.);
}
xCOffset = xC + ${u};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${v}C${T+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${v}C${T+2} = vec4(0.);
}
xR${v}C${T} = vec4(
xTexelR${v}C${T}.xy, xTexelR${v}C${T+2}.xy);
`,T+1<m&&(g+=`
xR${v}C${T+1} = vec4(
xTexelR${v}C${T}.zw, xTexelR${v}C${T+2}.zw);
`)),g+="}");T<m&&(g+=`
vec4 wTexelR${v}C${T} = getW(${v}, ${T}, d1, q);
wR${v}C${T} = vec4(wTexelR${v}C${T}.xz, wTexelR${v}C${T}.xz);
`,T+1<m&&(g+=`
vec4 wTexelR${v}C${T+1} = getW(${v}, ${T+1}, d1, q);
wR${v}C${T+1} =
vec4(wTexelR${v}C${T+1}.xz, wTexelR${v}C${T+1}.xz);`))}for(let v=0;v<h;v++)for(let N=0;N<m;N++)g+=`dotProd += xR${v}C${N} * wR${v}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 x=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;
${x}
${b}
setOutput(result);
}
`}};function tJ(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(_.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let p=_.computeConv2DInfo(r.shape,s.shape,i,u,o,c,!0),d;return ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?d=new JS(p):d=new YS(p),n.runWebGLProgram(d,[r,s],"float32")}var nJ={kernelName:Gs,backendName:"webgl",kernelFunc:tJ},aJ=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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},rJ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${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 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function sJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=a,p=_.computeConv2DInfo(r.shape,u,i,o,l,c,!0),d=new aJ(p);return n.runWebGLProgram(d,[r,s],"float32")}var iJ={kernelName:Kd,backendName:"webgl",kernelFunc:sJ};function oJ(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=a,p=_.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new rJ(p);return n.runWebGLProgram(d,[r,s],"float32")}var lJ={kernelName:Xd,backendName:"webgl",kernelFunc:oJ},uJ=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function cJ(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=xe({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new uJ(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var pJ={kernelName:Yd,backendName:"webgl",kernelFunc:cJ},dJ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${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 hJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:l}=a,c=_.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",l),u,p=new dJ(c);u=n.runWebGLProgram(p,[r,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var mJ={kernelName:yc,backendName:"webgl",kernelFunc:hJ},fJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",gJ=`
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;
`,yJ=Ye({opSnippet:fJ,packedOpSnippet:gJ}),bJ={kernelName:Jo,backendName:"webgl",kernelFunc:yJ},xJ="return (b >= 1.0) ? a : a * (b + 1.0);",vJ=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,wJ=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=ee().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cp(vJ,a.shape,r.shape):new Tu(xJ,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},kJ={kernelName:Qd,backendName:"webgl",kernelFunc:wJ},IJ=`
return vec4(equal(a, b));
`,NJ="return float(a == b);",TJ=on({opSnippet:NJ,packedOpSnippet:IJ,dtype:"bool"}),SJ={kernelName:Qo,backendName:"webgl",kernelFunc:TJ},CJ=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${_.ERF_P};
float a1 = ${_.ERF_A1};
float a2 = ${_.ERF_A2};
float a3 = ${_.ERF_A3};
float a4 = ${_.ERF_A4};
float a5 = ${_.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,EJ=Ye({opSnippet:CJ}),_J={kernelName:Zo,backendName:"webgl",kernelFunc:EJ},ZS="return exp(x);",QS=Ye({opSnippet:ZS,packedOpSnippet:ZS,cpuKernelImpl:CK}),FJ={kernelName:js,backendName:"webgl",kernelFunc:QS};function tw(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+r+1),o.splice(l,0,1),xe({inputs:{x:s},backend:a,attrs:{shape:o}})}var AJ={kernelName:el,backendName:"webgl",kernelFunc:tw},e2="return exp(x) - 1.0;",$J=Ye({opSnippet:e2,packedOpSnippet:e2,cpuKernelImpl:EK}),DJ={kernelName:tl,backendName:"webgl",kernelFunc:$J},t2=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 n2(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=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}).shape,l=new t2("real",o,t),c=new t2("imag",o,t),u=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:o},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:o}],p=n.runWebGLProgram(l,u,"float32"),d=n.runWebGLProgram(c,u,"float32"),h=bs({inputs:{real:p,imag:d},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d);let m=xe({inputs:{x:h},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(m),m}function MJ(e){let{inputs:t,backend:n}=e,{input:a}=t;return n2(a,!1,n)}var RJ={kernelName:eh,backendName:"webgl",kernelFunc:MJ},PJ=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 nw(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 PJ(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var OJ={kernelName:bc,backendName:"webgl",kernelFunc:nw},LJ=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},zJ={kernelName:nl,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new LJ(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},a2="return floor(x);",BJ=Ye({opSnippet:a2,packedOpSnippet:a2,cpuKernelImpl:_K}),WJ={kernelName:qs,backendName:"webgl",kernelFunc:BJ},VJ=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,UJ=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,GJ=on({opSnippet:VJ,packedOpSnippet:UJ,dtype:"int32"}),HJ={kernelName:Ks,backendName:"webgl",kernelFunc:GJ},jJ=class{constructor(e){this.variableNames=["A"];let t=gn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},qJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=gn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${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);
}
}
${t.output} = result;
}
`}},XJ={kernelName:mh,backendName:"webgl",kernelFunc:KJ},Cu;function KJ(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,u]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[u,c],d=[u,c,s];(o||i||l)&&(Cu==null&&(Cu=document.createElement("canvas").getContext("2d")),Cu.canvas.width=c,Cu.canvas.height=u,Cu.drawImage(r,0,0,c,u),r=Cu.canvas);let h=n.makeTensorInfo(p,"int32");n.texData.get(h.dataId).usage=ra.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(h.dataId),r);let m=ee().getBool("WEBGL_PACK")?new qJ(d):new jJ(d),f=n.runWebGLProgram(m,[h],"int32");return n.disposeData(h.dataId),f}function YJ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=a,f=_.convertConv2DDataFormat(u),g=_.computeConv2DInfo(r.shape,s.shape,l,p,c,d,!1,f),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=HS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(ee().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=jS({x:r,filter:s,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,N=o!=null,T=h==="leakyrelu",E=h?of(h,!1):null,A=new GS(g,v,E,N,T),$=[r,s];if(i&&$.push(i),o&&$.push(o),T){let O=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));$.push(O),b.push(O)}y=n.runWebGLProgram(A,$,"float32")}let x=xe({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var JJ={kernelName:Si,backendName:"webgl",kernelFunc:YJ};function ZJ(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(_.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let g=_.computeConv2DInfo(r.shape,s.shape,l,f,c,p,!0),y=ee().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=d?of(d,y):null,x=[r,s],v=i!=null,N=o!=null,T=d==="leakyrelu";if(v&&x.push(i),N&&x.push(o),T){let $=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push($),m.push($)}let E;y?E=new JS(g,v,b,N,T):E=new YS(g,v,b,N,T);let A=n.runWebGLProgram(E,x,"float32");return m.forEach($=>n.disposeIntermediateTensorInfo($)),A}var QJ={kernelName:Ci,backendName:"webgl",kernelFunc:ZJ},eZ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=mt(t.length),r=mt(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 tZ(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,l,c,u]=_.prepareAndValidate(a,r),p=xe({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/c,c]}}),h=new eZ(i,u,[l,c]),m=n.runWebGLProgram(h,[d,p],d.dtype),f=xe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(m),f}var nZ={kernelName:rl,backendName:"webgl",kernelFunc:tZ},rZ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=mt(this.rank),a=aZ(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function aZ(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(`${n[r]}`);return a.join()}function sZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=k.sizeFromShape(s.shape),p=[],d=xe({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});p.push(d),p.push(h);let m=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.bufferSync(h),x=n.bufferSync(d),v=FK(x,b,m);return p.forEach(N=>n.disposeIntermediateTensorInfo(N)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let f=new rZ(d.shape,m),g=n.runWebGLProgram(f,[d,h],d.dtype);p.push(g);let y=xe({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var iZ={kernelName:al,backendName:"webgl",kernelFunc:sZ},oZ="return float(a > b);",lZ=`
return vec4(greaterThan(a, b));
`,uZ=on({opSnippet:oZ,packedOpSnippet:lZ,cpuKernelImpl:AK,dtype:"bool"}),cZ={kernelName:sl,backendName:"webgl",kernelFunc:uZ},pZ="return float(a >= b);",dZ=`
return vec4(greaterThanEqual(a, b));
`,hZ=on({opSnippet:pZ,packedOpSnippet:dZ,dtype:"bool"}),mZ={kernelName:Ys,backendName:"webgl",kernelFunc:hZ};function fZ(e){let{inputs:t,backend:n}=e,{input:a}=t;return n2(a,!0,n)}var gZ={kernelName:th,backendName:"webgl",kernelFunc:fZ},yZ="return float(!isnan(x) && !isinf(x));",bZ=Ye({opSnippet:yZ,dtype:"bool"}),xZ={kernelName:ol,backendName:"webgl",kernelFunc:bZ},vZ="return float(isinf(x));",wZ=Ye({opSnippet:vZ,dtype:"bool"}),kZ={kernelName:ll,backendName:"webgl",kernelFunc:wZ},IZ="return float(isnan(x));",NZ=Ye({opSnippet:IZ,dtype:"bool"}),TZ={kernelName:ul,backendName:"webgl",kernelFunc:NZ},SZ="return float(a < b);",CZ=`
return vec4(lessThan(a, b));
`,EZ=on({opSnippet:SZ,packedOpSnippet:CZ,cpuKernelImpl:$K,dtype:"bool"}),_Z={kernelName:cl,backendName:"webgl",kernelFunc:EZ},FZ="return float(a <= b);",AZ=`
return vec4(lessThanEqual(a, b));
`,$Z=on({opSnippet:FZ,packedOpSnippet:AZ,dtype:"bool"}),DZ={kernelName:pl,backendName:"webgl",kernelFunc:$Z};function MZ(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=DK(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var RZ={kernelName:ah,backendName:"webgl",kernelFunc:MZ},PZ=`if (x < 0.0) return NAN;
return log(x);`,OZ=`
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;
`,LZ=Ye({opSnippet:PZ,packedOpSnippet:OZ,cpuKernelImpl:MK}),zZ={kernelName:Zs,backendName:"webgl",kernelFunc:LZ},BZ="return log(1.0 + x);",WZ=Ye({opSnippet:BZ}),VZ={kernelName:dl,backendName:"webgl",kernelFunc:WZ},UZ="return float(a >= 1.0 && b >= 1.0);",GZ=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,HZ=on({opSnippet:UZ,packedOpSnippet:GZ,dtype:"bool"}),jZ={kernelName:hl,backendName:"webgl",kernelFunc:HZ},qZ="return float(!(x >= 1.0));",KZ=Ye({opSnippet:qZ}),XZ={kernelName:xc,backendName:"webgl",kernelFunc:KZ},YZ="return float(a >= 1.0 || b >= 1.0);",JZ=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,ZZ=on({opSnippet:YZ,packedOpSnippet:JZ,dtype:"bool"}),QZ={kernelName:vc,backendName:"webgl",kernelFunc:ZZ},e9=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 * ${o};
setOutput(val);
}
`}},t9=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
}
`}},n9=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=a,c=ee().getBool("WEBGL_PACK_NORMALIZATION")?new t9(r.shape,s,i,o,l):new e9(r.shape,s,i,o,l);return n.runWebGLProgram(c,[r],r.dtype)},a9={kernelName:wc,backendName:"webgl",kernelFunc:n9},r9=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);
}
`}},s9=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 r9(r.shape,o,l,c,u);return n.runWebGLProgram(p,[r,s,i],r.dtype)},i9={kernelName:rh,backendName:"webgl",kernelFunc:s9};function o9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=xe({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=eo(i,e.dtype,"max",a),l=xe({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}function r2(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=_.getAxesPermutation(c,o),p=u!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let b=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[u[T]];let v=Xv(b,r.shape,r.dtype,u,x);h=n.makeTensorInfo(x,r.dtype);let N=n.texData.get(h.dataId);N.values=v}else h=lf(r,u,n);c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("max",c,o);let[m,f]=_.computeOutAndReduceShapes(h.shape,c),g=m;i&&(g=_.expandShapeToKeepDim(m,l));let y;if(d){let b=n.texData.get(h.dataId).values,x=RK(b,k.sizeFromShape(f),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=o9(h,f,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var l9={kernelName:Qs,backendName:"webgl",kernelFunc:r2},u9=wS+`
return max(a, b);
`,c9=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+sf+`
return result;
`,p9=on({opSnippet:u9,packedOpSnippet:c9,cpuKernelImpl:PK}),d9={kernelName:ei,backendName:"webgl",kernelFunc:p9};function h9(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Tp(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=a,c=1;k.assert(_.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=_.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 Ep(u,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var m9={kernelName:ti,backendName:"webgl",kernelFunc:h9};function f9(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=_.computePool3DInfo(r.shape,s,i,u,o,c,l),d=new Zv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var g9={kernelName:kc,backendName:"webgl",kernelFunc:f9},y9=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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},b9=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,p=l-1-e.padInfo.top,d=c-1-e.padInfo.left,h=o*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${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 x9(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=_.computePool3DInfo(i.shape,o,l,p,c,u),h=new Zv(d,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new b9(d),g=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),g}var v9={kernelName:ih,backendName:"webgl",kernelFunc:x9};function w9(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Tp([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:p}=a,d=_.computePool2DInfo(o.shape,l,c,1,u,p),h=!0,m=new Ep(d,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),g=new y9(d),y=n.runWebGLProgram(g,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var k9={kernelName:sh,backendName:"webgl",kernelFunc:w9};function I9(e,t,n,a){let r=new Ep(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Ep(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var N9={kernelName:oh,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(_.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=_.computePool2DInfo(a.shape,r,s,c,i),[p,d]=I9(a,o,u,l);return[p,d]}};function T9(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=xe({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=eo(i,"float32","mean",a),l=xe({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),l}var S9={kernelName:ni,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=_.getAxesPermutation(c,o),p=u!=null,d=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(d){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let E=0;E<v.length;E++)v[E]=a.shape[u[E]];let N=Xv(x,a.shape,a.dtype,u,v);m=i.makeTensorInfo(v,a.dtype);let T=i.texData.get(m.dataId);T.values=N}else m=lf(a,u,i);h.push(m),c=_.getInnerMostAxes(c.length,o)}_.assertAxesAreInnerMostDims("sum",c,o);let[f,g]=_.computeOutAndReduceShapes(m.shape,c),y=f;r&&(y=_.expandShapeToKeepDim(f,l));let b=T9(m,g,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return b}};function C9(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=_.getAxesPermutation(c,o),p=r;u!=null&&(p=En({inputs:{x:r},backend:n,attrs:{perm:u}}),c=_.getInnerMostAxes(c.length,r.shape.length)),_.assertAxesAreInnerMostDims("min",c,o);let[d,h]=_.computeOutAndReduceShapes(p.shape,c),m=k.sizeFromShape(h),f=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),g=eo(f,f.dtype,"min",n),y;if(i){let b=_.expandShapeToKeepDim(d,l);y=xe({inputs:{x:g},backend:n,attrs:{shape:b}})}else y=xe({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(p),y}var E9={kernelName:ai,backendName:"webgl",kernelFunc:C9},_9=wS+`
return min(a, b);
`,F9=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+sf+`
return result;
`,A9=on({opSnippet:_9,packedOpSnippet:F9,cpuKernelImpl:OK}),$9={kernelName:ri,backendName:"webgl",kernelFunc:A9},D9=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=mt(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}));
}
`}},M9=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=mt(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=yn("rc",a),l=yn("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);
}
`}},R9=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new M9(a.shape,r,s):new D9(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},P9={kernelName:Ic,backendName:"webgl",kernelFunc:R9},O9=`if (b == 0.0) return NAN;
return mod(a, b);`,L9=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+sf+`
return result;
`,z9=on({opSnippet:O9,packedOpSnippet:L9}),B9={kernelName:ml,backendName:"webgl",kernelFunc:z9},W9=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)}}},V9=`
if (a == b) {
return 1.0;
};
return a / b;`,U9=`
// 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;
`,s2=on({opSnippet:V9,packedOpSnippet:U9,checkOutOfBounds:!0}),G9={kernelName:Hs,backendName:"webgl",kernelFunc:s2},i2="return a - b;",o2=on({opSnippet:i2,packedOpSnippet:i2,supportsComplex:!0,cpuKernelImpl:HK}),H9={kernelName:ki,backendName:"webgl",kernelFunc:o2};function l2(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=r2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=_.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=o2({inputs:{a:r,b:c},backend:n}),p=QS({inputs:{x:u},backend:n}),d=Jv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),m=s2({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 j9={kernelName:vi,backendName:"webgl",kernelFunc:l2};function q9(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,l=o?r:l2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],p=new W9(c,u,s),d=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),h}var K9={kernelName:lh,backendName:"webgl",kernelFunc:q9},u2="return -x;";function X9(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=zK(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return ee().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Nu(a.shape,u2):r=new ys(a.shape,u2),n.runWebGLProgram(r,[a],a.dtype)}var Y9={kernelName:fl,backendName:"webgl",kernelFunc:X9},J9=Qa.nonMaxSuppressionV3Impl;function Z9(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}=J9(c,u,i,o,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Q9={kernelName:yl,backendName:"webgl",kernelFunc:Z9},eQ=Qa.nonMaxSuppressionV4Impl;function tQ(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}=eQ(u,p,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var nQ={kernelName:bl,backendName:"webgl",kernelFunc:tQ},aQ=Qa.nonMaxSuppressionV5Impl;function rQ(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}=aQ(u,p,d,h,m,f);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var sQ={kernelName:xl,backendName:"webgl",kernelFunc:rQ},iQ=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)));
}
`}},oQ=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 iQ(l,s,i,o),u=xe({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let d=[...r.shape,s],h=xe({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},lQ={kernelName:ii,backendName:"webgl",kernelFunc:oQ};function hf(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Fp({inputs:{input:a},backend:n}),s=hf({inputs:{x:r},backend:n}),i=df({inputs:{input:a},backend:n}),o=hf({inputs:{x:i},backend:n}),l=bs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return nw({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var uQ={kernelName:Ol,backendName:"webgl",kernelFunc:hf};function c2(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=Fp({inputs:{input:a},backend:n}),s=c2({inputs:{x:r},backend:n}),i=df({inputs:{input:a},backend:n}),o=hf({inputs:{x:i},backend:n}),l=bs({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return nw({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var cQ={kernelName:vl,backendName:"webgl",kernelFunc:c2};function pQ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return tw({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=tw({inputs:{input:u},backend:n,attrs:{dim:r}});return o.push(p),p}),c=US({inputs:l,backend:n,attrs:{axis:r}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var dQ={kernelName:wl,backendName:"webgl",kernelFunc:pQ},hQ=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=mt(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}));
}
}
`}},mQ=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=mt(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=yn("rc",a),l=yn("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);
}
`}},p2=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mQ(r.shape,s,i):new hQ(r.shape,s,i);return n.runWebGLProgram(o,[r],r.dtype)},fQ={kernelName:oi,backendName:"webgl",kernelFunc:p2},gQ=`
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);
`,yQ=`
// 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));
`+sf+`
return result;
`,bQ=on({opSnippet:gQ,packedOpSnippet:yQ}),xQ={kernelName:li,backendName:"webgl",kernelFunc:bQ};function vQ(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=_.getAxesPermutation(u,o),d=r;p!=null&&(d=En({inputs:{x:r},backend:n,attrs:{perm:p}}),u=_.getInnerMostAxes(u.length,o),l.push(d)),_.assertAxesAreInnerMostDims("prod",u,o);let h;if(n.shouldExecuteOnCPU([d])){let m=n.texData.get(d.dataId).values,{outVals:f,outShape:g,outDtype:y}=BK(d.shape,d.dtype,m,u);h=n.makeTensorInfo(g,y,f)}else{let[m,f]=_.computeOutAndReduceShapes(d.shape,u),g=k.sizeFromShape(f),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),b=xh(r.dtype),x=eo(y,b,"prod",n);h=xe({inputs:{x},backend:n,attrs:{shape:m}}),l.push(y),l.push(x)}if(i){l.push(h);let m=_.expandShapeToKeepDim(h.shape,c);h=xe({inputs:{x:h},backend:n,attrs:{shape:m}})}return l.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var wQ={kernelName:kl,backendName:"webgl",kernelFunc:vQ},d2=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=WK(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},kQ={kernelName:Nc,backendName:"webgl",kernelFunc:d2},IQ="return 1.0 / x;",NQ=Ye({opSnippet:IQ}),TQ={kernelName:Il,backendName:"webgl",kernelFunc:NQ},SQ=Oa+`
return (x < 0.0) ? 0.0 : x;
`,CQ=`
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=Ye({opSnippet:SQ,packedOpSnippet:CQ}),_Q={kernelName:ci,backendName:"webgl",kernelFunc:EQ},FQ=Oa+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,AQ=`
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;
`,$Q=Ye({opSnippet:FQ,packedOpSnippet:AQ}),DQ={kernelName:di,backendName:"webgl",kernelFunc:$Q},MQ=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);
}
`}},RQ=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 PQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=ee().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new RQ(r.shape,l,c,s,i):new MQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],"float32")}var OQ={kernelName:pi,backendName:"webgl",kernelFunc:PQ},LQ=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 zQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new LQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var BQ={kernelName:ph,backendName:"webgl",kernelFunc:zQ},WQ=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 + ${p})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function VQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[l,c]=o,u=new WQ(r.shape,l,c,s,i);return n.runWebGLProgram(u,[r],r.dtype)}var UQ={kernelName:Tc,backendName:"webgl",kernelFunc:VQ},GQ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],p=1/c,d=1/u,h=Math.ceil(p)*2+2,m=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${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),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function HQ(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new GQ(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var jQ={kernelName:ch,backendName:"webgl",kernelFunc:HQ},qQ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=mt(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},KQ=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-${n} tensor is not yet supported`);this.outputShape=e;let a=yn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=mt(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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 XQ(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=ee().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new KQ(r.shape,o):new qQ(r.shape,o);return n.runWebGLProgram(l,[r],r.dtype)}var YQ={kernelName:hi,backendName:"webgl",kernelFunc:XQ},JQ=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]=_.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 < ${r}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},ZQ={kernelName:zl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,l=new JQ(a.shape,r,s,i);return o.runWebGLProgram(l,[a],a.dtype)}},QQ=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,eee=Ye({opSnippet:QQ}),tee={kernelName:mi,backendName:"webgl",kernelFunc:eee},nee="return inversesqrt(x);",aee=Ye({opSnippet:nee,cpuKernelImpl:VK}),ree={kernelName:fi,backendName:"webgl",kernelFunc:aee},h2=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=mt(r.length),l=mt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 see(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}=_.calculateShapes(s,r,i),d=[p/c,c];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=xe({inputs:{x:r},backend:n,attrs:{shape:[l,o]}}),m=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new h2(l,o,h.shape.length,m.shape.length,u,d),y=n.runWebGLProgram(g,[m,h,f],m.dtype),b=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),b}var iee={kernelName:Tl,backendName:"webgl",kernelFunc:see},oee=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=mt(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function lee(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new oee(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],pa(r.dtype,s.dtype))}var uee={kernelName:Sl,backendName:"webgl",kernelFunc:lee},cee=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${_.SELU_SCALEALPHA};
float scale = ${_.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,pee=Ye({opSnippet:cee}),dee={kernelName:Cl,backendName:"webgl",kernelFunc:pee},hee="return 1.0 / (1.0 + exp(-1.0 * x));",mee=Ye({opSnippet:hee}),fee={kernelName:yi,backendName:"webgl",kernelFunc:mee},gee=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,yee=Ye({opSnippet:gee}),bee={kernelName:Fl,backendName:"webgl",kernelFunc:yee},xee=SS+`
return sin(x);
`,vee=Ye({opSnippet:xee}),wee={kernelName:gi,backendName:"webgl",kernelFunc:vee},kee=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Iee=Ye({opSnippet:kee}),Nee={kernelName:_l,backendName:"webgl",kernelFunc:Iee},Tee=`
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;
`,See=Ye({opSnippet:Tee}),Cee={kernelName:Al,backendName:"webgl",kernelFunc:See},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=p2({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=_.getReshaped(u.shape,s,o,!1),d=_.getPermuted(p.length,s.length,!1),h=_.getReshapedPermuted(u.shape,s,o,!1),m=xe({inputs:{x:u},backend:n,attrs:{shape:p}}),f=En({inputs:{x:m},backend:n,attrs:{perm:d}}),g=xe({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},_ee={kernelName:Sc,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}=_.calculateShapes(s,r,o),d=!1,h=new h2(c,l,r.shape.length,s.shape.length,u,[p,1],d),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=xe({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var Aee={kernelName:dh,backendName:"webgl",kernelFunc:Fee};function $ee(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],l=_.prepareSplitSize(r,s,o),c=r.shape.length,u=new Array(c).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[o]=d;let m=_p({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[o]+=d,m})}var Dee={kernelName:$l,backendName:"webgl",kernelFunc:$ee},Mee="return sqrt(x);",Ree=Ye({opSnippet:Mee}),Pee={kernelName:bi,backendName:"webgl",kernelFunc:Ree},Oee="return x * x;",Lee=Ye({opSnippet:Oee}),zee={kernelName:Cc,backendName:"webgl",kernelFunc:Lee},m2="return (a - b) * (a - b);",Bee=on({opSnippet:m2,packedOpSnippet:m2}),Wee={kernelName:wi,backendName:"webgl",kernelFunc:Bee};function Vee({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Oa+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new ys(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var Uee={kernelName:Ll,backendName:"webgl",kernelFunc:Vee},Gee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=mt(n.length),s=mt(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 Hee(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}=hn.sliceInfo(r.shape,s,i,o,l,c,u,p,d),x=xe({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let T=_p({inputs:{x},backend:n,attrs:{begin:m,size:g}});v=xe({inputs:{x:T},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(T)}else if(b.some(T=>T===0))v=n.makeTensorInfo(b,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let T=n.texData.get(x.dataId).values,E=Le(x.shape,x.dtype,T),A=GK(b,E,f,m);v=n.makeTensorInfo(b,x.dtype,A.values)}else{let T=new Gee(m,f,b);v=n.runWebGLProgram(T,[x],x.dtype)}let N=xe({inputs:{x:v},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),N}var jee={kernelName:Dl,backendName:"webgl",kernelFunc:Hee},qee="return tan(x);",Kee=Ye({opSnippet:qee}),Xee={kernelName:Ml,backendName:"webgl",kernelFunc:Kee},Yee=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Jee=Ye({opSnippet:Yee}),Zee={kernelName:Ii,backendName:"webgl",kernelFunc:Jee},ete=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=mt(this.rank),r=Qee(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Qee(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 f2(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=jK(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new ete(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var tte={kernelName:Xr,backendName:"webgl",kernelFunc:f2};function nte(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[l,c]=qK(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 ate={kernelName:Rl,backendName:"webgl",kernelFunc:nte};function rte(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Tp(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}=KK(i,r,s.shape,s.dtype);return[a.makeTensorInfo(l,s.dtype,o),a.makeTensorInfo([c.length],"int32",c)]}var ste={kernelName:hh,backendName:"webgl",kernelFunc:rte};function ite(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=xe({inputs:{x:g},backend:n,attrs:{shape:c}});m[f]=y,p.push(g)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var ote={kernelName:Pl,backendName:"webgl",kernelFunc:ite},lte=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 ute(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=_.getAxesPermutation([c],o),p=r;u!=null&&(p=En({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(p),c=_.getInnerMostAxes(1,o)[0]);let d=_.segment_util.computeOutShape(p.shape,c,i),h=k.sizeFromShape([p.shape[c]]),m=xe({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(m);let f=xh(r.dtype),g=(v,N,T,E,A)=>{let $=v.shape[0],O=v.shape[1],V=_.segment_util.segOpComputeOptimalWindowSize(O,A),W={windowSize:V,inSize:O,batchSize:$,numSegments:A},H=new lte(W,N),X=n.compileAndRun(H,[v,T],E);if(l.push(X),X.shape[1]===A)return X;let q=d2({backend:n,attrs:{start:0,stop:A,step:1,dtype:"float32"}}),K=f2({inputs:{x:q},backend:n,attrs:{reps:[O/V]}});return l.push(q),l.push(K),g(X,N,K,E,A)},y=g(m,"unsortedSegmentSum",s,f,i),b=xe({inputs:{x:y},backend:n,attrs:{shape:d}}),x=b;if(u!=null){l.push(b);let v=_.getUndoAxesPermutation(u);x=En({inputs:{x},backend:n,attrs:{perm:v}})}return l.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var cte={kernelName:Ec,backendName:"webgl",kernelFunc:ute},pte=[a9,i9,HX,qX,YX,QX,tY,rY,iY,lY,dY,mY,yY,vY,CY,IY,FY,MY,$Y,LY,BY,VY,jY,QY,t7,o7,u7,h7,g7,SX,v7,F7,$7,N7,P7,L7,M7,W7,G7,q7,X7,J7,eJ,iJ,lJ,nJ,pJ,mJ,bJ,kJ,SJ,_J,FJ,AJ,DJ,RJ,OJ,zJ,WJ,HJ,XJ,JJ,QJ,nZ,iZ,cZ,mZ,TX,gZ,x7,xZ,kZ,TZ,EX,_Z,DZ,RZ,VZ,zZ,jZ,XZ,QZ,l9,g9,m9,v9,k9,N9,d9,S9,E9,$9,P9,B9,K9,DX,Y9,Q9,nQ,sQ,a7,lQ,cQ,dQ,fQ,xQ,FX,wQ,kQ,r7,G9,TQ,DQ,_Q,RX,OQ,BQ,UQ,jQ,YQ,ZQ,tee,ree,iee,uee,dee,fee,bee,wee,Nee,JY,j9,Cee,_ee,Aee,Dee,Pee,zee,Wee,Uee,jee,H9,VX,Xee,Zee,tte,ate,UX,ste,ote,cte,uQ];for(let e of pte)Fc(e);var dte="2.8.3",hte={"tfjs-core":rk,"tfjs-backend-cpu":UG,"tfjs-backend-webgl":NX,"tfjs-data":HN,"tfjs-layers":Dm,"tfjs-converter":zN,tfjs:dte},Vn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Vn||(Vn={}));var Ap;(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"})(Ap||(Ap={}));var g2;function mte(e){g2=e.wasm.cwrap(Ti,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fte(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 A=n.dataIdMap.get(i.dataId);if(A.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);m=A.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,g=Ap[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],b=c?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,b],r.dtype),N=n.dataIdMap.get(v.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),E=new Uint8Array(new Int32Array(s.shape).buffer);return g2(d,T,r.shape.length,h,E,s.shape.length,l,c,g,m,f,p||0,N),v}var gte={kernelName:Ti,backendName:"wasm",setupFunc:mte,kernelFunc:fte};function Un(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),c=s.dataIdMap.get(l.dataId).id;return k.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var yte=Un(Lo);function bn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:c,b:u}=l,p=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,m=_.assertAndGetBroadcastShape(c.shape,u.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),b=o.dataIdMap.get(f.dataId).id,x=()=>a(p,g,c.shape.length,d,y,u.shape.length,Vn[c.dtype],b);if(t&&c.dtype==="float32")return x(),f;let v=_.getBroadcastDims(c.shape,m),N=_.getBroadcastDims(u.shape,m),T=v.every((A,$)=>A===$),E=N.every((A,$)=>A===$);if(T&&E)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var bte=!0,xte=bn(qr,bte),y2;function vte(e){y2=e.wasm.cwrap(Rs,null,["array","number","number","number"])}function wte(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return y2(s,r.length,Vn[a.dtype],i),a}var kte={kernelName:Rs,backendName:"wasm",setupFunc:vte,kernelFunc:wte};function mf(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var Ite={kernelName:il,backendName:"wasm",kernelFunc:mf},b2;function Nte(e){b2=e.wasm.cwrap(Ni,null,["number","array","number","number","number","array","number"])}function ff(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=Ste(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=Tte(t.x.shape,a.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=mf({inputs:t,backend:n});return m.shape=o,m}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return b2(u,h,l.shape.length,Vn[l.dtype],p,d,s.length),c}function Tte(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function Ste(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var Cte={kernelName:Ni,backendName:"wasm",kernelFunc:ff,setupFunc:Nte};function Eu(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=_.getAxesPermutation(i,r),l=null,c=!1;if(o!=null){let u=new Array(r);for(let d=0;d<u.length;d++)u[d]=a[o[d]];i=_.getInnerMostAxes(i.length,r),l=ff({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var x2;function Ete(e){x2=e.wasm.cwrap(Ps,null,["number","number","number","number","number"])}function _te(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:p}=Eu(s,r,t);if(p){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),g=l.shape[u[0]];return x2(o,Vn[l.dtype],f,g,m),p&&t.disposeData(c.dataId),h}var Fte={kernelName:Ps,backendName:"wasm",kernelFunc:_te,setupFunc:Ete},v2;function Ate(e){v2=e.wasm.cwrap(Os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $te(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=_.computePool2DInfo(r.shape,i,o,1,l,c),p=u.filterHeight,d=u.filterWidth,h=u.padInfo.top,m=u.padInfo.right,f=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,b=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let v=a.makeOutput(u.outShape,"float32"),N=a.dataIdMap.get(v.dataId).id;return v2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,N),v}var Dte={kernelName:Os,backendName:"wasm",setupFunc:Ate,kernelFunc:$te};function La(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),{dataId:a.dataId,shape:i,dtype:a.dtype}}var Mte={kernelName:Nl,backendName:"wasm",kernelFunc:La},w2;function Rte(e){w2=e.wasm.cwrap(Ls,null,["number","array","number","number","array","number","number","number","number"])}function Pte(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=s.shape.length,u=i?r.shape[l-2]:r.shape[l-1],p=o?s.shape[c-1]:s.shape[c-2],d=i?r.shape[l-1]:r.shape[l-2],h=o?s.shape[c-2]:s.shape[c-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),g=k.sizeFromShape(m),y=k.sizeFromShape(f),b=g===y||g===1||y===1;k.assert(l>=2&&c>=2&&b,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. 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Please use 'channelsLast'.`);let W=a.makeOutput(m.outShape,"float32"),H=a.dataIdMap.get(W.dataId).id;return N2(i,r.shape[0],r.shape[1],r.shape[2],o,f,g,y,b,x,v,V,N,T,E,A,$,O,H),W}var Hte={kernelName:Bs,backendName:"wasm",setupFunc:Ute,kernelFunc:Gte},T2;function jte(e){T2=e.wasm.cwrap(Ws,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qte(e){let{backend:t,inputs:n,attrs:a}=e,{dy:r,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=a,p=1,d=_.convertConv2DDataFormat(l),h=_.computeConv2DInfo(u,s.shape,i,p,o,c,!1,d),{batchSize:m,filterHeight:f,filterWidth:g,inChannels:y,inHeight:b,inWidth:x,outChannels:v,outHeight:N,outWidth:T,strideHeight:E,strideWidth:A}=h,$=f-1-h.padInfo.top,O=g-1-h.padInfo.left,V=h.dataFormat==="channelsLast",W=k.computeStrides(h.inShape),H=k.computeStrides(r.shape),[X,q,K]=k.computeStrides(s.shape),J=W[0],te=V?W[1]:W[2],Q=V?W[2]:1,ie=V?1:W[1],re=H[0],ae=V?H[1]:H[2],oe=V?H[2]:1,he=V?1:H[1],ce=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(ce.dataId).id,be=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(s.dataId).id;return T2(be,Ie,m,f,g,b,x,y,N,T,v,E,A,$,O,X,q,K,J,te,Q,ie,re,ae,oe,he,ge),ce}var Kte={kernelName:Ws,backendName:"wasm",setupFunc:jte,kernelFunc:qte},Xte=Un(Vs),aw;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(aw||(aw={}));var S2;function Yte(e){S2=e.wasm.cwrap(Xo,null,["number","number","number","number","array","number","number","number","number","number"])}function Jte(e){let{backend:t,inputs:n,attrs:a}=e,{method:r,extrapolationValue:s,cropSize:i}=a,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[p,d]=i,h=[u,p,d,o.shape[3]],m=t.dataIdMap.get(o.dataId),f;o.dtype!=="float32"&&(f=gf({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(f.dataId));let g=m.id,y=t.dataIdMap.get(l.dataId).id,b=t.dataIdMap.get(c.dataId).id,x=t.makeOutput(h,"float32"),v=t.dataIdMap.get(x.dataId).id,N=new Uint8Array(new Int32Array(o.shape).buffer);return S2(g,y,b,u,N,p,d,aw[r],s,v),f!=null&&t.disposeData(f.dataId),x}var Zte={kernelName:Xo,backendName:"wasm",setupFunc:Yte,kernelFunc:Jte},C2;function Qte(e){C2=e.wasm.cwrap(Us,null,["number","number","number","number","number","number"])}function ene(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,l=r.shape.length;k.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=_.getAxesPermutation([s],l),u=r;c!==null&&(u=ff({inputs:{x:r},attrs:{perm:c},backend:n}));let p=_.getInnerMostAxes(1,l)[0];_.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(u.shape,u.dtype),h=u.shape[p],m=n.dataIdMap.get(u.dataId).id,f=n.dataIdMap.get(d.dataId).id;C2(m,i?1:0,o?1:0,h,f,Vn[r.dtype]);let g=d;if(c!==null){let y=_.getUndoAxesPermutation(c);g=ff({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return g}var tne={kernelName:Us,backendName:"wasm",setupFunc:Qte,kernelFunc:ene},E2;function nne(e){E2=e.wasm.cwrap(Yo,null,["number","number","number","array","number","array","array","number","number"])}function ane(e){let{backend:t,inputs:n,attrs:a}=e,{x:r}=n,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],l=i==="NHWC"?r.shape[1]:r.shape[2],c=i==="NHWC"?r.shape[2]:r.shape[3],u=i==="NHWC"?r.shape[3]:r.shape[1],p=l*s,d=c*s,h=u/(s*s),m=i==="NHWC"?[o,p,d,h]:[o,h,p,d],f=t.makeOutput(m,"float32"),g=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(k.computeStrides(r.shape)).buffer),b=new Uint8Array(new Int32Array(m).buffer),x=new Uint8Array(new Int32Array(k.computeStrides(m)).buffer),v=t.dataIdMap.get(f.dataId).id;return E2(g,s,i==="NHWC"?1:0,y,r.shape.length-1,b,x,m.length,v),f}var rne={kernelName:Yo,backendName:"wasm",setupFunc:nne,kernelFunc:ane},_2;function sne(e){_2=e.wasm.cwrap(Gs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ine(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:p}=n,d=c==null?[1,1]:c,h=_.computeConv2DInfo(r.shape,s.shape,l,d,u,p,!0),m=h.filterHeight,f=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,b=h.padInfo.bottom,x=h.padInfo.left,v=h.dilationHeight,N=h.dilationWidth,T=h.strideHeight,E=h.strideWidth,A=h.inChannels,$=h.outChannels,O=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let V=a.makeOutput(h.outShape,"float32"),W=a.dataIdMap.get(V.dataId).id;return _2(i,r.shape[0],r.shape[1],r.shape[2],o,m,f,g,y,b,x,O,v,N,T,E,A,$,W),V}var one={kernelName:Gs,backendName:"wasm",setupFunc:sne,kernelFunc:ine},lne=!1,une=bn(Qo,lne,"bool"),cne=Un(js);function rw(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),La({inputs:{x:r},backend:a,attrs:{shape:o}})}var pne={kernelName:el,backendName:"wasm",kernelFunc:rw};function dne(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 hne={kernelName:bc,backendName:"wasm",kernelFunc:dne},F2;function mne(e){F2=e.wasm.cwrap(nl,null,["number","number","number","number","number","number"])}function fne(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 gne={kernelName:nl,backendName:"wasm",kernelFunc:fne,setupFunc:mne},yne=Un(qs),bne=!1,xne=bn(Ks,bne),A2;function vne(e){A2=e.wasm.cwrap(Xs,null,["number","number","number","number","number","number","number"])}function wne(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 A2(u,p,d,h,m,r,g),f}var kne={kernelName:Xs,backendName:"wasm",setupFunc:vne,kernelFunc:wne},$2;function Ine(e){$2=e.wasm.cwrap(Si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Nne(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=n,f=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d),g=Ap[h];if(g==null)throw new Error(`${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,x=f.outChannels,v=0;if(i!=null){let oe=a.dataIdMap.get(i.dataId);if(oe.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${oe.shape.length}.`);if(oe.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${oe.shape}) does not match the number of output channels (${x})`);v=oe.id}let N=f.filterHeight,T=f.filterWidth,E=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,O=f.padInfo.left,V=f.dilationHeight,W=f.dilationWidth,H=f.strideHeight,X=f.strideWidth,q=f.inChannels,K=f.padInfo.type==="SAME"?1:0,J=f.batchSize,te=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"),re=a.dataIdMap.get(ie.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return $2(y,J,te,Q,b,N,T,v,E,A,$,O,K,V,W,H,X,q,x,g,ae,m||0,re),ie}var Tne={kernelName:Si,backendName:"wasm",setupFunc:Ine,kernelFunc:Nne},D2;function Sne(e){D2=e.wasm.cwrap(Ci,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 Cne(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=_.computeConv2DInfo(r.shape,s.shape,l,u,c,d,!0),g=Ap[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,x=f.outChannels,v=0;if(i!=null){let oe=a.dataIdMap.get(i.dataId);if(oe.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${oe.shape.length}.`);if(oe.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${oe.shape}) does not match the number of output channels (${x})`);v=oe.id}let N=f.filterHeight,T=f.filterWidth,E=f.padInfo.top,A=f.padInfo.right,$=f.padInfo.bottom,O=f.padInfo.left,V=f.dilationHeight,W=f.dilationWidth,H=f.strideHeight,X=f.strideWidth,q=f.inChannels,K=f.padInfo.type==="SAME"?1:0,J=f.batchSize,te=f.inHeight,Q=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let ie=a.makeOutput(f.outShape,"float32"),re=a.dataIdMap.get(ie.dataId).id,ae=o==null?0:a.dataIdMap.get(o.dataId).id;return D2(y,J,te,Q,b,N,T,v,E,A,$,O,K,V,W,H,X,q,x,g,ae,m||0,re),ie}var Ene={kernelName:Ci,backendName:"wasm",setupFunc:Sne,kernelFunc:Cne},M2;function _ne(e){M2=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]=_y.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 M2(d,Vn[a.dtype],h,i,p,o,m,f),c}var Ane={kernelName:rl,backendName:"wasm",setupFunc:_ne,kernelFunc:Fne},R2;function $ne(e){R2=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Dne(e){let{backend:t,inputs:n,attrs:a}=e,{x:r,indices:s}=n,{axis:i,batchDims:o}=a,l=k.parseAxisParam(i,r.shape)[0],c=_.segment_util.collectGatherOpShapeInfo(r,s,l,o),u=La({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),p=k.sizeFromShape(s.shape),d=La({inputs:{x:s},attrs:{shape:[c.batchSize,p/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize],m=t.makeOutput(h,r.dtype);if(k.sizeFromShape(r.shape)===0)return m;let f=u.shape.length-1,g=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,b=t.dataIdMap.get(m.dataId).id,x=new Uint8Array(new Int32Array(k.computeStrides(u.shape)).buffer),v=new Uint8Array(new Int32Array(k.computeStrides(h)).buffer);return R2(g,Vn[r.dtype],x,f,y,c.batchSize,v,b),m.shape=c.outputShape,m}var Mne={kernelName:al,backendName:"wasm",setupFunc:$ne,kernelFunc:Dne},Rne=!1,Pne=bn(sl,Rne,"bool"),One=!1,Lne=bn(Ys,One,"bool"),P2;function zne(e){P2=e.wasm.cwrap(Js,null,["number","number","number"])}function Bne(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;P2(r,n,i)}return s}var Wne={kernelName:Js,backendName:"wasm",setupFunc:zne,kernelFunc:Bne},Vne=!1,Une=bn(cl,Vne,"bool"),Gne=!1,Hne=bn(pl,Gne,"bool"),jne=Un(Zs),qne=!1,Kne=bn(hl,qne,"bool"),O2;function Xne(e){O2=e.wasm.cwrap(Qs,null,["number, number, number"])}function Yne(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}=Eu(i,r,t);if(d){let b=t.dataIdMap.get(c.dataId).id;l=c,o=b}let h=l.shape.length;_.assertAxesAreInnerMostDims("max",u,h);let[m,f]=_.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;O2(o,g,b)}if(d&&t.disposeData(c.dataId),s){let b=_.expandShapeToKeepDim(y.shape,p);y.shape=b}return y}var Jne={kernelName:Qs,backendName:"wasm",setupFunc:Xne,kernelFunc:Yne},Zne=!1,Qne=bn(ei,Zne),L2;function eae(e){L2=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tae(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=_.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,x=u.strideHeight,v=u.strideWidth,N=u.inChannels,T=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let E=a.makeOutput(u.outShape,"float32"),A=a.dataIdMap.get(E.dataId).id;return L2(s,r.shape[0],r.shape[1],r.shape[2],p,d,h,m,f,g,y,b,x,v,N,T,A),E}var nae={kernelName:ti,backendName:"wasm",setupFunc:eae,kernelFunc:tae},z2;function aae(e){z2=e.wasm.cwrap(ni,null,["number, number, number"])}function rae(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}=Eu(i,r,t),m=p;if(h){let v=t.dataIdMap.get(u.dataId).id;v!==o&&(c=u,l=v,m=_.getInnerMostAxes(m.length,c.shape.length))}_.assertAxesAreInnerMostDims("mean",m,c.shape.length);let[f,g]=_.computeOutAndReduceShapes(c.shape,m),y=k.sizeFromShape(g),b=c;c.dtype!=="float32"&&(b=gf({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(b.dataId).id);let x=t.makeOutput(f,"float32");if(k.sizeFromShape(c.shape)!==0){let v=t.dataIdMap.get(x.dataId).id;z2(l,y,v)}if(h&&t.disposeData(u.dataId),s){let v=_.expandShapeToKeepDim(x.shape,d);x.shape=v}return c.dtype!=="float32"&&t.disposeData(b.dataId),x}var sae={kernelName:ni,backendName:"wasm",setupFunc:aae,kernelFunc:rae},B2;function iae(e){B2=e.wasm.cwrap(ai,null,["number, number, number"])}function oae(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}=Eu(i,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==o&&(c=u,l=x)}let m=c.shape.length;_.assertAxesAreInnerMostDims("min",p,m);let[f,g]=_.computeOutAndReduceShapes(c.shape,p),y=k.sizeFromShape(g),b=t.makeOutput(f,c.dtype);if(k.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(b.dataId).id;B2(l,y,x)}if(h&&t.disposeData(u.dataId),s){let x=_.expandShapeToKeepDim(b.shape,d);b.shape=x}return b}var lae={kernelName:ai,backendName:"wasm",setupFunc:iae,kernelFunc:oae},uae=!1,cae=bn(ri,uae),pae=!0,dae=bn(si,pae),hae=Un(fl);function sw(e,t){let n=new 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 W2;function mae(e){W2=e.wasm.cwrap(yl,"number",["number","number","number","number","number"])}function fae(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=W2(c,u,s,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=sw(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",d)}var gae={kernelName:yl,backendName:"wasm",setupFunc:mae,kernelFunc:fae},V2;function yae(e){V2=e.wasm.cwrap(bl,"number",["number","number","number","number","number","bool"])}function bae(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=V2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=sw(t,d);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([],"int32",g);return[y,b]}var xae={kernelName:bl,backendName:"wasm",setupFunc:yae,kernelFunc:bae},U2;function vae(e){U2=e.wasm.cwrap(xl,"number",["number","number","number","number","number","number"])}function wae(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=U2(u,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:g}=sw(t,d);t.wasm._free(g);let y=t.makeOutput([m],"int32",h),b=t.makeOutput([m],"float32",f);return[y,b]}var 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i(o,r);this.loadFromWeightMap(l)}loadFromWeightMap(t){let{paramMappings:n,params:a}=this.extractParamsFromWeigthMap(t);this._paramMappings=n,this._params=a}extractWeights(t){let{paramMappings:n,params:a}=this.extractParams(t);this._paramMappings=n,this._params=a}traversePropertyPath(t){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");let n=t.split("/").reduce((s,i)=>{if(!s.nextObj.hasOwnProperty(i))throw new Error(`traversePropertyPath - object does not have property ${i}, for path ${t}`);return{obj:s.nextObj,objProp:i,nextObj:s.nextObj[i]}},{nextObj:this.params}),{obj:a,objProp:r}=n;if(!a||!r||!(a[r]instanceof z))throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${t}`);return{obj:a,objProp:r}}};function _n(e,t,n){return D(()=>{let a=Oi(e,t.depthwise_filter,t.pointwise_filter,n,"same");return a=Z(a,t.bias),a})}function Rf(e,t,n=!1){return D(()=>{let a=Xe(n?Z($t(e,t.conv0.filters,[2,2],"same"),t.conv0.bias):_n(e,t.conv0,[2,2])),r=_n(a,t.conv1,[1,1]),s=Xe(Z(a,r)),i=_n(s,t.conv2,[1,1]);return Xe(Z(a,Z(r,i)))})}function Op(e,t,n=!1,a=!0){return D(()=>{let r=Xe(n?Z($t(e,t.conv0.filters,a?[2,2]:[1,1],"same"),t.conv0.bias):_n(e,t.conv0,a?[2,2]:[1,1])),s=_n(r,t.conv1,[1,1]),i=Xe(Z(r,s)),o=_n(i,t.conv2,[1,1]),l=Xe(Z(r,Z(s,o))),c=_n(l,t.conv3,[1,1]);return Xe(Z(r,Z(s,Z(o,c))))})}function co(e,t,n="same",a=!1){return D(()=>{let r=Z($t(e,t.filters,[1,1],n),t.bias);return a?Xe(r):r})}function vn(e,t){Object.keys(e).forEach(n=>{t.some(a=>a.originalPath===n)||e[n].dispose()})}function Ru(e,t){return(n,a,r,s)=>{let i=_a(e(n*a*r*r),[r,r,n,a]),o=tt(e(a));return t.push({paramPath:`${s}/filters`},{paramPath:`${s}/bias`}),{filters:i,bias:o}}}function Pf(e,t){return(n,a,r)=>{let s=Ea(e(n*a),[n,a]),i=tt(e(a));return t.push({paramPath:`${r}/weights`},{paramPath:`${r}/bias`}),{weights:s,bias:i}}}var Of=class{constructor(t,n,a){this.depthwise_filter=t;this.pointwise_filter=n;this.bias=a}};function Pu(e,t){return(n,a,r)=>{let s=_a(e(3*3*n),[3,3,n,1]),i=_a(e(n*a),[1,1,n,a]),o=tt(e(a));return t.push({paramPath:`${r}/depthwise_filter`},{paramPath:`${r}/pointwise_filter`},{paramPath:`${r}/bias`}),new Of(s,i,o)}}function Ou(e){return t=>{let n=e(`${t}/depthwise_filter`,4),a=e(`${t}/pointwise_filter`,4),r=e(`${t}/bias`,1);return new Of(n,a,r)}}function Hn(e,t){return(n,a,r)=>{let s=e[n];if(!to(s,a))throw new Error(`expected weightMap[${n}] to be a Tensor${a}D, instead have ${s}`);return t.push({originalPath:n,paramPath:r||n}),s}}function wn(e){let t=e;function n(r){let s=t.slice(0,r);return t=t.slice(r),s}function a(){return t}return{extractWeights:n,getRemainingWeights:a}}function Lf(e,t){let n=Ru(e,t),a=Pu(e,t);function r(i,o,l,c=!1){let u=c?n(i,o,3,`${l}/conv0`):a(i,o,`${l}/conv0`),p=a(o,o,`${l}/conv1`),d=a(o,o,`${l}/conv2`);return{conv0:u,conv1:p,conv2:d}}function s(i,o,l,c=!1){let{conv0:u,conv1:p,conv2:d}=r(i,o,l,c),h=a(o,o,`${l}/conv3`);return{conv0:u,conv1:p,conv2:d,conv3:h}}return{extractDenseBlock3Params:r,extractDenseBlock4Params:s}}function fC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=wn(e),{extractDenseBlock4Params:r}=Lf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2"),l=r(128,256,"dense3");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o,dense3:l}}}function zf(e){return t=>{let n=e(`${t}/filters`,4),a=e(`${t}/bias`,1);return{filters:n,bias:a}}}function Bf(e,t){let n=Hn(e,t),a=zf(n),r=Ou(n);function s(o,l=!1){let c=l?a(`${o}/conv0`):r(`${o}/conv0`),u=r(`${o}/conv1`),p=r(`${o}/conv2`);return{conv0:c,conv1:u,conv2:p}}function i(o,l=!1){let 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Vf.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var Wp=class extends Bp{constructor(t=new Lp){super("FaceExpressionNet",t)}forwardInput(t){return D(()=>Ca(this.runNet(t)))}async forward(t){return this.forwardInput(await ft(t))}async predictExpressions(t){let n=await ft(t),a=await this.forwardInput(n),r=await Promise.all(pt(a).map(async i=>{let o=await i.data();return i.dispose(),o}));a.dispose();let s=r.map(i=>new Pr(i));return n.isBatchInput?s:s[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Uf(e){return e.expressions instanceof Pr}function Vp(e,t){return{...e,...{expressions:t}}}function mse(e,t,n=.1,a){(Array.isArray(t)?t:[t]).forEach(s=>{let i=s instanceof Pr?s:Uf(s)?s.expressions:void 0;if(!i)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let l=i.asSortedArray().filter(p=>p.probability>n),c=Ba(s)?s.detection.box.bottomLeft:a||new De(0,0);new ws(l.map(p=>`${p.expression} (${no(p.probability)})`),c).draw(e)})}function Ns(e){return Ba(e)&&e.landmarks instanceof Gn&&e.unshiftedLandmarks instanceof Gn&&e.alignedRect instanceof yt}function po(e,t){let{box:n}=e.detection,a=t.shiftBy(n.x,n.y),r=a.align(),{imageDims:s}=e.detection,i=new yt(e.detection.score,r.rescale(s.reverse()),s);return{...e,...{landmarks:a,unshiftedLandmarks:t,alignedRect:i}}}var vw=class{constructor(t={}){let{drawLines:n=!0,drawPoints:a=!0,lineWidth:r,lineColor:s,pointSize:i,pointColor:o}=t;this.drawLines=n,this.drawPoints=a,this.lineWidth=r||1,this.pointSize=i||2,this.lineColor=s||"rgba(0, 255, 255, 1)",this.pointColor=o||"rgba(255, 0, 255, 1)"}},ww=class{constructor(t,n={}){this.faceLandmarks=t,this.options=new vw(n)}draw(t){let n=xn(t),{drawLines:a,drawPoints:r,lineWidth:s,lineColor:i,pointSize:o,pointColor:l}=this.options;if(a&&this.faceLandmarks instanceof 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d=s("exit_flow/reduction_block"),h=r("exit_flow/separable_conv"),m={reduction_block:d,separable_conv:h};return vn(e,n),{params:{entry_flow:u,middle_flow:p,exit_flow:m},paramMappings:n}}function kC(e,t,n){return Z($t(e,t.filters,n,"same"),t.bias)}function kw(e,t,n=!0){let a=n?Xe(e):e;return a=_n(a,t.separable_conv0,[1,1]),a=_n(Xe(a),t.separable_conv1,[1,1]),a=Dt(a,[3,3],[2,2],"same"),a=Z(a,kC(e,t.expansion_conv,[2,2])),a}function bse(e,t){let n=_n(Xe(e),t.separable_conv0,[1,1]);return n=_n(Xe(n),t.separable_conv1,[1,1]),n=_n(Xe(n),t.separable_conv2,[1,1]),n=Z(n,e),n}var Iw=class extends nn{constructor(t){super("TinyXception");this._numMainBlocks=t}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyXception - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=va(a,[122.782,117.001,104.298]).div(de(256)),i=Xe(kC(s,n.entry_flow.conv_in,[2,2]));return i=kw(i,n.entry_flow.reduction_block_0,!1),i=kw(i,n.entry_flow.reduction_block_1),or(this._numMainBlocks,0,1).forEach(o=>{i=bse(i,n.middle_flow[`main_block_${o}`])}),i=kw(i,n.exit_flow.reduction_block),i=Xe(_n(i,n.exit_flow.separable_conv,[1,1])),i})}async forward(t){return this.forwardInput(await ft(t))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(t){return wC(t,this._numMainBlocks)}extractParams(t){return vC(t,this._numMainBlocks)}};function IC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=wn(e),r=Pf(n,t),s=r(512,1,"fc/age"),i=r(512,2,"fc/gender");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{fc:{age:s,gender:i}}}}function NC(e){let t=[],n=Hn(e,t);function a(s){let i=n(`${s}/weights`,2),o=n(`${s}/bias`,1);return{weights:i,bias:o}}let r={fc:{age:a("fc/age"),gender:a("fc/gender")}};return vn(e,t),{params:r,paramMappings:t}}var pr;(function(e){e.FEMALE="female",e.MALE="male"})(pr||(pr={}));var Up=class extends nn{constructor(t=new Iw(2)){super("AgeGenderNet");this._faceFeatureExtractor=t}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(t){let{params:n}=this;if(!n)throw new Error(`${this._name} - load model before inference`);return D(()=>{let a=t instanceof cr?this.faceFeatureExtractor.forwardInput(t):t,r=Zn(a,[7,7],[2,2],"valid").as2D(a.shape[0],-1),s=zp(r,n.fc.age).as1D(),i=zp(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:Ca(a)}})}async forward(t){return this.forwardInput(await ft(t))}async predictAgeAndGender(t){let n=await ft(t),a=await this.forwardInput(n),r=pt(a.age),s=pt(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?pr.MALE:pr.FEMALE,m=d?p:1-p;return l.dispose(),c.dispose(),{age:u,gender:h,genderProbability:m}}));return a.age.dispose(),a.gender.dispose(),n.isBatchInput?o:o[0]}getDefaultModelName(){return"age_gender_model"}dispose(t=!0){this.faceFeatureExtractor.dispose(t),super.dispose(t)}loadClassifierParams(t){let{params:n,paramMappings:a}=this.extractClassifierParams(t);this._params=n,this._paramMappings=a}extractClassifierParams(t){return IC(t)}extractParamsFromWeigthMap(t){let{featureExtractorMap:n,classifierMap:a}=Wf(t);return this.faceFeatureExtractor.loadFromWeightMap(n),NC(a)}extractParams(t){let n=512*1+1+(512*2+2),a=t.slice(0,t.length-n),r=t.slice(t.length-n);return this.faceFeatureExtractor.extractWeights(a),this.extractClassifierParams(r)}};var Gp=class extends Bp{postProcess(t,n,a){let r=a.map(({width:i,height:o})=>{let l=n/Math.max(o,i);return{width:i*l,height:o*l}}),s=r.length;return D(()=>{let i=(p,d)=>Mt([Nn([68],p,"float32"),Nn([68],d,"float32")],1).as2D(1,136).as1D(),o=(p,d)=>{let{width:h,height:m}=r[p];return 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256}};function TC(e){let t=[],{extractDenseBlock3Params:n}=Bf(e,t),a={dense0:n("dense0",!0),dense1:n("dense1"),dense2:n("dense2")};return vn(e,t),{params:a,paramMappings:t}}function SC(e){let t=[],{extractWeights:n,getRemainingWeights:a}=wn(e),{extractDenseBlock3Params:r}=Lf(n,t),s=r(3,32,"dense0",!0),i=r(32,64,"dense1"),o=r(64,128,"dense2");if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{paramMappings:t,params:{dense0:s,dense1:i,dense2:o}}}var Nw=class extends nn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(t){let{params:n}=this;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return D(()=>{let a=pe(t.toBatchTensor(112,!0),"float32"),s=va(a,[122.782,117.001,104.298]).div(de(255)),i=Rf(s,n.dense0,!0);return i=Rf(i,n.dense1),i=Rf(i,n.dense2),i=Zn(i,[14,14],[2,2],"valid"),i})}async forward(t){return this.forwardInput(await ft(t))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(t){return TC(t)}extractParams(t){return SC(t)}};var Hp=class extends Gp{constructor(t=new Nw){super("FaceLandmark68TinyNet",t)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}};var Tw=class extends ho{};function CC(e,t){return Z(L(e,t.weights),t.biases)}function Sw(e,t,n,a,r="same"){let{filters:s,bias:i}=t.conv,o=$t(e,s,n,r);return o=Z(o,i),o=CC(o,t.scale),a?Xe(o):o}function EC(e,t){return Sw(e,t,[1,1],!0)}function Cw(e,t){return Sw(e,t,[1,1],!1)}function Hf(e,t){return Sw(e,t,[2,2],!0,"valid")}function xse(e,t){function n(o,l,c){let u=e(o),p=u.length/(l*c*c);if(uw(p))throw new Error(`depth has to be an integer: ${p}, weights.length: ${u.length}, numFilters: ${l}, filterSize: ${c}`);return D(()=>Ue(_a(u,[l,p,c,c]),[2,3,1,0]))}function a(o,l,c,u){let p=n(o,l,c),d=tt(e(l));return t.push({paramPath:`${u}/filters`},{paramPath:`${u}/bias`}),{filters:p,bias:d}}function r(o,l){let c=tt(e(o)),u=tt(e(o));return t.push({paramPath:`${l}/weights`},{paramPath:`${l}/biases`}),{weights:c,biases:u}}function s(o,l,c,u){let p=a(o,l,c,`${u}/conv`),d=r(l,`${u}/scale`);return{conv:p,scale:d}}function i(o,l,c,u,p=!1){let d=s((p?.5:1)*o,l,c,`${u}/conv1`),h=s(o,l,c,`${u}/conv2`);return{conv1:d,conv2:h}}return{extractConvLayerParams:s,extractResidualLayerParams:i}}function _C(e){let{extractWeights:t,getRemainingWeights:n}=wn(e),a=[],{extractConvLayerParams:r,extractResidualLayerParams:s}=xse(t,a),i=r(4704,32,7,"conv32_down"),o=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),c=s(9216,32,3,"conv32_3"),u=s(36864,64,3,"conv64_down",!0),p=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),h=s(36864,64,3,"conv64_3"),m=s(147456,128,3,"conv128_down",!0),f=s(147456,128,3,"conv128_1"),g=s(147456,128,3,"conv128_2"),y=s(589824,256,3,"conv256_down",!0),b=s(589824,256,3,"conv256_1"),x=s(589824,256,3,"conv256_2"),v=s(589824,256,3,"conv256_down_out"),N=D(()=>Ue(Ea(t(256*128),[128,256]),[1,0]));if(a.push({paramPath:"fc"}),n().length!==0)throw new Error(`weights remaing after extract: ${n().length}`);return{params:{conv32_down:i,conv32_1:o,conv32_2:l,conv32_3:c,conv64_down:u,conv64_1:p,conv64_2:d,conv64_3:h,conv128_down:m,conv128_1:f,conv128_2:g,conv256_down:y,conv256_1:b,conv256_2:x,conv256_down_out:v,fc:N},paramMappings:a}}function vse(e,t){let n=Hn(e,t);function a(i){let o=n(`${i}/scale/weights`,1),l=n(`${i}/scale/biases`,1);return{weights:o,biases:l}}function r(i){let o=n(`${i}/conv/filters`,4),l=n(`${i}/conv/bias`,1),c=a(i);return{conv:{filters:o,bias:l},scale:c}}function s(i){return{conv1:r(`${i}/conv1`),conv2:r(`${i}/conv2`)}}return{extractConvLayerParams:r,extractResidualLayerParams:s}}function FC(e){let t=[],{extractConvLayerParams:n,extractResidualLayerParams:a}=vse(e,t),r=n("conv32_down"),s=a("conv32_1"),i=a("conv32_2"),o=a("conv32_3"),l=a("conv64_down"),c=a("conv64_1"),u=a("conv64_2"),p=a("conv64_3"),d=a("conv128_down"),h=a("conv128_1"),m=a("conv128_2"),f=a("conv256_down"),g=a("conv256_1"),y=a("conv256_2"),b=a("conv256_down_out"),{fc:x}=e;if(t.push({originalPath:"fc",paramPath:"fc"}),!lw(x))throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${x}`);let 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${JSON.stringify(e.meanRgb)}`)}function Lu(e){return D(()=>{let t=L(e,de(.10000000149011612));return Z(Xe(fe(e,t)),t)})}function Or(e,t){return D(()=>{let n=na(e,[[0,0],[1,1],[1,1],[0,0]]);return n=$t(n,t.conv.filters,[1,1],"valid"),n=fe(n,t.bn.sub),n=L(n,t.bn.truediv),n=Z(n,t.conv.bias),Lu(n)})}function Lr(e,t){return D(()=>{let n=na(e,[[0,0],[1,1],[1,1],[0,0]]);return n=Oi(n,t.depthwise_filter,t.pointwise_filter,[1,1],"valid"),n=Z(n,t.bias),Lu(n)})}function _se(e,t){let n=Ru(e,t);function a(i,o){let l=tt(e(i)),c=tt(e(i));return t.push({paramPath:`${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,`${l}/bn`);return{conv:c,bn:u}}let s=Pu(e,t);return{extractConvParams:n,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}}function qC(e,t,n,a){let{extractWeights:r,getRemainingWeights:s}=wn(e),i=[],{extractConvParams:o,extractConvWithBatchNormParams:l,extractSeparableConvParams:c}=_se(r,i),u;if(t.withSeparableConvs){let[p,d,h,m,f,g,y,b,x]=a,v=t.isFirstLayerConv2d?o(p,d,3,"conv0"):c(p,d,"conv0"),N=c(d,h,"conv1"),T=c(h,m,"conv2"),E=c(m,f,"conv3"),A=c(f,g,"conv4"),$=c(g,y,"conv5"),O=b?c(y,b,"conv6"):void 0,V=x?c(b,x,"conv7"):void 0,W=o(x||b||y,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:E,conv4:A,conv5:$,conv6:O,conv7:V,conv8:W}}else{let[p,d,h,m,f,g,y,b,x]=a,v=l(p,d,"conv0"),N=l(d,h,"conv1"),T=l(h,m,"conv2"),E=l(m,f,"conv3"),A=l(f,g,"conv4"),$=l(g,y,"conv5"),O=l(y,b,"conv6"),V=l(b,x,"conv7"),W=o(x,5*n,1,"conv8");u={conv0:v,conv1:N,conv2:T,conv3:E,conv4:A,conv5:$,conv6:O,conv7:V,conv8:W}}if(s().length!==0)throw new Error(`weights remaing after extract: ${s().length}`);return{params:u,paramMappings:i}}function Fse(e,t){let n=Hn(e,t);function a(o){let l=n(`${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(`${o}/bn`);return{conv:l,bn:c}}let i=Ou(n);return{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:i}}function KC(e,t){let n=[],{extractConvParams:a,extractConvWithBatchNormParams:r,extractSeparableConvParams:s}=Fse(e,n),i;if(t.withSeparableConvs){let o=t.filterSizes&&t.filterSizes.length||9;i={conv0:t.isFirstLayerConv2d?a("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:o>7?s("conv6"):void 0,conv7:o>8?s("conv7"):void 0,conv8:a("conv8")}}else i={conv0:r("conv0"),conv1:r("conv1"),conv2:r("conv2"),conv3:r("conv3"),conv4:r("conv4"),conv5:r("conv5"),conv6:r("conv6"),conv7:r("conv7"),conv8:a("conv8")};return vn(e,n),{params:i,paramMappings:n}}var Va=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!=0)throw new Error(`${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 nn{constructor(t){super("TinyYolov2");qf(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=Or(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Or(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Or(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Or(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Or(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Or(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=Or(a,n.conv6),a=Or(a,n.conv7),co(a,n.conv8,"valid",!1)}runMobilenet(t,n){let a=this.config.isFirstLayerConv2d?Lu(co(t,n.conv0,"valid",!1)):Lr(t,n.conv0);return a=Dt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv1),a=Dt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv2),a=Dt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv3),a=Dt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv4),a=Dt(a,[2,2],[2,2],"same"),a=Lr(a,n.conv5),a=Dt(a,[2,2],[1,1],"same"),a=n.conv6?Lr(a,n.conv6):a,a=n.conv7?Lr(a,n.conv7):a,co(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?va(r,this.config.meanRgb):r,r=r.div(de(256)),this.config.withSeparableConvs?this.runMobilenet(r,a):this.runTinyYolov2(r,a)})}async forward(t,n){return this.forwardInput(await ft(t),n)}async detect(t,n={}){let{inputSize:a,scoreThreshold:r}=new Va(n),s=await ft(t),i=await this.forwardInput(s,a),o=D(()=>pt(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 Nf(u.map(g=>g.rescale(a)),p,this.config.iouThreshold,!0).map(g=>new Rr(p[g],d[g],h[g],u[g],l))}getDefaultModelName(){return""}extractParamsFromWeigthMap(t){return KC(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 ${a} filterSizes in config`);return qC(t,this.config,this.boxEncodingSize,n)}async extractBoxes(t,n,a){let{width:r,height:s}=n,i=Math.max(r,s),o=i/r,l=i/s,c=t.shape[1],u=this.config.anchors.length,[p,d,h]=D(()=>{let y=t.reshape([c,c,u,this.boxEncodingSize]),b=y.slice([0,0,0,0],[c,c,u,4]),x=y.slice([0,0,0,4],[c,c,u,1]),v=this.withClassScores?Ca(y.slice([0,0,0,5],[c,c,u,this.config.classes.length]),3):de(0);return[b,x,v]}),m=[],f=await d.array(),g=await p.array();for(let y=0;y<c;y++)for(let b=0;b<c;b++)for(let x=0;x<u;x++){let v=Fu(f[y][b][x][0]);if(!a||v>a){let N=(b+Fu(g[y][b][x][0]))/c*o,T=(y+Fu(g[y][b][x][1]))/c*l,E=Math.exp(g[y][b][x][2])*this.config.anchors[x].x/c*o,A=Math.exp(g[y][b][x][3])*this.config.anchors[x].y/c*l,$=N-E/2,O=T-A/2,V={row:y,col:b,anchor:x},{classScore:W,label:H}=this.withClassScores?await this.extractPredictedClass(h,V):{classScore:1,label:0};m.push({box:new ro($,O,$+E,O+A),score:v,classScore:v*W,label:H,...V})}}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)}},zu=Fw;zu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var go=class extends zu{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:WC,classes:["face"],...t?{anchors:UC,meanRgb:GC}:{anchors:VC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?jC:HC}extractParamsFromWeigthMap(t){return super.extractParamsFromWeigthMap(t)}};function XC(e,t=!0){let n=new go(t);return n.extractWeights(e),n}var Yp=class extends Va{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var oa=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function yo(e,t,n,a,r=({alignedRect:s})=>s){let s=e.map(l=>Ns(l)?r(l):l.detection),i=a||(t instanceof z?await uo(t,s):await lo(t,s)),o=await n(i);return i.forEach(l=>l instanceof z&&l.dispose()),o}async function Bu(e,t,n,a,r){return yo([e],t,async s=>n(s[0]),a,r)}var YC=.4,JC=[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)],ZC=[117.001,114.697,97.404];var bo=class extends zu{constructor(){let t={withSeparableConvs:!0,iouThreshold:YC,classes:["face"],anchors:JC,meanRgb:ZC,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 yt(r.score,r.relativeBox,{width:r.imageWidth,height:r.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(t){return super.extractParamsFromWeigthMap(t)}};var Qe={ssdMobilenetv1:new Ts,tinyFaceDetector:new bo,tinyYolov2:new go,faceLandmark68Net:new ho,faceLandmark68TinyNet:new Hp,faceRecognitionNet:new mo,faceExpressionNet:new Wp,ageGenderNet:new Up},Aw=(e,t)=>Qe.ssdMobilenetv1.locateFaces(e,t),QC=(e,t)=>Qe.tinyFaceDetector.locateFaces(e,t),eE=(e,t)=>Qe.tinyYolov2.locateFaces(e,t),$w=e=>Qe.faceLandmark68Net.detectLandmarks(e),tE=e=>Qe.faceLandmark68TinyNet.detectLandmarks(e),nE=e=>Qe.faceRecognitionNet.computeFaceDescriptor(e),aE=e=>Qe.faceExpressionNet.predictExpressions(e),rE=e=>Qe.ageGenderNet.predictAgeAndGender(e),Dw=e=>Qe.ssdMobilenetv1.load(e),sE=e=>Qe.tinyFaceDetector.load(e),iE=e=>Qe.tinyYolov2.load(e),oE=e=>Qe.faceLandmark68Net.load(e),lE=e=>Qe.faceLandmark68TinyNet.load(e),uE=e=>Qe.faceRecognitionNet.load(e),cE=e=>Qe.faceExpressionNet.load(e),pE=e=>Qe.ageGenderNet.load(e),dE=Dw,hE=Aw,mE=$w;var Mw=class extends oa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Uu=class extends Mw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>Qe.faceExpressionNet.predictExpressions(r))),this.extractedFaces);return t.map((a,r)=>Vp(a,n[r]))}withAgeAndGender(){return new Wu(this,this.input)}},Gu=class extends Mw{async run(){let t=await this.parentTask;if(!t)return;let n=await Bu(t,this.input,a=>Qe.faceExpressionNet.predictExpressions(a),this.extractedFaces);return Vp(t,n)}withAgeAndGender(){return new Vu(this,this.input)}},wo=class extends Uu{withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new zr(this,this.input)}},ko=class extends Gu{withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Br(this,this.input)}};var Rw=class extends oa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.extractedFaces=a}},Wu=class extends Rw{async run(){let t=await this.parentTask,n=await yo(t,this.input,async a=>Promise.all(a.map(r=>Qe.ageGenderNet.predictAgeAndGender(r))),this.extractedFaces);return t.map((a,r)=>{let{age:s,gender:i,genderProbability:o}=n[r];return Kp(Xp(a,i,o),s)})}withFaceExpressions(){return new Uu(this,this.input)}},Vu=class extends Rw{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:a,genderProbability:r}=await Bu(t,this.input,s=>Qe.ageGenderNet.predictAgeAndGender(s),this.extractedFaces);return Kp(Xp(t,a,r),n)}withFaceExpressions(){return new Gu(this,this.input)}},xo=class extends Wu{withFaceExpressions(){return new wo(this,this.input)}withFaceDescriptors(){return new zr(this,this.input)}},vo=class extends Vu{withFaceExpressions(){return new ko(this,this.input)}withFaceDescriptor(){return new Br(this,this.input)}};var Jp=class extends oa{constructor(t,n){super();this.parentTask=t;this.input=n}},zr=class extends Jp{async run(){let t=await this.parentTask;return(await yo(t,this.input,a=>Promise.all(a.map(r=>Qe.faceRecognitionNet.computeFaceDescriptor(r))),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}))).map((a,r)=>qp(t[r],a))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}},Br=class extends Jp{async run(){let t=await this.parentTask;if(!t)return;let n=await Bu(t,this.input,a=>Qe.faceRecognitionNet.computeFaceDescriptor(a),null,a=>a.landmarks.align(null,{useDlibAlignment:!0}));return qp(t,n)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}};var Zp=class extends oa{constructor(t,n,a){super();this.parentTask=t;this.input=n;this.useTinyLandmarkNet=a}get landmarkNet(){return this.useTinyLandmarkNet?Qe.faceLandmark68TinyNet:Qe.faceLandmark68Net}},Qp=class extends Zp{async run(){let t=await this.parentTask,n=t.map(s=>s.detection),a=this.input instanceof z?await uo(this.input,n):await lo(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)=>po(s,r[i]))}withFaceExpressions(){return new wo(this,this.input)}withAgeAndGender(){return new xo(this,this.input)}withFaceDescriptors(){return new zr(this,this.input)}},ed=class extends Zp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,a=this.input instanceof z?await uo(this.input,[n]):await lo(this.input,[n]),r=await this.landmarkNet.detectLandmarks(a[0]);return a.forEach(s=>s instanceof z&&s.dispose()),po(t,r)}withFaceExpressions(){return new ko(this,this.input)}withAgeAndGender(){return new vo(this,this.input)}withFaceDescriptor(){return new Br(this,this.input)}};var td=class extends oa{constructor(t,n=new ia){super();this.input=t;this.options=n}},Hu=class extends td{async run(){let{input:t,options:n}=this,a=n instanceof Yp?r=>Qe.tinyFaceDetector.locateFaces(r,n):n instanceof ia?r=>Qe.ssdMobilenetv1.locateFaces(r,n):n instanceof Va?r=>Qe.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=>xs({},a)))})}withFaceLandmarks(t=!1){return new Qp(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Uu(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new Wu(this.runAndExtendWithFaceDetections(),this.input)}},nd=class extends td{async run(){let t=await new Hu(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?xs({},n):void 0)})}withFaceLandmarks(t=!1){return new ed(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Gu(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new Vu(this.runAndExtendWithFaceDetection(),this.input)}};function fE(e,t=new ia){return new nd(e,t)}function ad(e,t=new ia){return new Hu(e,t)}async function Pw(e,t){return ad(e,new ia(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function gE(e,t={}){return ad(e,new Va(t)).withFaceLandmarks().withFaceDescriptors()}var yE=Pw;function Kf(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 rd=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 lr)return i;if(i instanceof Float32Array)return new lr(s(),[i]);if(i.descriptor&&i.descriptor instanceof Float32Array)return new lr(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=>Kf(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=>lr.fromJSON(a));return new rd(n,t.distanceThreshold)}};function bE(e){let t=new bo;return t.extractWeights(e),t}function Ow(e,t){let{width:n,height:a}=new cn(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=>Ow(r,{width:n,height:a}));if(Ns(e)){let r=e.detection.forSize(n,a),s=e.unshiftedLandmarks.forSize(r.box.width,r.box.height);return po(xs(e,r),s)}return Ba(e)?xs(e,e.detection.forSize(n,a)):e instanceof Gn||e instanceof yt?e.forSize(n,a):e}var $se=typeof process!="undefined",Dse=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",xE={faceapi:xC,node:$se,browser:Dse};return Ase;})();
/**
* @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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