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o=++this.pendingBackendInitId,s=r.then(c=>o<this.pendingBackendInitId?!1:(this.registry[t]=c,this.pendingBackendInit=null,!0)).catch(c=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${t} failed`),console.warn(c.stack||c.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=r,{success:!0,asyncInit:!1}}catch(r){return console.warn(`Initialization of backend ${t} failed`),console.warn(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(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((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let r=t[e],{success:o,asyncInit:s}=this.initializeBackend(r);if(s||o)return{name:r,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let r=this.state.tensorInfo.get(e),o=r.backend,s=this.readSync(e);o.disposeData(e),r.backend=t,t.move(e,s,r.shape,r.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let r=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to 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this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,r){let o=this.backend.numDataIds(),s=0;r.forEach(p=>{s+=p.dtype==="complex64"?3:1});let c=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],l=o-e-s-c;if(l>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${l} data ids) after running '${t}'`)}runKernelFunc(t,e,r,o,s,c,l){let p,f=[],m=this.isTapeOn();o==null&&(o=this.state.activeScope!=null?this.state.activeScope.name:"");let y=this.state.numBytes,b=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let v,T=Gx(o,this.backendName),N;if(T!=null)v=()=>{let D=this.backend.numDataIds();N=T.kernelFunc({inputs:e,attrs:s,backend:this.backend});let I=Array.isArray(N)?N:[N];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,D,I);let P=I.map(({dataId:E,shape:L,dtype:B})=>this.makeTensorFromDataId(E,L,B));if(m){let E=this.getTensorsForGradient(o,e,P);if(E==null){l==null&&(l=[]);let L=P.filter((B,q)=>l[q]);E=(c||[]).slice().concat(L)}f=this.saveTensorsForBackwardMode(E)}return P};else{let D=I=>{if(!m)return;f=I.map(P=>this.keep(this.clone(P)))};v=()=>{let I=this.backend.numDataIds();N=this.tidy(()=>t(this.backend,D));let P=Array.isArray(N)?N:[N];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,I,P),P}}let S;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?p=v():(S=this.profiler.profileKernel(o,e,()=>v()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(S),p=S.outputs)}),m&&this.addTapeNode(o,e,p,r,f,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:o,bytesAdded:this.state.numBytes-y,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-b,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(e).map(D=>e[D]!=null?e[D].shape:null),outputShapes:p.map(D=>D.shape),kernelTimeMs:S.timeMs,extraInfo:S.extraInfo}),Array.isArray(N)?p:p[0]}saveTensorsForBackwardMode(t){let e=t.map(r=>this.keep(this.clone(r)));return e}getTensorsForGradient(t,e,r){let o=Ux(t);if(o!=null){let s=o.inputsToSave||[],c=o.outputsToSave||[],l;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an 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r=this.state.tensorInfo.has(t.dataId)?this.state.tensorInfo.get(t.dataId).refCount:0;if(this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++,r===0){this.state.numDataBuffers++;let o=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(o=t.size*Zb(t.dtype)),this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:o,refCount:0}),this.state.numBytes+=o}this.state.tensorInfo.get(t.dataId).refCount++,t instanceof tp||this.track(t)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;this.state.numTensors--,t.dtype==="string"&&this.state.numStringTensors--;let e=this.state.tensorInfo.get(t.dataId),r=e.refCount;r<=1?(t.dtype!=="complex64"&&(this.state.numBytes-=e.bytes),this.state.numDataBuffers--,e.backend.disposeData(t.dataId),this.state.tensorInfo.delete(t.dataId)):this.state.tensorInfo.get(t.dataId).refCount--}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let o of 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c=this.state.activeScope.track[s];!c.kept&&!r.has(c.id)&&c.dispose()}let o=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],e.forEach(s=>{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,r,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof ot,()=>"The result y returned by f() must be a tensor.");let c=KP(this.state.activeTape,e,s);if(!o&&c.length===0&&e.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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JN=n=>ct().getBool("IS_BROWSER")&&(!Array.isArray(n)&&n.startsWith(ta.URL_SCHEME))?bO(n.slice(ta.URL_SCHEME.length)):null;Ze.registerSaveRouter(JN),Ze.registerLoadRouter(JN);function bO(n){return new ta(n)}function xO(n){return n.startsWith(ta.URL_SCHEME)?n.slice(ta.URL_SCHEME.length):n}class wO{constructor(){this.indexedDB=ow()}async listModels(){return new Promise((t,e)=>{let r=this.indexedDB.open(pd,rw);r.onupgradeneeded=()=>sw(r),r.onsuccess=()=>{let o=r.result,s=o.transaction(si,"readonly"),c=s.objectStore(si),l=c.getAll();l.onsuccess=()=>{let p={};for(let f of l.result)p[f.modelPath]=f.modelArtifactsInfo;t(p)},l.onerror=p=>(o.close(),e(l.error)),s.oncomplete=()=>o.close()},r.onerror=o=>e(r.error)})}async removeModel(t){return t=xO(t),new Promise((e,r)=>{let o=this.indexedDB.open(pd,rw);o.onupgradeneeded=()=>sw(o),o.onsuccess=()=>{let s=o.result,c=s.transaction(si,"readwrite"),l=c.objectStore(si),p=l.get(t),f;p.onsuccess=()=>{if(p.result==null)return s.close(),r(new Error(`Cannot 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cp=j({batchToSpaceND_:sM});function iM(n){let t;return n.rank===0||n.rank===1?t=Q(n,[1,1,1,n.size]):n.rank===2?t=Q(n,[1,1,n.shape[0],n.shape[1]]):n.rank===3?t=Q(n,[1,n.shape[0],n.shape[1],n.shape[2]]):t=n,t}function aM(n,t,e,r,o,s){s==null&&(s=.001);let c=M(n,"x","batchNorm"),l=M(t,"mean","batchNorm"),p=M(e,"variance","batchNorm"),f;o!=null&&(f=M(o,"scale","batchNorm"));let m;r!=null&&(m=M(r,"offset","batchNorm")),_(l.rank===p.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(m==null||l.rank===m.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(f==null||l.rank===f.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let y=iM(c),b=(S,D)=>(D([y,l,p,f]),S.batchNorm(y,_d(l),_d(p),_d(m),_d(f),s)),v={x:y,scale:f,offset:m,mean:l,variance:p},T={varianceEpsilon:s},N=X.runKernelFunc(b,v,null,ku,T);return Q(N,c.shape)}function _d(n){return 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QW={fft:wp,ifft:qc,rfft:vp,irfft:qd},t4={hammingWindow:Jz,hannWindow:xC,frame:wC,stft:eW},mi={flipLeftRight:sW,resizeNearestNeighbor:kC,resizeBilinear:TC,rotateWithOffset:aW,cropAndResize:rW,nonMaxSuppression:lW,nonMaxSuppressionAsync:yW,nonMaxSuppressionWithScore:xW,nonMaxSuppressionWithScoreAsync:vW,nonMaxSuppressionPadded:kW,nonMaxSuppressionPaddedAsync:_W},_C={bandPart:IW,gramSchmidt:DW,qr:FW},e4={absoluteDifference:OW,computeWeightedLoss:bs,cosineDistance:MW,hingeLoss:zW,huberLoss:VW,logLoss:UW,meanSquaredError:HW,sigmoidCrossEntropy:XW,softmaxCrossEntropy:ZW};class xs extends sa{minimize(t,e=!1,r){let{value:o,grads:s}=this.computeGradients(t,r);if(r!=null){let c=r.map(l=>({name:l.name,tensor:s[l.name]}));this.applyGradients(c)}else this.applyGradients(s);return Xt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Ww(t,e)}dispose(){this.iterations_!=null&&Xt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Et(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}}Object.defineProperty(xs,Symbol.hasInstance,{value:n=>n.minimize!=null&&n.computeGradients!=null&&n.applyGradients!=null});class Np extends xs{constructor(t,e,r=null){super();this.learningRate=t,this.rho=e,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${r}/accum_grad`,variable:rt(()=>re(s).variable(c))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${r}/accum_var`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedGrads[o].variable,f=this.accumulatedUpdates[o].variable;rt(()=>{let m=Tt(nt(p,this.rho),nt(Ae(l),1-this.rho)),y=nt(Bt(On(Tt(f,this.epsilon)),On(Tt(p,this.epsilon))),l),b=Tt(nt(f,this.rho),nt(Ae(y),1-this.rho));p.assign(m),f.assign(b);let v=Tt(nt(y,-this.learningRate),s);s.assign(v)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Xt(this.accumulatedGrads.map(t=>t.variable)),Xt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,r=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}}Np.className="Adadelta",vt(Np);class _p extends xs{constructor(t,e=.1){super();this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulatedGrads[o]==null){let p=!1;this.accumulatedGrads[o]={originalName:`${r}/accumulator`,variable:rt(()=>Lc(s.shape,this.initialAccumulatorValue).variable(p))}}let c=Array.isArray(t)?t[o].tensor:t[r];if(c==null)return;let l=this.accumulatedGrads[o].variable;rt(()=>{let p=Tt(l,Ae(c));l.assign(p);let f=Tt(nt(Bt(c,On(Tt(p,X.backend.epsilon()))),-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Xt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}}_p.className="Adagrad",vt(_p);class Cp extends xs{constructor(t,e,r,o=null){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],rt(()=>{this.accBeta1=Et(e).variable(),this.accBeta2=Et(r).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Dt(1,this.accBeta2);e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:rt(()=>re(l).variable(p))}),this.accumulatedSecondMoment[c]==null&&(this.accumulatedSecondMoment[c]={originalName:`${s}/v`,variable:rt(()=>re(l).variable(p))});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedSecondMoment[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=Tt(nt(y,this.beta2),nt(Ae(f),1-this.beta2)),T=Bt(b,r),N=Bt(v,o);m.assign(b),y.assign(v);let S=Tt(nt(Bt(T,Tt(On(N),this.epsilon)),-this.learningRate),l);l.assign(S)}),this.accBeta1.assign(nt(this.accBeta1,this.beta1)),this.accBeta2.assign(nt(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Xt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),rt(()=>{this.accBeta1.assign(mo(this.beta1,this.iterations_+1)),this.accBeta2.assign(mo(this.beta2,this.iterations_+1))});let e=t.length/2,r=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}}Cp.className="Adam",vt(Cp);class Sp extends xs{constructor(t,e,r,o=null,s=0){super();this.learningRate=t,this.beta1=e,this.beta2=r,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],rt(()=>{this.iteration=Et(0).variable(),this.accBeta1=Et(e).variable()}),o==null&&(this.epsilon=X.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);rt(()=>{let r=Dt(1,this.accBeta1),o=Bt(-this.learningRate,Tt(nt(this.iteration,this.decay),1));e.forEach((s,c)=>{let l=X.registeredVariables[s],p=!1;this.accumulatedFirstMoment[c]==null&&(this.accumulatedFirstMoment[c]={originalName:`${s}/m`,variable:re(l).variable(p)}),this.accumulatedWeightedInfNorm[c]==null&&(this.accumulatedWeightedInfNorm[c]={originalName:`${s}/v`,variable:re(l).variable(p)});let f=Array.isArray(t)?t[c].tensor:t[s];if(f==null)return;let m=this.accumulatedFirstMoment[c].variable,y=this.accumulatedWeightedInfNorm[c].variable,b=Tt(nt(m,this.beta1),nt(f,1-this.beta1)),v=nt(y,this.beta2),T=bn(f),N=Jr(v,T);m.assign(b),y.assign(N);let S=Tt(nt(Bt(o,r),Bt(b,Tt(N,this.epsilon))),l);l.assign(S)}),this.iteration.assign(Tt(this.iteration,1)),this.accBeta1.assign(nt(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Xt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Xt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){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(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}}Sp.className="Adamax",vt(Sp);class Xc extends xs{constructor(t){super();this.learningRate=t,this.setLearningRate(t)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=Array.isArray(t)?t[o].tensor:t[r];if(s==null)return;let c=X.registeredVariables[r];rt(()=>{let l=Tt(nt(this.c,s),c);c.assign(l)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=Sn(Et(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}}Xc.className="SGD",vt(Xc);class $p extends Xc{constructor(t,e,r=!1){super(t);this.learningRate=t,this.momentum=e,this.useNesterov=r,this.accumulations=[],this.m=Et(this.momentum)}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r];if(this.accumulations[o]==null){let p=!1;this.accumulations[o]={originalName:`${r}/momentum`,variable:rt(()=>re(s).variable(p))}}let c=this.accumulations[o].variable,l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;rt(()=>{let p,f=Tt(nt(this.m,c),l);this.useNesterov?p=Tt(nt(this.c,Tt(l,nt(f,this.m))),s):p=Tt(nt(this.c,f),s),c.assign(f),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Xt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(r=>({originalName:r.name,variable:r.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}}$p.className="Momentum",vt($p);class Ip extends xs{constructor(t,e=.9,r=0,o=null,s=!1){super();if(this.learningRate=t,this.decay=e,this.momentum=r,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=X.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){let e=Array.isArray(t)?t.map(r=>r.name):Object.keys(t);e.forEach((r,o)=>{let s=X.registeredVariables[r],c=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${r}/rms`,variable:rt(()=>re(s).variable(c))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${r}/momentum`,variable:rt(()=>re(s).variable(c))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${r}/mg`,variable:rt(()=>re(s).variable(c))});let l=Array.isArray(t)?t[o].tensor:t[r];if(l==null)return;let p=this.accumulatedMeanSquares[o].variable,f=this.accumulatedMoments[o].variable;rt(()=>{let m=Tt(nt(p,this.decay),nt(Ae(l),1-this.decay));if(this.centered){let y=this.accumulatedMeanGrads[o].variable,b=Tt(nt(y,this.decay),nt(l,1-this.decay)),v=Bt(nt(l,this.learningRate),On(Dt(m,Tt(Ae(b),this.epsilon)))),T=Tt(nt(f,this.momentum),v);p.assign(m),y.assign(b),f.assign(T);let N=Dt(s,T);s.assign(N)}else{let y=Tt(nt(p,this.decay),nt(Ae(l),1-this.decay)),b=Tt(nt(f,this.momentum),Bt(nt(l,this.learningRate),On(Tt(y,this.epsilon))));p.assign(y),f.assign(b);let v=Dt(s,b);s.assign(v)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Xt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Xt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Xt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,r=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}}Ip.className="RMSProp",vt(Ip);class ma{static sgd(t){return new Xc(t)}static momentum(t,e,r=!1){return new $p(t,e,r)}static rmsprop(t,e=.9,r=0,o=null,s=!1){return new Ip(t,e,r,o,s)}static adam(t=.001,e=.9,r=.999,o=null){return new Cp(t,e,r,o)}static adadelta(t=.001,e=.95,r=null){return new Np(t,e,r)}static adamax(t=.002,e=.9,r=.999,o=null,s=0){return new Sp(t,e,r,o,s)}static adagrad(t,e=.1){return new _p(t,e)}}let ga={sgd:ma.sgd,momentum:ma.momentum,adadelta:ma.adadelta,adagrad:ma.adagrad,rmsprop:ma.rmsprop,adamax:ma.adamax,adam:ma.adam};let n4=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:n=>n())();function rm(){return new Promise(n=>n4(()=>n()))}function uv(n,t,e){let r=e*(typeof n=="number"?n:n[0]),o=t*(typeof n=="number"?n:n[1]);return[r,o]}function Ep(n,t,e,r=!0){let o=[];if(r)o=o.concat(t.slice(0)),o.push(n[0]/e),o=o.concat(n.slice(1));else{o=o.concat(n[0]);let s=t.length;for(let c=0;c<s;++c)o=o.concat([n[c+1]/t[c],t[c]]);o=o.concat(n.slice(s+1))}return o}function Dp(n,t,e=!0){let r=[];if(e){r.push(t);for(let o=t+1;o<n;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{let o=[],s=[];for(let c=1;c<n;++c)c>=t*2+1||c%2===1?s.push(c):o.push(c);r.push(...o),r.push(0),r.push(...s)}return r}function Ap(n,t,e,r=!0){let o=[];r?o.push(n[0]/e):o.push(n[0]*e);for(let s=1;s<n.length;++s)s<=t.length?r?o.push(t[s-1]*n[s]):o.push(n[s]/t[s-1]):o.push(n[s]);return o}function pv(n,t){let e=[0];for(let r=0;r<t;++r)e.push(n[r][0]);return e}function hv(n,t,e){let r=n.slice(0,1);for(let o=0;o<e;++o)r.push(n[o+1]-t[o][0]-t[o][1]);return r}let om=1.7580993408473768,sm=1.0507009873554805;let fv=.3275911,dv=.254829592,mv=-.284496736,gv=1.421413741,yv=-1.453152027,bv=1.061405429;function Yc(...n){ct().getBool("IS_TEST")||console.warn(...n)}function r4(...n){ct().getBool("IS_TEST")||console.log(...n)}function ws(n,t){if(n.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${n.length}, imag: ${t.length}.`);let e=new Float32Array(n.length*2);for(let r=0;r<e.length;r+=2)e[r]=n[r/2],e[r+1]=t[r/2];return e}function CC(n){let t=new Float32Array(n.length/2),e=new Float32Array(n.length/2);for(let r=0;r<n.length;r+=2)t[r/2]=n[r],e[r/2]=n[r+1];return{real:t,imag:e}}function SC(n){let t=Math.ceil(n.length/4),e=new Float32Array(t),r=new Float32Array(t);for(let o=0;o<n.length;o+=4)e[Math.floor(o/4)]=n[o],r[Math.floor(o/4)]=n[o+1];return{real:e,imag:r}}function $C(n){let t=Math.floor(n.length/4),e=new Float32Array(t),r=new Float32Array(t);for(let o=2;o<n.length;o+=4)e[Math.floor(o/4)]=n[o],r[Math.floor(o/4)]=n[o+1];return{real:e,imag:r}}function xv(n,t){let e=n[t*2],r=n[t*2+1];return{real:e,imag:r}}function IC(n,t,e,r){n[r*2]=t,n[r*2+1]=e}function EC(n,t){let e=new Float32Array(n/2),r=new Float32Array(n/2);for(let o=0;o<Math.ceil(n/2);o++){let s=(t?2:-2)*Math.PI*(o/n);e[o]=Math.cos(s),r[o]=Math.sin(s)}return{real:e,imag:r}}function DC(n,t,e){let r=(e?2:-2)*Math.PI*(n/t),o=Math.cos(r),s=Math.sin(r);return{real:o,imag:s}}function o4(n,t,e){if(t==="complex64"){if(n.dtype==="complex64")return n.clone();let r=xe(n.shape),o=$t(n,"float32"),s=e.complex(o,r);return r.dispose(),o.dispose(),s}if(!gc(n.dtype,t))return X.makeTensorFromDataId(n.dataId,n.shape,t);if(n.dtype==="complex64"){let r=e.real(n),o=$t(r,t);return r.dispose(),o}if(t==="int32")return e.int(n);if(t==="bool"){let r=Et(0,n.dtype),o=e.notEqual(n,r);return r.dispose(),o}else throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${t}`)}function AC(n,t){return X.makeTensorFromDataId(n.dataId,t,n.dtype)}function wv(n,t,e){let r=(t-n)/(e-1),o=bc(e,"float32");o[0]=n;for(let s=1;s<o.length;s++)o[s]=o[s-1]+r;return vr(o,"float32")}var 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Tv(n,t,e){let r=new Array(n.rank).fill(0),o=n.shape.slice();return t.map(s=>{let c=[...o];c[e]=s;let l=ce(n,r,c);return r[e]+=s,l})}function kv(n,t){let e=new Array(n.rank);for(let o=0;o<e.length;o++)e[o]=n.shape[o]*t[o];let r=Se(e,n.dtype);for(let o=0;o<r.values.length;++o){let s=r.indexToLoc(o),c=new Array(n.rank);for(let p=0;p<c.length;p++)c[p]=s[p]%n.shape[p];let l=n.locToIndex(c);r.values[o]=n.values[l]}return r.toTensor()}function Nv(n,t,e,r,o){let s=t[t.length-1],[c,l]=[n.length/s,s],p=Ce(e,c*r),f=Ce("int32",c*r);for(let y=0;y<c;y++){let b=y*l,v=n.subarray(b,b+l),T=[];for(let I=0;I<v.length;I++)T.push({value:v[I],index:I});T.sort((I,P)=>P.value-I.value);let N=y*r,S=p.subarray(N,N+r),D=f.subarray(N,N+r);for(let I=0;I<r;I++)S[I]=T[I].value,D[I]=T[I].index}let m=t.slice();return m[m.length-1]=r,[un(p,m,e),un(f,m,"int32")]}var 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Input received: ${t}`);for(let r=0;r<t.length;r++){let o=t[r],s=e[r];if(s==null)continue;let c=o.rank;if(s.ndim!=null&&c!==s.ndim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${c}`);if(s.maxNDim!=null&&c>s.maxNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${c}`);if(s.minNDim!=null&&c<s.minNDim)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${c}.`);if(s.dtype!=null&&o.dtype!==s.dtype)throw new Y(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${o.dtype}.`);if(s.axes){let l=o.shape;for(let p in s.axes){let f=Number(p),m=s.axes[p],y=f>=0?l[f]:l[l.length+f];if(m!=null&&[m,null].indexOf(y)===-1)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected axis ${f} of input shape to have value ${m} but got shape ${l}.`)}}if(s.shape!=null)for(let l=0;l<s.shape.length;++l){let p=s.shape[l],f=o.shape[l];if(p!=null&&f!=null&&p!==f)throw new Y(`Input ${r} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${o.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let r=We(t),o=!0;for(let c of r)if(!(c instanceof wo)){o=!1;break}let s=!0;for(let c of r)if(c instanceof wo){s=!1;break}if(o===s)throw new Y("Arguments to apply() must be all SymbolicTensors or all Tensors");return xa(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let c=[];for(let l of We(t))c.push(l.shape);this.build(hr(c)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(t),s){let c=this.call(t,e),l=We(c),p=[];for(let f of l)r.indexOf(f)!==-1&&(f=f.clone()),p.push(f);if(c=hr(p),this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return c}else{let c=JG(t),l=this.computeOutputShape(c),p,f=ZG(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?c[0]:c),l!=null&&l.length>0&&Array.isArray(l[0])?p=l.map((m,y)=>new wo(f,m,this,We(t),e,this.name,y)):p=new wo(f,l,this,We(t),e,this.name),this.addInboundNode(t,p,null,null,c,l,e),this._refCount++,this.activityRegularizer!=null)throw new Ut("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return p}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape==null)return;if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((r,o)=>{r!=null&&t[o]!=null&&t[o]!==r&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new qo(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let r=JSON.stringify(e.outputShapes);t.indexOf(r)===-1&&t.push(r)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new qo(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Qr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return vm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return Hv(t?this.trainableWeights:this.weights)}setWeights(t){rt(()=>{let e=this.weights;if(e.length!==t.length)throw new Y(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let r=[],o=Hv(e);for(let s=0;s<o.length;++s){let c=o[s],l=e[s],p=t[s];if(!lt(c.shape,p.shape))throw new Y(`Layer weight shape ${c.shape} not compatible with provided weight shape ${p.shape}`);r.push([l,p])}jv(r)})}addWeight(t,e,r,o,s,c,l){if(this._addedWeightNames.indexOf(t)!==-1)throw new Y(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(o=je("zeros"));let p=o.apply(e,r),f=new xo(p,r,t,c,l);return p.dispose(),s!=null&&this.addLoss(()=>s.apply(f.read())),c==null&&(c=!0),c?this._trainableWeights.push(f):this._nonTrainableWeights.push(f),f}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){if(t==null||Array.isArray(t)&&t.length===0)return;t=We(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t)}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}addInboundNode(t,e,r,o,s,c,l=null){let p=We(t);e=We(e),r=We(r),o=We(o),s=wm(s),c=wm(c);let f=[],m=[],y=[];for(let b of p)f.push(b.sourceLayer),m.push(b.nodeIndex),y.push(b.tensorIndex);new Tm({outboundLayer:this,inboundLayers:f,nodeIndices:m,tensorIndices:y,inputTensors:p,outputTensors:e,inputMasks:r,outputMasks:o,inputShapes:s,outputShapes:c},l);for(let b=0;b<e.length;b++)e[b].sourceLayer=this,e[b].nodeIndex=this.inboundNodes.length-1,e[b].tensorIndex=b}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}}function JG(n){n=We(n);let t=[];for(let e of n)t.push(e.shape);return hr(t)}function ZG(n){return"float32"}function sS(n,t,e){if((t==null||e!=null&&e>0)&&(t=n.sourceLayer,e=n.nodeIndex),t.inboundNodes.length===0)return[n];{let r=t.inboundNodes[e];if(r.inboundLayers.length===0)return r.inputTensors;{let o=[];for(let s=0;s<r.inboundLayers.length;s++){let c=r.inputTensors[s],l=r.inboundLayers[s],p=r.nodeIndices[s],f=sS(c,l,p);for(let m of f)o.indexOf(m)===-1&&o.push(m)}return o}}}class tl extends ye{constructor(t){super({dtype:t.dtype,name:t.name!=null?t.name:xm("input").toString()});if(t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new Y("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new Y("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new Y("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=t.dtype||"float32";this.batchInputShape=e,this.dtype=r,this.inputSpec=[{shape:e}];let o=new wo(this.dtype,this.batchInputShape,this,[],{},this.name);o.nodeIndex=0,o.tensorIndex=0,new Tm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[o],outputTensors:[o],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new Y(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}}tl.className="InputLayer",vt(tl);function iS(n){if(n.batchShape==null&&n.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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Provided ${e} not understood: ${JSON.stringify(n)}`)}function _S(n,t){return NS(n,t,"classWeight")}function ust(n,t){return NS(n,t,"sampleWeight")}async function CS(n,t,e,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let o=rt(()=>{if(n.shape.length===1)return n.clone();if(n.shape.length===2)if(n.shape[1]>1){let l=1;return n.argMax(l)}else{if(n.shape[1]===1)return n.reshape([n.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${n.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${n.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());Xt(o);let c=[];return s.forEach(l=>{if(e[l]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(n.outputNames)})`);for(let p=0;p<s.length;p++)_(s[p].shape[0]===l,()=>`Batch size mismatch: input ${n.inputNames[p]} has ${s[p].shape[0]}; expected  ${l} based on input ${n.inputNames[0]}.`);for(let p=0;p<c.length;p++)_(c[p].shape[0]===l,()=>`Batch size mismatch: output ${n.outputNames[p]} has ${c[p].shape[0]}; expected  ${l} based on input ${n.inputNames[0]}.`);return{xs:s,ys:c}}function $S(n,t,e){if(e instanceof ot)return[e];if(Array.isArray(e))return _(e.length===t.length,()=>`Received an array of ${e.length} Tensors, but expected ${t.length} to match the ${n} keys ${t}.`),e;{let r=[];for(let o of t){if(e[o]==null)throw new Y(`The feature data generated by the dataset lacks the required ${n} key '${o}'.`);r.push(e[o])}return r}}function EU(n){if(n.length===3)throw new Ut("Validation with sample weights is not implemented yet.");return{xs:n[0],ys:n[1]}}async function DU(n,t,e){let r=e.batchesPerEpoch!=null;if(_(n.optimizer!=null,()=>"You must compile a model before training/testing. 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if(t.metrics!=null){s={};for(let c in t.metrics)s[c]=ba(t.metrics[c])}this.compile({loss:o,metrics:s,optimizer:r})}async save(t,e){if(typeof t=="string"){let f=ew(t);if(f.length===0)throw new Y(`Cannot find any save handlers for URL '${t}'`);if(f.length>1)throw new Y(`Found more than one (${f.length}) save handlers for URL '${t}'`);t=f[0]}if(t.save==null)throw new Y("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Qx(this.getNamedWeights(e)),o=!1,s=null,c=this.toJSON(s,o),l={modelTopology:c,format:WU,generatedBy:`TensorFlow.js tfjs-layers v${Dm}`,convertedBy:null},p=e==null?!1:e.includeOptimizer;if(p&&this.optimizer!=null){l.trainingConfig=this.getTrainingConfig();let f="optimizer",{data:m,specs:y}=await Qx(await this.optimizer.getWeights(),f);r.specs.push(...y),r.data=ud([r.data,m])}if(this.userDefinedMetadata!=null){let 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UU(n,t,e){if(e==null&&(e={}),n.load==null)throw new Y("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await n.load(),o=r.modelTopology;o.model_config!=null&&(o=o.model_config);let s=e.strict==null?!0:e.strict,c=r.weightData!=null&&r.weightSpecs!=null&&s,l=vo(Wp(o),t,c),p=r.trainingConfig;if(p!=null&&l.loadTrainingConfig(p),r.userDefinedMetadata!=null&&l.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new Y("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:f,optimizerWeights:m}=qU(r.weightData,r.weightSpecs);l.loadWeights(f,s),l.optimizer!=null&&m.length>0&&await l.optimizer.setWeights(m),Xt(f),Xt(m.map(y=>y.tensor))}return l}function qU(n,t){let e=ld(n,t),r={},o=[];return t.forEach(s=>{s.group==="optimizer"?o.push({name:s.name,tensor:e[s.name]}):r[s.name]=e[s.name]}),{modelWeights:r,optimizerWeights:o}}class Ta extends ks{constructor(t){super({inputs:[],outputs:[]});if(t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:xm("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){let e=t.inboundNodes[0].outputTensors[0].shape;if(e.some(r=>r<0))throw new Y(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof Ta||t instanceof ks,r;if(e){if(r=t,r.outputs.length!==1)throw new Y("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(t,e,r)}async evaluateDataset(t,e){if(!this.built)throw new Qr("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,e,r={}){if(!this.built)throw new Qr("The model needs to be compiled before being used.");return this.model.fit(t,e,r)}async fitDataset(t,e){if(!this.built)throw new Qr("The model needs to be compiled before being used.");return 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this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let r={};r.className=e.getClassName(),r.config=e.getConfig(),t.push(r)}return{name:this.name,layers:t}}}Ta.className="Sequential",vt(Ta);function HU(n){return new ks(n)}function jU(n){return new Ta(n)}function KU(n,t){return t==null&&(t={}),GU(n,t)}function PS(n){return iS(n)}function XU(n,t){Mr.registerCallbackConstructor(n,t)}class kr extends sa{getConfig(){return{}}}class OS extends kr{apply(t,e=1){return kG(t,e)}}OS.className="elu",vt(OS);class LS extends kr{apply(t){return zd(t)}}LS.className="selu",vt(LS);class MS extends kr{apply(t){return Uo(t)}}MS.className="relu",vt(MS);class BS extends kr{apply(t){return rt(()=>ua(6,Uo(t)))}}BS.className="relu6",vt(BS);class zS extends kr{apply(t){return t}}zS.className="linear",vt(zS);class WS extends kr{apply(t){return Wo(t)}}WS.className="sigmoid",vt(WS);class VS extends kr{apply(t){return _G(t)}}VS.className="hardSigmoid",vt(VS);class GS extends kr{apply(t){return 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Received: ${n.length} elements.`);for(let r=0;r<t;++r){let o=n[r];if(!yG(o))throw new Y(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(n)} including a non-integer number ${o}`)}return n}function ko(n,t,e,r,o=1){if(n==null)return n;let s=t+(t-1)*(o-1),c;return e==="same"?c=n:c=n-s+1,Math.floor((c+r-1)/r)}function Am(n,t,e,r){if(n==null)return null;if(r==="valid")n=n*t+bi([e-t,0]);else if(r==="same")n=n*t;else throw new Y(`Unsupport padding mode: ${r}.`);return n}function g0(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,1]):n))}function JS(n,t){return rt(()=>(nn(t),t==="channelsFirst"?Kt(n,[0,2,3,4,1]):n))}function ZS(n,t,e,r=1,o="valid",s,c=1){return rt(()=>{if(s==null&&(s=yo()),nn(s),n.shape.length!==3)throw new Y(`The input of a conv1dWithBias operation should be 3, but is ${n.shape.length} instead.`);if(t.shape.length!==3)throw new Y(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new Y(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(n=Kt(n,[0,2,1])),o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let l=Cd(n,t,r,o==="same"?"same":"valid","NWC",c);return e!=null&&(l=jo(l,e)),l})}function pst(n,t,e=1,r="valid",o,s=1){return rt(()=>(nn(o),ZS(n,t,null,e,r,o,s)))}function hst(n,t,e=[1,1],r="valid",o,s){return rt(()=>(nn(o),y0(n,t,null,e,r,o,s)))}function y0(n,t,e,r=[1,1],o="valid",s,c,l=null){return rt(()=>{if(s==null&&(s=yo()),nn(s),n.rank!==3&&n.rank!==4)throw new Y(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${n.rank}.`);if(t.rank!==3&&t.rank!==4)throw new Y(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${n.rank}.`);let p=g0(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return p=cv({x:p,filter:t,strides:r,pad:o==="same"?"same":"valid",dilations:c,dataFormat:"NHWC",bias:e,activation:l}),s==="channelsFirst"&&(p=Kt(p,[0,3,1,2])),p})}function fst(n,t,e=[1,1,1],r="valid",o,s){return rt(()=>(nn(o),QS(n,t,null,e,r,o,s)))}function QS(n,t,e,r=[1,1,1],o="valid",s,c){return rt(()=>{if(s==null&&(s=yo()),nn(s),n.rank!==4&&n.rank!==5)throw new Y(`conv3dWithBias expects input to be of rank 4 or 5, but received ${n.rank}.`);if(t.rank!==4&&t.rank!==5)throw new Y(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${n.rank}.`);let l=JS(n,s);if(o==="causal")throw new Ut("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return l=Fw(l,t,r,o==="same"?"same":"valid","NDHWC",c),e!=null&&(l=jo(l,e)),s==="channelsFirst"&&(l=Kt(l,[0,4,1,2,3])),l})}class Fm extends ye{constructor(t,e){super(e);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Fm.verifyArgs(e),this.rank=t,$n(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ut(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=rl(e.kernelSize,t,"kernelSize"),this.strides=rl(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,Lr(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,nn(this.dataFormat),this.activation=ki(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=je(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Tn(e.biasConstraint),this.biasRegularizer=Ke(e.biasRegularizer),this.activityRegularizer=Ke(e.activityRegularizer),this.dilationRate=rl(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new Y(`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 Y(`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 Y(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(Or("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!$v(t.kernelSize,"number",1,3))throw new Y(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ti(this.activation),useBias:this.useBias,biasInitializer:rn(this.biasInitializer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}class ol extends Fm{constructor(t,e){super(t,e);this.kernel=null,ol.verifyArgs(e),this.filters=e.filters,$n(this.filters,"filters"),this.kernelInitializer=je(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Tn(e.kernelConstraint),this.kernelRegularizer=Ke(e.kernelRegularizer)}build(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[e]}`);let r=t[e],o=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",o,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:{[e]:r}}],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r,o=this.bias==null?null:this.bias.read(),s=GC(this.activation.getClassName());if(s!=null&&this.rank===2)r=y0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)r=ZS(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=y0(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=QS(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ut("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(t){t=Re(t);let e=[],r=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<r.length;++s){let c=ko(r[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(c)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:rn(this.kernelInitializer),kernelRegularizer:Pe(this.kernelRegularizer),kernelConstraint:vn(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new Y(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}}class sl extends ol{constructor(t){super(2,t);sl.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!$v(t.kernelSize,"number",1,2))throw new Y(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}}sl.className="Conv2D",vt(sl);class qp extends ol{constructor(t){super(3,t);qp.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new Y(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}}qp.className="Conv3D",vt(qp);class b0 extends sl{constructor(t){super(t);if(this.inputSpec=[new In({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new Y(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Re(t),t.length!==4)throw new Y("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new Y("The channel dimension of the inputs should be defined. Found `None`.");let r=t[e],o=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new In({ndim:4,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{let r=Qt(t);if(r.shape.length!==4)throw new Y(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let o=r.shape,s=o[0],c,l;this.dataFormat==="channelsFirst"?(c=2,l=3):(c=1,l=2);let p=o[c],f=o[l],m=this.kernelSize[0],y=this.kernelSize[1],b=this.strides[0],v=this.strides[1],T=Am(p,b,m,this.padding),N=Am(f,v,y,this.padding),S=[s,T,N,this.filters];this.dataFormat!=="channelsLast"&&(r=Kt(r,[0,2,3,1]));let D=Sd(r,this.kernel.read(),S,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(D=Kt(D,[0,3,1,2])),this.bias!=null&&(D=jo(D,this.bias.read(),this.dataFormat)),this.activation!=null&&(D=this.activation.apply(D)),D})}computeOutputShape(t){t=Re(t);let e=t.slice(),r,o,s;this.dataFormat==="channelsFirst"?(r=1,o=2,s=3):(r=3,o=1,s=2);let c=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],f=this.strides[1];return e[r]=this.filters,e[o]=Am(e[o],p,c,this.padding),e[s]=Am(e[s],f,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}}b0.className="Conv2DTranspose",vt(b0);class t$ extends ol{constructor(t,e){super(t,e);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new Y("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new Y("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new Y(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ke(e.depthwiseRegularizer),this.depthwiseConstraint=Tn(e.depthwiseConstraint),this.pointwiseInitializer=je(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ke(e.pointwiseRegularizer),this.pointwiseConstraint=Tn(e.pointwiseConstraint)}build(t){if(t=Re(t),t.length<this.rank+2)throw new Y(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let r=t[e],o=this.kernelSize.concat([r,this.depthMultiplier]),s=[];for(let l=0;l<this.rank;++l)s.push(1);s.push(r*this.depthMultiplier,this.filters);let c=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,c,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,c,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,c,this.biasConstraint):this.bias=null,this.inputSpec=[new In({ndim:this.rank+2,axes:{[e]:r}})],this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r;if(this.rank===1)throw new Ut("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Kt(t,[0,2,3,1])),r=Jw(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=jo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=Kt(r,[0,3,1,2])),r})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.pointwiseInitializer=rn(this.pointwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.pointwiseRegularizer=Pe(this.pointwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseConstraint),t.pointwiseConstraint=vn(this.pointwiseConstraint),t}}t$.className="SeparableConv";class x0 extends t${constructor(t){super(2,t)}}x0.className="SeparableConv2D",vt(x0);class Hp extends ol{constructor(t){super(1,t);Hp.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!$v(t.kernelSize,"number",1,1))throw new Y(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}}Hp.className="Conv1D",vt(Hp);class w0 extends ye{constructor(t){super(t);typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return rt(()=>{if(t=Qt(t),this.dataFormat==="channelsLast"){let r=lm(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return lm(r,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=lm(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return lm(r,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}w0.className="Cropping2D",vt(w0);class v0 extends ye{constructor(t){super(t);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],r=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,r]}else{let e=t[1]==null?null:this.size[0]*t[1],r=t[2]==null?null:this.size[1]*t[2];return[t[0],e,r,t[3]]}}call(t,e){return rt(()=>{let r=Qt(t),o=r.shape;if(this.dataFormat==="channelsFirst"){r=Kt(r,[0,2,3,1]);let s=this.size[0]*o[2],c=this.size[1]*o[3],l=r.resizeNearestNeighbor([s,c]);return Kt(l,[0,3,1,2])}else{let s=this.size[0]*o[1],c=this.size[1]*o[2];return r.resizeNearestNeighbor([s,c])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}v0.className="UpSampling2D",vt(v0);function ZU(n,t,e=[1,1],r="valid",o,s){return rt(()=>{o==null&&(o=yo()),nn(o);let c=g0(n,o);if(n.rank!==4)throw new Y(`Input for depthwiseConv2d is required to be 4-D, but is instead ${n.rank}-D`);if(t.rank!==4)throw new Y(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return c=ca(c,t,e,r==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}class T0 extends Fm{constructor(t){super(2,t);this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=je(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Tn(t.depthwiseConstraint),this.depthwiseRegularizer=Ke(t.depthwiseRegularizer)}build(t){if(t=Re(t),t.length<4)throw new Y(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new Y(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let r=t[e],o=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return rt(()=>{t=Qt(t);let r=ZU(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=jo(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=ko(e,this.kernelSize[0],this.padding,this.strides[0]),c=ko(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,c]:[t[0],s,c,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=rn(this.depthwiseInitializer),t.depthwiseRegularizer=Pe(this.depthwiseRegularizer),t.depthwiseConstraint=vn(this.depthwiseRegularizer),t}}T0.className="DepthwiseConv2D",vt(T0);function e$(n,t,e,r){if(Array.isArray(n)){if(t!=null||e!=null)throw new Y("When inputs is an array, neither initialState or constants should be provided");r!=null&&(e=n.slice(n.length-r,n.length),n=n.slice(0,n.length-r)),n.length>1&&(t=n.slice(1,n.length)),n=n[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:n,initialState:t,constants:e}}function n$(n,t,e,r=!1,o,s,c=!1,l=!1){return rt(()=>{let p=t.shape.length;if(p<3)throw new Y(`Input should be at least 3D, but is ${p}D.`);let f=[1,0].concat(bo(2,p));if(t=Kt(t,f),s!=null)throw new Ut("The rnn() functoin of the deeplearn.js backend does not support constants yet.");c&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===p-1&&(o=lr(o,-1)),o=Kt(o,f)),r&&(t=Pr(t,0),o!=null&&(o=Pr(o,0)));let m=[],y,b=e,v=t.shape[0],T=go(t),N;o!=null&&(N=go(o));for(let D=0;D<v;++D){let I=T[D],P=rt(()=>n(I,b));if(o==null)y=P[0],b=P[1];else{let E=rt(()=>{let L=N[D],B=qn(L).sub(L),q=P[0].mul(L).add(b[0].mul(B)),H=b.map((Z,J)=>P[1][J].mul(L).add(Z.mul(B)));return{output:q,newStates:H}});y=E.output,b=E.newStates}l&&m.push(y)}let S;if(l){let D=1;S=pr(m,D)}return[y,S,b]})}class No extends ye{constructor(t){super(t);let e;if(t.cell==null)throw new Y("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new Om({cells:t.cell}):e=t.cell,e.stateSize==null)throw new Y("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new In({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return bo(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){qv(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let r=e[0],o;if(this.returnSequences?o=[t[0],t[1],r]:o=[t[0],r],this.returnState){let s=[];for(let c of e)s.push([t[0],c]);return[o].concat(s)}else return o}computeMask(t,e){return rt(()=>{Array.isArray(e)&&(e=e[0]);let r=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[r].concat(o)}else return r})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let r=0;r<t;++r)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){let e=null;if(this.numConstants!=null)throw new Ut("Constants support is not implemented in RNN yet.");qv(t)&&(t=t[0]),t=t;let r=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new In({shape:[r,null,...o]});let s=[t[0]].concat(t.slice(2));if(e!=null)throw new Ut("Constants support is not implemented in RNN yet.");this.cell.build(s);let c;if(Array.isArray(this.cell.stateSize)?c=this.cell.stateSize:c=[this.cell.stateSize],this.stateSpec!=null){if(!lt(this.stateSpec.map(l=>l.shape[l.shape.length-1]),c))throw new Y(`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=c.map(l=>new In({shape:[null,l]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new qo("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new Y("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(o=>xe([r,o])):this.states_=[xe([r,this.cell.stateSize])];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>xe([r,o])):this.states_[0]=xe([r,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],c=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,l=[r,c];if(!lt(s.shape,l))throw new Y(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Sn(o.clone()))})}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=e$(t,r,o,this.numConstants);t=s.inputs,r=s.initialState,o=s.constants;let c=[],l=[];if(r!=null){e.initialState=r,c=c.concat(r),this.stateSpec=[];for(let f of r)this.stateSpec.push(new In({shape:f.shape}));l=l.concat(this.stateSpec)}o!=null&&(e.constants=o,c=c.concat(o),this.numConstants=o.length);let p=c[0]instanceof wo;if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=Qt(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let c=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==c)throw new Y(`RNN Layer has ${c} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let l={training:o},p=(T,N)=>{let S=this.cell.call([T].concat(N),l);return[S[0],S.slice(1)]},f=n$(p,t,s,this.goBackwards,r,null,this.unroll,this.returnSequences),m=f[0],y=f[1],b=f[2];this.stateful&&this.resetStates(b,o);let v=this.returnSequences?y:m;return this.returnState?[v].concat(b):v})}getInitialState(t){return rt(()=>{let e=xe(t.shape);return e=zt(e,[1,2]),e=Lp(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Ov(e,[1,r]):e):this.cell.stateSize>1?[Ov(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===No.className&&(e.cell={className:this.cell.getClassName(),config:r}),Object.assign({},r,t,e)}static fromConfig(t,e,r={}){let o=e.cell,s=vo(o,r);return new t(Object.assign(e,{cell:s}))}}No.className="RNN",vt(No);class il extends ye{}class Rm extends il{constructor(t){super(t);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,$n(this.units,"units"),this.activation=ki(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Qc([1,bi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Qc([1,bi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t),this.kernel=this.addWeight("kernel",[t[t.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(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let r=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(t),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(r),rate:this.recurrentDropout,training:o}));let s,c=this.dropoutMask,l=this.recurrentDropoutMask;c!=null?s=Ho(nt(t,c),this.kernel.read()):s=Ho(t,this.kernel.read()),this.bias!=null&&(s=jo(s,this.bias.read())),l!=null&&(r=nt(r,l));let p=Tt(s,Ho(r,this.recurrentKernel.read()));return this.activation!=null&&(p=this.activation.apply(p)),[p,p]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ti(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},t,e)}}Rm.className="SimpleRNNCell",vt(Rm);class k0 extends No{constructor(t){t.cell=new Rm(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}}k0.className="SimpleRNN",vt(k0);class Pm extends il{constructor(t){super(t);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",t.resetAfter)throw new Y("GRUCell does not support reset_after parameter set to true.");this.units=t.units,$n(this.units,"units"),this.activation=ki(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ki(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Qc([1,bi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Qc([1,bi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Re(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,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(t,e){return rt(()=>{if(t=t,t.length!==2)throw new Y(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(t),rate:this.dropout,training:r,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:3}));let s=this.dropoutMask,c=this.recurrentDropoutMask,l,p,f;0<this.dropout&&this.dropout<1&&(t=nt(t,s[0]));let m=Ho(t,this.kernel.read());this.useBias&&(m=jo(m,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,c[0]));let y=this.recurrentKernel.read(),[b,v]=Tr(y,[2*this.units,this.units],y.rank-1),T=Ho(o,b),[N,S,D]=Tr(m,3,m.rank-1),[I,P]=Tr(T,2,T.rank-1);l=this.recurrentActivation.apply(Tt(N,I)),p=this.recurrentActivation.apply(Tt(S,P));let E=Ho(nt(p,o),v);f=this.activation.apply(Tt(D,E));let L=Tt(nt(l,o),nt(Tt(1,tn(l)),f));return[L,L]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ti(this.activation),recurrentActivation:Ti(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},t,e)}}Pm.className="GRUCell",vt(Pm);class N0 extends No{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Pm(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}N0.className="GRU",vt(N0);class jp extends il{constructor(t){super(t);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=t.units,$n(this.units,"units"),this.activation=ki(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=ki(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=je(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=Ke(t.kernelRegularizer),this.recurrentRegularizer=Ke(t.recurrentRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.kernelConstraint=Tn(t.kernelConstraint),this.recurrentConstraint=Tn(t.recurrentConstraint),this.biasConstraint=Tn(t.biasConstraint),this.dropout=Qc([1,bi([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Qc([1,bi([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Re(t);let r=t[t.length-1];this.kernel=this.addWeight("kernel",[r,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 o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,c=this.units;o=new(e=class extends eo{apply(p,f){let m=s.apply([c]),y=new pm().apply([c]),b=s.apply([c*2]);return ZC(ZC(m,y),b)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new Y(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(t),rate:this.dropout,training:r,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(o),rate:this.recurrentDropout,training:r,count:4}));let c=this.dropoutMask,l=this.recurrentDropoutMask,p,f,m,y;0<this.dropout&&this.dropout<1&&(t=nt(t,c[0]));let b=Ho(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=nt(o,l[0])),b=Tt(b,Ho(o,this.recurrentKernel.read())),this.useBias&&(b=jo(b,this.bias.read()));let[v,T,N,S]=Tr(b,4,b.rank-1);p=this.recurrentActivation.apply(v),f=this.recurrentActivation.apply(T),m=Tt(nt(f,s),nt(p,this.activation.apply(N))),y=this.recurrentActivation.apply(S);let D=nt(y,this.activation.apply(m));return[D,D,m]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:Ti(this.activation),recurrentActivation:Ti(this.recurrentActivation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),recurrentInitializer:rn(this.recurrentInitializer),biasInitializer:rn(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:Pe(this.kernelRegularizer),recurrentRegularizer:Pe(this.recurrentRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),recurrentConstraint:vn(this.recurrentConstraint),biasConstraint:vn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},t,e)}}jp.className="LSTMCell",vt(jp);class _0 extends No{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new jp(t),super(t)}call(t,e){return rt(()=>{this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}}_0.className="LSTM",vt(_0);class Om extends il{constructor(t){super(t);this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return rt(()=>{t=t;let r=t.slice(1),o=[];for(let l of this.cells.slice().reverse())Array.isArray(l.stateSize)?o.push(r.splice(0,l.stateSize.length)):o.push(r.splice(0,1));o.reverse();let s=[],c;for(let l=0;l<this.cells.length;++l){let p=this.cells[l];r=o[l],l===0?c=[t[0]].concat(r):c=[c[0]].concat(r),c=p.call(c,e),s.push(c.slice(1))}r=[];for(let l of s.slice().reverse())r.push(...l);return[c[0]].concat(r)})}build(t){qv(t)&&(t=t[0]),t=t;let e;this.cells.forEach((r,o)=>{xa(`RNNCell_${o}`,()=>{r.build(t),Array.isArray(r.stateSize)?e=r.stateSize[0]:e=r.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),r=this.cells.map(e),o={cells:r};return Object.assign({},t,o)}static fromConfig(t,e,r={}){let o=[];for(let s of e.cells)o.push(vo(s,r));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let r of this.cells)e.push(...r.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return Hv(t)}setWeights(t){let e=[];for(let r of this.cells){let o=r.weights.length,s=t.splice(o);for(let c=0;c<r.weights.length;++c)e.push([r.weights[c],s[c]])}jv(e)}}Om.className="StackedRNNCells",vt(Om);function Ni(n){let{ones:t,rate:e,training:r=!1,count:o=1}=n,s=()=>tS(t(),e),c=()=>Bp(s,t,r);if(!o||o<=1)return Sn(c().clone());let l=Array(o).fill(void 0).map(c);return l.map(p=>Sn(p.clone()))}var QU=function(n,t){var e={};for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&t.indexOf(r)<0&&(e[r]=n[r]);if(n!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,r=Object.getOwnPropertySymbols(n);o<r.length;o++)t.indexOf(r[o])<0&&Object.prototype.propertyIsEnumerable.call(n,r[o])&&(e[r[o]]=n[r[o]]);return e};class dst extends il{}class r$ extends No{constructor(t){if(t.unroll)throw new Ut("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new Ut("It is not possible at the moment to stack convolutional cells.");super(t);this.inputSpec=[new In({ndim:5})]}call(t,e){return rt(()=>{if(this.cell.dropoutMask!=null&&(Xt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Xt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new Y("ConvRNN2D cell does not support constants");let r=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:r,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return rt(()=>{let{stateSize:e}=this.cell,r=t.shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=xe(s);return Array.isArray(e)?Array(e.length).fill(c):[c]})}resetStates(t,e=!1){rt(()=>{if(!this.stateful)throw new qo("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,o=this.computeSingleOutputShape(r),s=[o[0],...o.slice(2)],c=r[0];if(c==null)throw new Y("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(()=>xe(s)):this.states_=[xe(s)];else if(t==null)Xt(this.states_),this.keptStates!=null&&(Xt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>xe(s)):this.states_[0]=xe(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new Y(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Xt(this.states_);for(let l=0;l<this.states_.length;++l){let p=t[l],f=s;if(!lt(p.shape,f))throw new Y(`State ${l} is incompatible with layer ${this.name}: expected shape=${f}, received shape=${p.shape}`);this.states_[l]=p}}this.states_=this.states_.map(l=>Sn(l.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:r,kernelSize:o,padding:s,strides:c,dilationRate:l}=this.cell,p=e==="channelsFirst",f=t[p?3:2],m=t[p?4:3],y=ko(f,o[0],s,c[0],l[0]),b=ko(m,o[1],s,c[1],l[1]),v=[...t.slice(0,2),...p?[r,y,b]:[y,b,r]];return v}}r$.className="ConvRNN2D";class Lm extends jp{constructor(t){let{filters:e,kernelSize:r,strides:o,padding:s,dataFormat:c,dilationRate:l}=t;super(Object.assign({},t,{units:e}));this.filters=e,$n(this.filters,"filters"),this.kernelSize=rl(r,2,"kernelSize"),this.kernelSize.forEach(p=>$n(p,"kernelSize")),this.strides=rl(o||1,2,"strides"),this.strides.forEach(p=>$n(p,"strides")),this.padding=s||"valid",Lr(this.padding),this.dataFormat=c||"channelsLast",nn(this.dataFormat),this.dilationRate=rl(l||1,2,"dilationRate"),this.dilationRate.forEach(p=>$n(p,"dilationRate"))}build(t){var e;t=Re(t);let r=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[r]==null)throw new Y(`The channel dimension of the input should be defined. Found ${t[r]}`);let o=t[r],s=4,c=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",c,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let l=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",l,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let p;if(this.unitForgetBias){let f=this.biasInitializer,m=this.filters;p=new(e=class extends eo{apply(b,v){let T=f.apply([m]),N=fo([m]),S=f.apply([m*2]);return Pv([T,N,S])}},e.className="CustomInit",e)}else p=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,p,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return rt(()=>{if(t.length!==3)throw new Y(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let r=e.training||!1,o=t[0],s=t[1],c=t[2],l=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ni({ones:()=>qn(o),rate:this.dropout,training:r,count:l}));let p=this.dropoutMask,f=(kt,Nt,At)=>!Nt||!Nt[At]?kt:nt(Nt[At],kt),m=f(o,p,0),y=f(o,p,1),b=f(o,p,2),v=f(o,p,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ni({ones:()=>qn(s),rate:this.recurrentDropout,training:r,count:l}));let T=this.recurrentDropoutMask,N=f(s,T,0),S=f(s,T,1),D=f(s,T,2),I=f(s,T,3),P=3,[E,L,B,q]=Tr(this.kernel.read(),l,P),[H,Z,J,it]=this.useBias?Tr(this.bias.read(),l):[null,null,null,null];m=this.inputConv(m,E,H,this.padding),y=this.inputConv(y,L,Z,this.padding),b=this.inputConv(b,B,J,this.padding),v=this.inputConv(v,q,it,this.padding);let[pt,ht,dt,ft]=Tr(this.recurrentKernel.read(),l,P);N=this.recurrentConv(N,pt),S=this.recurrentConv(S,ht),D=this.recurrentConv(D,dt),I=this.recurrentConv(I,ft);let ut=this.recurrentActivation.apply(Tt(m,N)),bt=this.recurrentActivation.apply(Tt(y,S)),yt=Tt(nt(bt,c),nt(ut,this.activation.apply(Tt(b,D)))),xt=nt(this.recurrentActivation.apply(Tt(v,I)),this.activation.apply(yt));return[xt,xt,yt]})}getConfig(){let t=super.getConfig(),{units:e}=t,r=QU(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},r,o)}inputConv(t,e,r,o){let s=gs(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?jo(s,r,this.dataFormat):s}recurrentConv(t,e){let r=1;return gs(t,e,r,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}}Lm.className="ConvLSTM2DCell",vt(Lm);class C0 extends r${constructor(t){let e=new Lm(t);super(Object.assign({},t,{cell:e}))}static fromConfig(t,e){return new t(e)}}C0.className="ConvLSTM2D",vt(C0);class Mm extends ye{constructor(t){super(t);this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,r=[];for(let o=0;o<this.noiseShape.length;++o)r.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return r}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(r),c=Bp(()=>tS(r,this.rate,s,this.seed),()=>r,o);return c}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}}Mm.className="Dropout",vt(Mm);class S0 extends Mm{constructor(t){super(t);this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}}S0.className="SpatialDropout1D",vt(S0);class $0 extends ye{constructor(t){super(t);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,$n(this.units,"units"),this.activation=ki(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=je(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=je(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Tn(t.kernelConstraint),this.biasConstraint=Tn(t.biasConstraint),this.kernelRegularizer=Ke(t.kernelRegularizer),this.biasRegularizer=Ke(t.biasRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Re(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,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]:e}}],this.built=!0}computeOutputShape(t){t=Re(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=GC(this.activation.getClassName()),s;return o!=null?s=Ho(r,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Ho(r,this.kernel.read()),this.bias!=null&&(s=jo(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:Ti(this.activation),useBias:this.useBias,kernelInitializer:rn(this.kernelInitializer),biasInitializer:rn(this.biasInitializer),kernelRegularizer:Pe(this.kernelRegularizer),biasRegularizer:Pe(this.biasRegularizer),activityRegularizer:Pe(this.activityRegularizer),kernelConstraint:vn(this.kernelConstraint),biasConstraint:vn(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}}$0.className="Dense",vt($0);class I0 extends ye{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Re(t);for(let e of t.slice(1))if(e==null)throw new Y(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],yi(t,1)]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.dataFormat==="channelsFirst"&&r.rank>1){let o=[0];for(let s=2;s<r.rank;++s)o.push(s);o.push(1),r=r.transpose(o)}return TG(r)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}}I0.className="Flatten",vt(I0);class E0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.activation=ki(t.activation)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);return this.activation.apply(r)})}getConfig(){let t={activation:Ti(this.activation)},e=super.getConfig();return Object.assign(t,e),t}}E0.className="Activation",vt(E0);class D0 extends ye{constructor(t){super(t);this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return rt(()=>(t=Qt(t),wG(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}}D0.className="RepeatVector",vt(D0);class A0 extends ye{constructor(t){super(t);this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let r="Total size of new array must be unchanged.",o=e.slice(),s=1,c=null;for(let p=0;p<o.length;++p){let f=o[p];if(this.isUnknown(f))if(c===null)c=p;else throw new Y("Can only specifiy one unknown dimension.");else s*=f}let l=yi(t);if(c!==null){if(s===0||l%s!==0)throw new Y(r);o[c]=l/s}else if(l!==s)throw new Y(r);return o}computeOutputShape(t){let e=!1;for(let r=0;r<t.length;++r)if(this.isUnknown(t[r])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=r.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return r.reshape(s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}}A0.className="Reshape",vt(A0);class F0 extends ye{constructor(t){super(t);if(t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=bo(1,t.dims.length+1);if(!lt(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new In({ndim:this.dims.length+1})]}computeOutputShape(t){t=Re(t);let e=t.slice();return this.dims.forEach((r,o)=>{e[o+1]=t[r]}),e}call(t,e){return Kt(Qt(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}}F0.className="Permute",vt(F0);class R0 extends ye{constructor(t){super(t==null?{}:t);this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let r=Qt(t),o=-1;return op(hi(r,this.maskValue),o)}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=-1,s=!0,c=op(hi(r,this.maskValue),o,s),l=r.mul(c.asType(r.dtype));return l})}}R0.className="Masking",vt(R0);class P0 extends ye{constructor(t){super(t);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(We(t.inputLength))}this.inputDim=t.inputDim,$n(this.inputDim,"inputDim"),this.outputDim=t.outputDim,$n(this.outputDim,"outputDim"),this.embeddingsInitializer=je(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ke(t.embeddingsRegularizer),this.activityRegularizer=Ke(t.activityRegularizer),this.embeddingsConstraint=Tn(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return rt(()=>this.maskZero?(t=Qt(t),hi(t,re(t))):null)}computeOutputShape(t){if(t=Re(t),this.inputLength==null)return[...t,this.outputDim];let e=We(this.inputLength);if(e.length!==t.length-1)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let r=0;for(let o=0;o<e.length;++o){let s=e[o],c=t[o+1];if(s!=null&&c!=null&&s!==c)throw new Y(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[r]=c),r++}}return[t[0],...e,this.outputDim]}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);r.dtype!=="int32"&&(r=Op(r,"int32"));let o=QC(this.embeddings.read(),r.as1D());return o.reshape(Re(this.computeOutputShape(r.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:rn(this.embeddingsInitializer),embeddingsRegularizer:Pe(this.embeddingsRegularizer),activityRegularizer:Pe(this.activityRegularizer),embeddingsConstraint:vn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}}P0.className="Embedding",vt(P0);class ka extends ye{constructor(t){super(t||{});this.supportsMasking=!0}mergeFunction(t){throw new Ut}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let r=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],c=e[o];if(s==null||c==null||s<0||c<0)r.push(null);else if(s===1)r.push(c);else if(c===1)r.push(s);else{if(s!==c)throw new Y("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));r.push(s)}}return r}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Re(t)]),t=t,t.length<2)throw new Y(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let s of t)s!=null&&s[0]!==null&&e.push(s[0]);if(e=gi(e),e.length>1)throw new Y(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let r=t[0]==null?null:t[0].slice(1);for(let s=1;s<t.length;++s){let c=t[s]==null?null:t[s].slice(1);r=this.computeElementwiseOpOutputShape(r,c)}let o=t.map(s=>s.length);t.indexOf(null)===-1&&gi(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return rt(()=>{if(t=t,this.reshapeRequired){let r=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=bi(o);for(let c of t){let l=c.rank;for(let p=0;p<s-l;++p)c=Lp(c,1);r.push(c)}return this.mergeFunction(r)}else{let s=!1;for(let p of t){let f=p.rank;if(f==null){let m=p.shape,y=m[0],b=m.slice(1).concat([y]),v=p.reshape([y].concat(yi(m.slice(1))));v=Kt(v,[1,0]),v=v.reshape(b),r.push(v),s=!0}else if(f>1){let m=bo(1,f).concat([0]);r.push(Kt(p,m)),s=!0}else r.push(p)}let c=this.mergeFunction(r),l=c.rank;if(s){if(l==null){let p=c.shape,f=p.length,m=p[f-1],y=[m].concat(p.slice(0,p.length-1));c=Kt(c.reshape([-1,m]),[1,0]).reshape(y)}else if(l>1){let p=[l-1].concat(bo(0,l-1));c=Kt(c,p)}}return c}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o<t.length;++o){let s=t[o]==null?null:t[o].slice(1);e=this.computeElementwiseOpOutputShape(e,s)}let r=[];for(let o of t)o!=null&&o[0]!==null&&r.push(o[0]);return r=gi(r),r.length===1?e=r.concat(e):e=[null].concat(e),e}computeMask(t,e){return rt(()=>{if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an Array");if(!Array.isArray(t))throw new Y("`inputs` should be an Array");if(e.length!==t.length)throw new Y(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:lr(o,0));let r=e[0];for(let o=1;o<e.length-1;++o)r=Zr(r,e[o]);return r})}}class Kp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return e})}}Kp.className="Add",vt(Kp);function mst(n){if(Array.isArray(n)){let t=new Kp({});return t.apply(n)}else return new Kp(n)}class Xp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=nt(e,t[r]);return e})}}Xp.className="Multiply",vt(Xp);function gst(n){if(Array.isArray(n)){let t=new Xp({});return t.apply(n)}else return new Xp(n)}class Yp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0].clone();for(let r=1;r<t.length;++r)e=Tt(e,t[r]);return nt(1/t.length,e)})}}Yp.className="Average",vt(Yp);function yst(n){if(Array.isArray(n)){let t=new Yp({});return t.apply(n)}else return new Yp(n)}class Jp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=Jr(e,t[r]);return e})}}Jp.className="Maximum",vt(Jp);function bst(n){if(Array.isArray(n)){let t=new Jp({});return t.apply(n)}else return new Jp(n)}class Zp extends ka{constructor(t){super(t)}mergeFunction(t){return rt(()=>{let e=t[0];for(let r=1;r<t.length;++r)e=ua(e,t[r]);return e})}}Zp.className="Minimum",vt(Zp);function xst(n){if(Array.isArray(n)){let t=new Zp({});return t.apply(n)}else return new Zp(n)}class Qp extends ka{constructor(t){super(t);this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new Y("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let o of t)if(o!=null){e=!1;break}if(e)return;let r=[];for(let o=0;o<t.length;++o){let s=t[o].slice();s.splice(this.axis,1);let c=!1;for(let l of r)if(lt(l,s)){c=!0;break}c||r.push(s)}if(r.length>1)throw new Y("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return rt(()=>Pv(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new Y("A `Concatenate` layer should be called on a list of inputs.");let e=t,r=e[0].slice(),o=this.axis<0?r.length+this.axis:this.axis;for(let s of e.slice(1)){if(r[o]==null||s[o]==null){r[o]=null;break}r[o]+=s[o]}return r}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new Y("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new Y("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new Y(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return rt(()=>{let r=!0;if(e.forEach(c=>{if(c!=null){r=!1;return}}),r)return null;let o=[];for(let c=0;c<t.length;++c)e[c]==null?o.push(qn(t[c]).asType("bool")):e[c].rank<t[c].rank?o.push(lr(e[c],-1)):o.push(e[c]);let s=Qe(o,this.axis);return vd(s,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}}Qp.className="Concatenate",vt(Qp);function wst(n){if(Array.isArray(n)){let t=new Qp({});return t.apply(n)}else return new Qp(n)}function th(n,t){for(;n<0;)n+=t;return n}function tq(n,t,e){if(n.shape.length>3||t.shape.length>3)throw new Ut("batchDot is not implemented for tensors of 4D or higher rank yet");if(_(n.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${n.shape.length}`),_(n.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),n.dtype==="complex64"||t.dtype==="complex64")throw new Ut("batchDot is not implemented for complex64-type Tensors yet.");let r=n.shape.length,o=t.shape.length;e==null&&(e=[r-1,o-2]);let s=e;return rt(()=>{let c;if(r>o){c=r-o;let p=[];for(let f=0;f<c;++f)p.push(1);t=t.reshape(t.shape.concat(p))}else if(o>r){c=o-r;let p=[];for(let f=0;f<c;++f)p.push(1);n=n.reshape(n.shape.concat(p))}else c=0;let l;if(n.shape.length===2&&t.shape.length===2)s[0]===s[1]?l=n.mul(t).sum(s[0]):l=n.transpose([1,0]).mul(t).sum(s[1]);else{let p=s[0]!==n.shape.length-1,f=s[1]===t.shape.length-1;l=n.matMul(t,p,f)}if(c>0){let p;r>o?p=r+o-3:p=r-1;let f=[];for(let m=p;m<p+c;++m)f.push(m);l=l.squeeze(f)}return l.shape.length===1&&(l=l.expandDims(1)),l})}class O0 extends ka{constructor(t){super(t);this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],r=t[1];if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);if(e[o[0]]!==r[o[1]])throw new Y(`Dimension incompatibility: ${e[o[0]]} !== ${r[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new Y(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],r=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,c)=>th(s,t[c].shape.length)):o=[th(this.axes,e.shape.length),th(this.axes,r.shape.length)],this.normalize&&(e=km(e,o[0]),r=km(r,o[1])),tq(e,r,o)}interpretAxes(t,e){let r;return Array.isArray(this.axes)?r=this.axes:r=[th(this.axes,t.length),th(this.axes,e.length)],r}computeOutputShape(t){_(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),r=t[1].slice();if(e.length>3||r.length>3)throw new Ut("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,r);e.splice(o[0],1),r.splice(o[1],1),r.splice(0,1);let s=e.concat(r);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}}O0.className="Dot",vt(O0);class L0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t),o=()=>um(r.shape,0,this.stddev).add(r),s=Bp(o,()=>r,e.training||!1);return s})}}L0.className="GaussianNoise",vt(L0);class M0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{this.invokeCallHook(t,e);let r=Qt(t);if(this.rate>0&&this.rate<1){let o=()=>{let s=Math.sqrt(this.rate/(1-this.rate));return r.mul(um(r.shape,1,s))};return Bp(o,()=>r,e.training||!1)}return r})}}M0.className="GaussianDropout",vt(M0);class B0 extends ye{constructor(t){super(t);this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||Qt(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return rt(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(t),o=()=>{let s=Qt(t),c=1.6732632423543772,l=1.0507009873554805,p=-c*l,f=ys(fa(r),this.rate);f=Op(f,"float32");let m=((1-this.rate)*(1+this.rate*p**2))**-.5,y=-m*p*this.rate,b=s.mul(f).add(f.add(-1).mul(p));return b.mul(m).add(y)};return Bp(o,()=>Qt(t),e.training||!1)}return t})}}B0.className="AlphaDropout",vt(B0);function eh(n,t,e,r,o,s=.001){let c;if(n.rank===2)c=$_(n,t,e,r,o,s);else if(n.rank===3)c=I_(n,t,e,r,o,s);else if(n.rank===4)c=E_(n,t,e,r,o,s);else throw new Ut(`batchNormalization is not implemented for array of rank ${n.rank} yet`);return c}function eq(n,t,e,r,o=.001){return rt(()=>{let s=Ld(n,r),c=s.mean,l=s.variance,p=eh(n,c,l,e,t,o);return[p,c,l]})}function nq(n,t,e,r,o=.001){return rt(()=>{let s=Ld(n,r),c=s.mean,l=s.variance,p=[];for(let T of bo(0,n.rank))r.indexOf(T)!==-1?p.push(1):p.push(n.shape[T]);let f=c.reshape(p),m=l.reshape(p),y=t==null?null:t.reshape(p),b=e==null?null:e.reshape(p),v=eh(n,f,m,b,y,o);return[v,c,l]})}function rq(n,t,e,r,o=.001){return lt(r.slice().sort(),bo(0,n.rank-1))?eq(n,t,e,r,o):nq(n,t,e,r,o)}class z0 extends ye{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=je(t.betaInitializer||"zeros"),this.gammaInitializer=je(t.gammaInitializer||"ones"),this.movingMeanInitializer=je(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=je(t.movingVarianceInitializer||"ones"),this.betaConstraint=Tn(t.betaConstraint),this.gammaConstraint=Tn(t.gammaConstraint),this.betaRegularizer=Ke(t.betaRegularizer),this.gammaRegularizer=Ke(t.gammaRegularizer)}build(t){t=Re(t);let e=this.axis>=0?this.axis:this.axis+t.length,r=t[e];if(r==null)throw new Y(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new In({ndim:t.length,axes:{[e]:r}})];let o=[r];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return rt(()=>{let r=e.training==null?!1:e.training,o=Qt(t),s=o.shape,c=s.length,l=bo(0,c),p=this.axis>=0?this.axis:this.axis+c;l.splice(p,1);let f=ya(1,c);f[p]=s[p];let m=l.slice();m.sort();let y=!lt(m,bo(0,c).slice(0,c-1)),b=()=>{if(y){let I=this.movingMean.read().reshape(f),P=this.movingVariance.read().reshape(f),E=this.center?this.beta.read().reshape(f):null,L=this.scale?this.gamma.read().reshape(f):null;return eh(o,I,P,E,L,this.epsilon)}else return eh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return b();let[v,T,N]=rq(o,this.gamma.read(),this.beta.read(),l,this.epsilon),S=(I,P,E)=>{rt(()=>{let L=1-E,B=I.read(),q=B.sub(P).mul(L);I.write(B.sub(q))})},D=()=>{S(this.movingMean,T,this.momentum),S(this.movingVariance,N,this.momentum)};return D(),v})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),movingMeanInitializer:rn(this.movingMeanInitializer),movingVarianceInitializer:rn(this.movingVarianceInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer),betaConstraint:vn(this.betaConstraint),gammaConstraint:vn(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}}z0.className="BatchNormalization",vt(z0);class W0 extends ye{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.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 e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected 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r=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let r=Qt(t),o=r.shape,s=o.length;return rt(()=>{let c=!0,{mean:l,variance:p}=Ld(r,this.axis,c),f=ya(1,s);for(let N of this.axis)f[N]=o[N];let m=N=>N!=null&&N.shape.length!==s&&this.axis!==[s-1]?N.reshape(f):N,y=m(this.gamma.read()),b=m(this.beta.read()),v=[],T=[];for(let N=0;N<s;++N)this.axis.indexOf(N)!==-1?(v.push(o[N]),T.push(1)):(v.push(1),T.push(o[N]));return l=l.tile(v),p=p.tile(v),y=y.tile(T),b=b.tile(T),eh(r,l,p,b,y,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:rn(this.betaInitializer),gammaInitializer:rn(this.gammaInitializer),betaRegularizer:Pe(this.betaRegularizer),gammaRegularizer:Pe(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}}W0.className="LayerNormalization",vt(W0);function vst(n,t){return rt(()=>{if(n.rank!==3)throw new Y(`temporalPadding expects input tensor to be 3-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[1,1]),t.length!==2)throw new Y(`temporalPadding expects input padding pattern to be a length-2 array, but received a length-${t.length} array.`);let e=[[0,0],t,[0,0]];return Go(n,e)})}function oq(n,t,e){return rt(()=>{if(n.rank!==4)throw new Y(`temporalPadding expects input tensor to be 4-D, but received a ${n.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new Y("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=yo()),e!=="channelsLast"&&e!=="channelsFirst")throw new Y(`Unknown data format: ${e}. 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length-${t.padding[1].length} array.`);r=t.padding[1]}this.padding=[e,r]}this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e,r;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?r=t[3]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],t[1],e,r]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?r=t[2]+this.padding[1][0]+this.padding[1][1]:r=null,[t[0],e,r,t[3]])}call(t,e){return rt(()=>oq(Qt(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}V0.className="ZeroPadding2D",vt(V0);function Bm(n,t,e,r,o,s){return rt(()=>{nn(o),HC(s),Lr(r),e==null&&(e=[1,1]),r==null&&(r="valid"),o==null&&(o=yo()),s==null&&(s="max"),n=g0(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=mp(n,t,e,l):c=ap(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,3,1,2])),c})}function o$(n,t,e,r,o,s){return rt(()=>{nn(o),HC(s),Lr(r),e==null&&(e=[1,1,1]),r==null&&(r="valid"),o==null&&(o=yo()),s==null&&(s="max"),n=JS(n,o);let c,l=r==="same"?"same":"valid";return s==="max"?c=Gw(n,t,e,l):c=Ew(n,t,e,l),o==="channelsFirst"&&(c=Kt(c,[0,4,1,2,3])),c})}class s$ extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new Y(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if($n(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new Y(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,Lr(this.padding),this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){t=Re(t);let e=ko(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return rt(()=>{this.invokeCallHook(t,e),t=Lp(Qt(t),2);let r=this.poolingFunction(Qt(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return fi(r,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}}class G0 extends s${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"max")}}G0.className="MaxPooling1D",vt(G0);class U0 extends s${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"avg")}}U0.className="AveragePooling1D",vt(U0);class i$ extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new Y(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Lr(this.padding),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=ko(e,this.poolSize[0],this.padding,this.strides[0]),r=ko(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r]:[t[0],e,r,t[3]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class q0 extends i${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"max")}}q0.className="MaxPooling2D",vt(q0);class H0 extends i${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),Bm(t,e,r,o,s,"avg")}}H0.className="AveragePooling2D",vt(H0);class a$ extends ye{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new Y(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];$n(this.poolSize,"poolSize"),$n(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),Lr(this.padding),this.inputSpec=[new In({ndim:5})]}computeOutputShape(t){t=Re(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],r=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=ko(e,this.poolSize[0],this.padding,this.strides[0]),r=ko(r,this.poolSize[1],this.padding,this.strides[1]),o=ko(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,r,o]:[t[0],e,r,o,t[4]]}call(t,e){return rt(()=>(this.invokeCallHook(t,e),this.poolingFunction(Qt(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class j0 extends a${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),o$(t,e,r,o,s,"max")}}j0.className="MaxPooling3D",vt(j0);class K0 extends a${constructor(t){super(t)}poolingFunction(t,e,r,o,s){return nn(s),Lr(o),o$(t,e,r,o,s,"avg")}}K0.className="AveragePooling3D",vt(K0);class c$ extends ye{constructor(t){super(t);this.inputSpec=[new In({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new Ut}}class X0 extends c${constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return en(r,1)})}}X0.className="GlobalAveragePooling1D",vt(X0);class Y0 extends c${constructor(t){super(t||{})}call(t,e){return rt(()=>{let r=Qt(t);return ur(r,1)})}}Y0.className="GlobalMaxPooling1D",vt(Y0);class l$ extends ye{constructor(t){super(t);this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,nn(this.dataFormat),this.inputSpec=[new In({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new Ut}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}}class J0 extends l${call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?en(r,[1,2]):en(r,[2,3])})}}J0.className="GlobalAveragePooling2D",vt(J0);class Z0 extends l${call(t,e){return rt(()=>{let r=Qt(t);return this.dataFormat==="channelsLast"?ur(r,[1,2]):ur(r,[2,3])})}}Z0.className="GlobalMaxPooling2D",vt(Z0);class u$ extends ye{constructor(t){super(t);this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}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(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,r={}){let o=e.layer,s=vo(o,r);delete e.layer;let c={layer:s};return Object.assign(c,e),new t(c)}}class Q0 extends u${constructor(t){super(t);this.supportsMasking=!0}build(t){if(t=Re(t),t.length<3)throw new Y(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Re(t);let e=[t[0]].concat(t.slice(2)),r=this.layer.computeOutputShape(e),o=t[1];return[r[0],o].concat(r.slice(1))}call(t,e){return rt(()=>{t=Qt(t);let r=(c,l)=>{let p=Qt(this.layer.call(c,e));return[p,[]]},o=n$(r,t,[],!1,null,null,!1,!0),s=o[1];return s})}}Q0.className="TimeDistributed",vt(Q0);function sq(n){Jc(dG,"BidirectionalMergeMode",n)}let iq="concat";class t1 extends u${constructor(t){super(t);let e=t.layer.getConfig(),r={};r.className=t.layer.getClassName(),r.config=e,this.forwardLayer=vo(r),e.goBackwards=!(e.goBackwards===!0);let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=vo(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?iq:t.mergeMode,sq(this.mergeMode),t.weights)throw new Ut("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,r=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,r)),this.backwardLayer.setWeights(t.slice(r))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let r,o,s;return this.returnState&&(s=e.slice(1)),r=e[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,o=[r]):this.mergeMode==null?o=[r,r.slice()]:o=[r],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[r].concat(s).concat(s.slice()):hr(o)}apply(t,e){let r=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=e$(t,r,o,this.numConstants);if(t=s.inputs,r=s.initialState,o=s.constants,Array.isArray(t)&&(r=t.slice(1),t=t[0]),(r==null||r.length===0)&&o==null)return super.apply(t,e);let c=[],l=[];if(r!=null){let f=r.length;if(f%2>0)throw new Y("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=r,c.push(...r);let m=r.map(y=>new In({shape:y.shape}));this.forwardLayer.stateSpec=m.slice(0,f/2),this.backwardLayer.stateSpec=m.slice(f/2),l.push(...m)}if(o!=null)throw new Ut("Support for constants in Bidirectional layers is not implemented yet.");let p=c[0]instanceof wo;for(let f of c)if(f instanceof wo!==p)throw new Y("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(p){let f=[t].concat(c),m=this.inputSpec.concat(l),y=this.inputSpec;this.inputSpec=m;let b=super.apply(f,e);return this.inputSpec=y,b}else return super.apply(t,e)}call(t,e){return rt(()=>{let r=e.initialState,o,s;if(r==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let p=r.slice(0,r.length/2),f=r.slice(r.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:p})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:f}))}let c;this.returnState&&(Array.isArray(o)&&(c=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=Pr(s,1));let l;return 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oj=Object.freeze({__proto__:null,json:rj});let 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ij=Object.freeze({__proto__:null,json:sj});let aj=[{tfOpName:"ResizeBilinear",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ResizeNearestNeighbor",category:"image",inputs:[{start:0,name:"images",type:"tensor"},{start:1,name:"size",type:"number[]"}],attrs:[{tfName:"align_corners",name:"alignCorners",type:"bool"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"CropAndResize",category:"image",inputs:[{start:0,name:"image",type:"tensor"},{start:1,name:"boxes",type:"tensor"},{start:2,name:"boxInd",type:"tensor"},{start:3,name:"cropSize",type:"number[]"}],attrs:[{tfName:"method",name:"method",type:"string"},{tfName:"extrapolation_value",name:"extrapolationValue",type:"number"}]}];var cj=Object.freeze({__proto__:null,json:aj});let lj=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var uj=Object.freeze({__proto__:null,json:lj});let pj=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var hj=Object.freeze({__proto__:null,json:pj});let fj=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}];var dj=Object.freeze({__proto__:null,json:fj});let mj=[{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}];var gj=Object.freeze({__proto__:null,json:mj});let yj=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool",notSupported:!0}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}];var bj=Object.freeze({__proto__:null,json:yj});let xj=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}];var wj=Object.freeze({__proto__:null,json:xj});let vj=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}];var Tj=Object.freeze({__proto__:null,json:vj});class w${static get Instance(){return this._instance||(this._instance=new this)}constructor(){let t=[UH,HH,KH,YH,ZH,tj,nj,uj,cj,oj,hj,dj,gj,bj,wj,Tj,ij],e=[].concat(...t.map(r=>r.json));this.opMappers=e.reduce((r,o)=>(r[o.tfOpName]=o,r),{})}transformGraph(t,e={}){let r=t.node,o=[],s=[],c=[],l=r.reduce((N,S)=>(N[S.name]=this.mapNode(S),S.op.startsWith("Placeholder")?o.push(N[S.name]):S.op==="Const"?s.push(N[S.name]):(S.input==null||S.input.length===0)&&c.push(N[S.name]),N),{}),p=[],f=[],m={},y={};e!=null&&(m=this.mapSignatureEntries(e.inputs),y=this.mapSignatureEntries(e.outputs));let b=Object.keys(l);b.forEach(N=>{let S=l[N];S.inputNames.forEach(D=>{let[I]=Ns(D);S.inputs.push(l[I]),l[I].children.push(S)})}),Object.keys(y).length===0?b.forEach(N=>{let S=l[N];S.children.length===0&&f.push(S)}):Object.keys(y).forEach(N=>{let[S]=Ns(N),D=l[S];D!=null&&(D.signatureKey=y[N],f.push(D))}),Object.keys(m).length>0?Object.keys(m).forEach(N=>{let[S]=Ns(N),D=l[S];D&&(D.signatureKey=m[N],p.push(D))}):p=o;let v={};t.library!=null&&t.library.function!=null&&(v=t.library.function.reduce((N,S)=>(N[S.signature.name]=this.mapFunction(S),N),{}));let T={nodes:l,inputs:p,outputs:f,weights:s,placeholders:o,signature:e,functions:v};return c.length>0&&(T.initNodes=c),T}mapSignatureEntries(t){return Object.keys(t||{}).reduce((e,r)=>(e[t[r].name]=r,e),{})}mapNode(t){let e=x$(t.op)||this.opMappers[t.op]||{};t.attr==null&&(t.attr={});let r={name:t.name,op:t.op,category:e.category,inputNames:(t.input||[]).map(o=>o.startsWith("^")?o.substr(1):o),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:t.attr};return 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0&&!!s.tfDeprecatedName&&(l=m1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":l=p1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=p1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":l=d1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=d1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":l=l1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=l1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":l=u1(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=u1(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":l=T$(t.attr,s.tfName,s.defaultValue),l===void 0&&!!s.tfDeprecatedName&&(l=T$(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${t.op}`)}return o[s.name]={value:l,type:c},o},{})),r}mapFunction(t){let e=t.nodeDef,r=[],o=[],s={};e!=null&&(s=e.reduce((y,b)=>(y[b.name]=this.mapNode(b),b.op==="Const"&&o.push(y[b.name]),y),{}));let c=[],l=[];t.signature.inputArg.forEach(y=>{let[b]=Ns(y.name),v={name:b,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:c1(y.type),type:"dtype"}},children:[]};v.signatureKey=y.name,c.push(v),s[b]=v});let p=Object.keys(s);p.forEach(y=>{let b=s[y];b.inputNames.forEach(v=>{let[T]=Ns(v);b.inputs.push(s[T]),s[T].children.push(b)})});let f=t.ret;t.signature.outputArg.forEach(y=>{let[b,v]=Ns(f[y.name]),T=s[b];T!=null&&(T.defaultOutput=v,l.push(T))});let m=this.mapArgsToSignature(t);return{nodes:s,inputs:c,outputs:l,weights:o,placeholders:r,signature:m}}mapArgsToSignature(t){return{methodName:t.signature.name,inputs:t.signature.inputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r),e),{}),outputs:t.signature.outputArg.reduce((e,r)=>(e[r.name]=this.mapArgToTensorInfo(r,t.ret),e),{})}}mapArgToTensorInfo(t,e){let r=t.name;return e!=null&&(r=e[r]),{name:r,dtype:t.type}}}function kj(n){let t=ct().global;if(typeof t.atob!="undefined")return t.atob(n);if(typeof Buffer!="undefined")return new Buffer(n,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function v$(n,t){let e=Array.isArray(n)?String.fromCharCode.apply(null,n):kj(n);return t?e:e.toLowerCase()}function s1(n,t,e,r=!1){let o=n[t];return o!=null?v$(o.s,r):e}function i1(n,t,e){let r=n[t];return r?r.b:e}function a1(n,t,e){let r=n[t]||{},o=r.i!=null?r.i:r.f!=null?r.f:e;return typeof o=="number"?o:parseInt(o,10)}function c1(n){typeof n=="string"&&(n=_o[n]);switch(n){case _o.DT_FLOAT:return"float32";case _o.DT_INT32:case _o.DT_INT64:case _o.DT_INT8:case _o.DT_UINT8:return"int32";case _o.DT_BOOL:return"bool";case _o.DT_DOUBLE:return"float32";case _o.DT_STRING:return"string";default:return null}}function T$(n,t,e){let r=n[t];return r&&r.func?r.func.name:e}function l1(n,t,e){let r=n[t];return r&&r.type?c1(r.type):e}function u1(n,t,e){let r=n[t];return r&&r.list&&r.list.type?r.list.type.map(o=>c1(o)):e}function k$(n){return n.unknownRank?void 0:n.dim!=null?n.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function p1(n,t,e){let r=n[t];return r&&r.shape?k$(r.shape):e}function h1(n,t,e){let r=n[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(o=>typeof o=="number"?o:parseInt(o,10)):e}function f1(n,t,e,r=!1){let o=n[t];return o&&o.list&&o.list.s?o.list.s.map(s=>v$(s,r)):e}function d1(n,t,e){let r=n[t];return r&&r.list&&r.list.shape?r.list.shape.map(o=>k$(o)):e}function m1(n,t,e){let r=n[t];return r&&r.list&&r.list.b?r.list.b:e}class Nj{constructor(t,e,r){this.node=t,this.tensorMap=e,this.context=r,this.inputs=[],this.attrs={},this.inputs=t.inputNames.map(o=>this.getInput(o)),t.rawAttrs!=null&&(this.attrs=Object.keys(t.rawAttrs).reduce((o,s)=>(o[s]=this.getAttr(s),o),{}))}getInput(t){return dr(t,this.tensorMap,this.context)}getAttr(t,e){let r=this.node.rawAttrs[t];if(r.tensor!=null)return dr(t,this.tensorMap,this.context);if(r.i!=null||r.f!=null)return a1(this.node.rawAttrs,t,e);if(r.s!=null)return s1(this.node.rawAttrs,t,e);if(r.b!=null)return i1(this.node.rawAttrs,t,e);if(r.shape!=null)return p1(this.node.rawAttrs,t,e);if(r.type!=null)return l1(this.node.rawAttrs,t,e);if(r.list!=null){if(r.list.i!=null||r.list.f!=null)return h1(this.node.rawAttrs,t,e);if(r.list.s!=null)return f1(this.node.rawAttrs,t,e);if(r.list.shape!=null)return d1(this.node.rawAttrs,t,e);if(r.list.b!=null)return m1(this.node.rawAttrs,t,e);if(r.list.type!=null)return u1(this.node.rawAttrs,t,e)}return e}}let _j=(n,t,e)=>{switch(n.op){case"BiasAdd":case"AddV2":case"Add":return[Tt(A("a",n,t,e),A("b",n,t,e))];case"AddN":return[C_(A("tensors",n,t,e))];case"FloorMod":case"Mod":return[Od(A("a",n,t,e),A("b",n,t,e))];case"Mul":return[nt(A("a",n,t,e),A("b",n,t,e))];case"RealDiv":case"Div":return[Bt(A("a",n,t,e),A("b",n,t,e))];case"DivNoNan":return[Ow(A("a",n,t,e),A("b",n,t,e))];case"FloorDiv":return[wd(A("a",n,t,e),A("b",n,t,e))];case"Sub":return[Dt(A("a",n,t,e),A("b",n,t,e))];case"Minimum":return[ua(A("a",n,t,e),A("b",n,t,e))];case"Maximum":return[Jr(A("a",n,t,e),A("b",n,t,e))];case"Pow":return[mo(A("a",n,t,e),A("b",n,t,e))];case"SquaredDifference":return[Tp(A("a",n,t,e),A("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},kst="arithmetic";let Cj=(n,t,e)=>{switch(n.op){case"Abs":case"ComplexAbs":return[bn(A("x",n,t,e))];case"Acos":return[xw(A("x",n,t,e))];case"Acosh":return[ww(A("x",n,t,e))];case"Asin":return[kw(A("x",n,t,e))];case"Asinh":return[Nw(A("x",n,t,e))];case"Atan":return[_w(A("x",n,t,e))];case"Atan2":return[Cw(A("x",n,t,e),A("y",n,t,e))];case"Atanh":return[Sw(A("x",n,t,e))];case"Ceil":return[Dw(A("x",n,t,e))];case"Complex":return[fs(A("real",n,t,e),A("imag",n,t,e))];case"Cos":return[up(A("x",n,t,e))];case"Cosh":return[$d(A("x",n,t,e))];case"Elu":return[Oc(A("x",n,t,e))];case"Erf":return[Lw(A("x",n,t,e))];case"Exp":return[Fr(A("x",n,t,e))];case"Expm1":return[Mw(A("x",n,t,e))];case"Floor":return[Mc(A("x",n,t,e))];case"Log":return[wr(A("x",n,t,e))];case"Log1p":return[Ad(A("x",n,t,e))];case"Imag":return[hp(A("x",n,t,e))];case"Neg":return[tn(A("x",n,t,e))];case"Reciprocal":return[Kw(A("x",n,t,e))];case"Real":return[Vc(A("x",n,t,e))];case"Relu":return[Uo(A("x",n,t,e))];case"Round":return[Yw(A("x",n,t,e))];case"Selu":return[zd(A("x",n,t,e))];case"Sigmoid":return[Wo(A("x",n,t,e))];case"Sin":return[Wd(A("x",n,t,e))];case"Sign":return[Zw(A("x",n,t,e))];case"Sinh":return[Vd(A("x",n,t,e))];case"Softplus":return[zc(A("x",n,t,e))];case"Sqrt":return[On(A("x",n,t,e))];case"Square":return[Ae(A("x",n,t,e))];case"Tanh":return[Pc(A("x",n,t,e))];case"Tan":return[ev(A("x",n,t,e))];case"Relu6":case"ClipByValue":return[cr(A("x",n,t,e),A("clipValueMin",n,t,e),A("clipValueMax",n,t,e))];case"Rsqrt":return[Bd(dr(n.inputNames[0],t,e))];case"Prod":return[Md(A("x",n,t,e),A("axes",n,t,e))];case"LeakyRelu":return[Dd(A("x",n,t,e),A("alpha",n,t,e))];case"Prelu":return[yp(A("x",n,t,e),A("alpha",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Nst="basic_math";function no(n,t,e=""){_(Sj(n,t),()=>e+` Shapes ${n} and ${t} must match`)}function Sj(n,t){if(n.length!==t.length)return!1;for(let e=0;e<n.length;e++)if(n[e]!==-1&&t[e]!==-1&&n[e]!==t[e])return!1;return!0}class $j{constructor(t,e,r,o,s,c,l){this.name=t,this.dtype=e,this.maxSize=r,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=c,this.clearAfterRead=l,this.tensors=[],this.closed_=!1,this.idTensor=Et(0),Sn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let e=this.tensors[t];if(e.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${t} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor}readMany(t){return t.map(e=>this.read(e))}write(t,e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||!this.dynamicSize&&t>=this.maxSize)throw new Error(`Tried to write to index ${t}, but array is not resizeable and size is: ${this.maxSize}`);let r=this.tensors[t]||{};if(e.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t},
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r=A("elementShape",n,t,e),o=A("elementDType",n,t,e),s=A("numElements",n,t,e),c=Ej(r,o,s);return e.addTensorList(c),[c.idTensor]}case"TensorListGather":{let r=A("tensorListId",n,t,e),o=A("indices",n,t,e),s=A("elementShape",n,t,e),c=A("elementDType",n,t,e),l=e.getTensorList(r.id);return[l.gather(o,c,s)]}case"TensorListStack":{let r=A("tensorListId",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=A("numElements",n,t,e),l=e.getTensorList(r.id);return[l.stack(o,s,c)]}case"TensorListFromTensor":{let r=A("tensor",n,t,e),o=A("elementShape",n,t,e),s=A("elementDType",n,t,e),c=Ij(r,o,s);return e.addTensorList(c),[c.idTensor]}case"TensorListConcat":{let r=A("tensorListId",n,t,e),o=e.getTensorList(r.id),s=A("dtype",n,t,e),c=A("elementShape",n,t,e);return[o.concat(s,c)]}case"TensorListPushBack":{let r=A("tensorListId",n,t,e),o=A("tensor",n,t,e),s=e.getTensorList(r.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let 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f=A("strides",n,t,e),m=Vm(n,t,e),y=A("dataFormat",n,t,e).toUpperCase(),b=A("dilations",n,t,e),[v,T]=A("args",n,t,e);return{stride:f,pad:m,dataFormat:y,dilations:b,biasArg:v,preluArg:T,activationFunc:o}}let Rj=(n,t,e)=>{switch(n.op){case"Conv1D":{let r=A("stride",n,t,e),o=A("pad",n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilation",n,t,e);return[Cd(A("x",n,t,e),A("filter",n,t,e),r,o,s,c)]}case"Conv2D":{let r=A("strides",n,t,e),o=Vm(n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilations",n,t,e);return[gs(A("x",n,t,e),A("filter",n,t,e),[r[1],r[2]],o,s,[c[1],c[2]])]}case"_FusedConv2D":{let{stride:r,pad:o,dataFormat:s,dilations:c,biasArg:l,preluArg:p,activationFunc:f}=N$(n,t,e);return[cv({x:A("x",n,t,e),filter:A("filter",n,t,e),strides:[r[1],r[2]],pad:o,dataFormat:s,dilations:[c[1],c[2]],bias:l,activation:f,preluActivationWeights:p})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:o,dataFormat:s,dilations:c,biasArg:l,preluArg:p,activationFunc:f}=N$(n,t,e);return[bC({x:A("x",n,t,e),filter:A("filter",n,t,e),strides:[r[1],r[2]],pad:o,dataFormat:s,dilations:[c[1],c[2]],bias:l,activation:f,preluActivationWeights:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=A("outputShape",n,t,e),o=A("strides",n,t,e),s=Vm(n,t,e);return[Sd(A("x",n,t,e),A("filter",n,t,e),r,[o[1],o[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=A("strides",n,t,e),o=Vm(n,t,e),s=A("dilations",n,t,e),c=A("dataFormat",n,t,e).toUpperCase();return[ca(A("input",n,t,e),A("filter",n,t,e),[r[1],r[2]],o,c,[s[1],s[2]])]}case"Conv3D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("dataFormat",n,t,e).toUpperCase(),c=A("dilations",n,t,e);return[Fw(A("x",n,t,e),A("filter",n,t,e),[r[1],r[2],r[3]],o,s,[c[1],c[2],c[3]])]}case"AvgPool":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[ap(A("x",n,t,e),[s[1],s[2]],[r[1],r[2]],o)]}case"MaxPool":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[mp(A("x",n,t,e),[s[1],s[2]],[r[1],r[2]],o)]}case"MaxPoolWithArgmax":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e),c=A("includeBatchInIndex",n,t,e),{result:l,indexes:p}=U_(A("x",n,t,e),[s[1],s[2]],[r[1],r[2]],o,c);return[l,p]}case"AvgPool3D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[Ew(A("x",n,t,e),[s[1],s[2],s[3]],[r[1],r[2],r[3]],o)]}case"MaxPool3D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("kernelSize",n,t,e);return[Gw(A("x",n,t,e),[s[1],s[2],s[3]],[r[1],r[2],r[3]],o)]}case"Dilation2D":{let r=A("strides",n,t,e),o=A("pad",n,t,e),s=A("dilations",n,t,e),c=r[1],l=r[2],p=s[1],f=s[2];return[Pw(A("x",n,t,e),A("filter",n,t,e),[c,l],o,[p,f],"NHWC")]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Cst="convolution";let Pj=(n,t,e)=>{switch(n.op){case"Fill":{let r=A("shape",n,t,e),o=A("dtype",n,t,e),s=A("value",n,t,e);return[Lc(r,s,o)]}case"LinSpace":{let r=A("start",n,t,e),o=A("stop",n,t,e),s=A("num",n,t,e);return[W_(r,o,s)]}case"Multinomial":{let r=A("logits",n,t,e),o=A("numSamples",n,t,e),s=A("seed",n,t,e);return[q_(r,o,s)]}case"OneHot":{let 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Oj=async(n,t,e)=>{switch(n.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:o,maxOutputSize:s,iouThreshold:c,scoreThreshold:l,softNmsSigma:p}=g1(n,t,e),f=await mi.nonMaxSuppressionWithScoreAsync(r,o,s,c,l,p);return[f.selectedIndices,f.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:o,maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=g1(n,t,e),p=A("padToMaxOutputSize",n,t,e),f=await mi.nonMaxSuppressionPaddedAsync(r,o,s,c,l,p);return[f.selectedIndices,f.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:o,maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=g1(n,t,e);return[await mi.nonMaxSuppressionAsync(r,o,s,c,l)]}case"Where":{let r=$t(A("condition",n,t,e),"bool"),o=[await ov(r)];return r.dispose(),o}case"ListDiff":return j_(A("x",n,t,e),A("y",n,t,e));default:throw TypeError(`Node type ${n.op} is not implemented`)}},$st="dynamic";let Lj=(n,t,e)=>{switch(n.op){case"TopKV2":{let r=A("x",n,t,e),o=A("k",n,t,e),s=A("sorted",n,t,e),c=nv(r,o,s);return[c.values,c.indices]}case"Unique":{let r=A("x",n,t,e),o=Hd(r);return[o.values,o.indices]}case"UniqueV2":{let r=A("x",n,t,e),o=A("axis",n,t,e),s=Hd(r,o);return[s.values,s.indices]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ist="evaluation";let Mj=(n,t,e)=>{switch(n.op){case"Const":return t[n.name];case"PlaceholderWithDefault":let r=A("default",n,t,e);return[dr(n.name,t,e)||r];case"Placeholder":return[dr(n.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let f=A("x",n,t,e);return[_s(f)]}case"IdentityN":return A("x",n,t,e).map(f=>_s(f));case"Snapshot":let o=A("x",n,t,e);return[_s(o)];case"Shape":return[vr(A("x",n,t,e).shape,"int32")];case"ShapeN":return A("x",n,t,e).map(f=>vr(f.shape));case"Size":return[Et(A("x",n,t,e).size,"int32")];case"Rank":return[Et(A("x",n,t,e).rank,"int32")];case"NoOp":return[Et(1)];case"Print":let s=A("x",n,t,e),c=A("data",n,t,e),l=A("message",n,t,e),p=A("summarize",n,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(l);for(let f=0;f<c.length;f++)console.log(Array.prototype.slice.call(c[f].dataSync()).slice(0,p));return[s];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Est="graph";class Bj{constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=Et(0),this.tensorMap=new Map,Sn(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),rt(()=>{let o=go(e),s=r.length,c=o.length;_(s===c,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${c} elements.`);for(let l=0;l<s;l++){let p=r[l],f=o[l];Sn(f),this.tensorMap.set(p,f)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let r=await t.data();return rt(()=>{let o=[];for(let s=0;s<r.length;s++){let c=r[s],l=this.findWithDefault(c,e);o.push(l)}return pr(o)})}findWithDefault(t,e){let r=this.tensorMap.get(t);return r!=null?r:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}}let zj=async(n,t,e,r)=>{switch(n.op){case"HashTable":case"HashTableV2":{let o=A("keyDType",n,t,e),s=A("valueDType",n,t,e),c=new Bj(o,s);return r.addHashTable(n.name,c),[c.handle]}case"LookupTableImport":case"LookupTableImportV2":{let o=A("tableHandle",n,t,e,r),s=A("keys",n,t,e),c=A("values",n,t,e),l=r.getHashTableById(o.id);return[await l.import(s,c)]}case"LookupTableFind":case"LookupTableFindV2":{let o=A("tableHandle",n,t,e,r),s=A("keys",n,t,e),c=A("defaultValue",n,t,e),l=r.getHashTableById(o.id);return[await l.find(s,c)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Dst="hash_table";let Wj=(n,t,e)=>{switch(n.op){case"ResizeBilinear":{let r=A("images",n,t,e),o=A("size",n,t,e),s=A("alignCorners",n,t,e);return[mi.resizeBilinear(r,[o[0],o[1]],s)]}case"ResizeNearestNeighbor":{let r=A("images",n,t,e),o=A("size",n,t,e),s=A("alignCorners",n,t,e);return[mi.resizeNearestNeighbor(r,[o[0],o[1]],s)]}case"CropAndResize":{let r=A("image",n,t,e),o=A("boxes",n,t,e),s=A("boxInd",n,t,e),c=A("cropSize",n,t,e),l=A("method",n,t,e),p=A("extrapolationValue",n,t,e);return[mi.cropAndResize(r,o,s,c,l,p)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ast="image";let Vj=(n,t,e)=>{switch(n.op){case"Equal":return[ho(A("a",n,t,e),A("b",n,t,e))];case"NotEqual":return[hi(A("a",n,t,e),A("b",n,t,e))];case"Greater":return[Rr(A("a",n,t,e),A("b",n,t,e))];case"GreaterEqual":return[ys(A("a",n,t,e),A("b",n,t,e))];case"Less":return[fp(A("a",n,t,e),A("b",n,t,e))];case"LessEqual":return[pi(A("a",n,t,e),A("b",n,t,e))];case"LogicalAnd":return[Zr(A("a",n,t,e),A("b",n,t,e))];case"LogicalNot":return[dp(A("a",n,t,e))];case"LogicalOr":return[Pd(A("a",n,t,e),A("b",n,t,e))];case"Select":case"SelectV2":return[Yn(A("condition",n,t,e),A("a",n,t,e),A("b",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Fst="logical";let 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Uj=(n,t,e)=>{switch(n.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[aa(A("x",n,t,e),A("mean",n,t,e),A("variance",n,t,e),A("offset",n,t,e),A("scale",n,t,e),A("epsilon",n,t,e))];case"FusedBatchNormV3":return[aa(A("x",n,t,e),A("mean",n,t,e),A("variance",n,t,e),A("offset",n,t,e),A("scale",n,t,e),A("epsilon",n,t,e))];case"LRN":return[zw(A("x",n,t,e),A("radius",n,t,e),A("bias",n,t,e),A("alpha",n,t,e),A("beta",n,t,e))];case"Softmax":return[da(A("x",n,t,e))];case"LogSoftmax":return[Rd(A("x",n,t,e))];case"SparseToDense":return[sv(A("sparseIndices",n,t,e),A("outputShape",n,t,e),A("sparseValues",n,t,e),A("defaultValue",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Pst="normalization";let qj=(n,t,e)=>{switch(n.op){case"Max":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[ur(A("x",n,t,e),r,o)]}case"Mean":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[en(A("x",n,t,e),r,o)]}case"Min":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[Wc(A("x",n,t,e),r,o)]}case"Sum":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[zt(A("x",n,t,e),r,o)]}case"All":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[vd(A("x",n,t,e),r,o)]}case"Any":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[op(A("x",n,t,e),r,o)]}case"ArgMax":{let r=A("axis",n,t,e);return[sp(A("x",n,t,e),r)]}case"ArgMin":{let r=A("axis",n,t,e);return[Tw(A("x",n,t,e),r)]}case"Prod":{let r=A("axis",n,t,e),o=A("keepDims",n,t,e);return[Md(A("x",n,t,e),r,o)]}case"Cumsum":{let r=A("axis",n,t,e),o=A("exclusive",n,t,e),s=A("reverse",n,t,e);return[Id(A("x",n,t,e),r,o,s)]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Ost="reduction";let Hj=(n,t,e)=>{switch(n.op){case"ConcatV2":case"Concat":{let r=A("n",n,t,e),o=A("axis",n,t,e),s=A("tensors",n,t,e);return s=s.slice(0,r),[Qe(s,o)]}case"GatherV2":case"Gather":{let r=A("axis",n,t,e),o=A("x",n,t,e),s=A("indices",n,t,e);return[Bc(o,$t(s,"int32"),r)]}case"ReverseV2":case"Reverse":{let r=A("axis",n,t,e),o=A("x",n,t,e);return[Pr(o,r)]}case"Slice":{let r=A("begin",n,t,e),o=A("size",n,t,e);return[ce(A("x",n,t,e),r,o)]}case"StridedSlice":{let r=A("begin",n,t,e),o=A("end",n,t,e),s=A("strides",n,t,e),c=A("beginMask",n,t,e),l=A("endMask",n,t,e),p=A("ellipsisMask",n,t,e),f=A("newAxisMask",n,t,e),m=A("shrinkAxisMask",n,t,e),y=A("x",n,t,e);return[tv(y,r,o,s,c,l,p,f,m)]}case"Pack":return rt(()=>{let r=A("axis",n,t,e),o=A("tensors",n,t,e),s=o[0].shape,c=fi(o[0]).shape,l=o.map(p=>{let f=lt(p.shape,s);if(!f&&!lt(fi(p).shape,c))throw new Error("the input tensors shape does not match");return f?p:Q(p,s)});return[pr(l,r)]});case"Unpack":{let r=A("axis",n,t,e),o=A("tensor",n,t,e);return go(o,r)}case"Tile":{let r=A("reps",n,t,e);return[ui(A("x",n,t,e),r)]}case"Split":case"SplitV":{let r=A("axis",n,t,e),o=A("numOrSizeSplits",n,t,e),s=A("x",n,t,e);return Tr(s,o,r)}case"ScatterNd":{let r=A("indices",n,t,e),o=A("values",n,t,e),s=A("shape",n,t,e);return[hC(r,o,s)]}case"GatherNd":{let r=A("x",n,t,e),o=A("indices",n,t,e);return[fC(r,o)]}case"SparseToDense":{let r=A("sparseIndices",n,t,e),o=A("outputShape",n,t,e),s=A("sparseValues",n,t,e),c=A("defaultValue",n,t,e);return[sv(r,s,o,s.dtype===c.dtype?c:$t(c,s.dtype))]}default:throw TypeError(`Node type ${n.op} is not implemented`)}},Lst="slice_join";let jj=(n,t,e)=>{switch(n.op){case"FFT":return[wp(A("x",n,t,e))];case"IFFT":return[qc(A("x",n,t,e))];case"RFFT":return[vp(A("x",n,t,e))];case"IRFFT":return[qd(A("x",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Mst="spectral";let Kj=(n,t,e)=>{switch(n.op){case"Cast":return[$t(A("x",n,t,e),A("dtype",n,t,e))];case"ExpandDims":{let r=A("axis",n,t,e);return[lr(A("x",n,t,e),r)]}case"Squeeze":{let r=A("axis",n,t,e);return[fi(A("x",n,t,e),r)]}case"Reshape":return[Q(A("x",n,t,e),A("shape",n,t,e))];case"MirrorPad":return[Uw(A("x",n,t,e),A("padding",n,t,e),A("mode",n,t,e))];case"PadV2":case"Pad":return[Go(A("x",n,t,e),A("padding",n,t,e),A("constantValue",n,t,e))];case"SpaceToBatchND":{let r=A("blockShape",n,t,e),o=A("paddings",n,t,e);return[gp(A("x",n,t,e),r,o)]}case"BatchToSpaceND":{let r=A("blockShape",n,t,e),o=A("crops",n,t,e);return[cp(A("x",n,t,e),r,o)]}case"DepthToSpace":{let r=A("blockSize",n,t,e),o=A("dataFormat",n,t,e).toUpperCase();return[Rw(A("x",n,t,e),r,o)]}case"BroadcastTo":return[lp(A("x",n,t,e),A("shape",n,t,e))];default:throw TypeError(`Node type ${n.op} is not implemented`)}},Bst="transformation";function _$(n,t,e,r){let o=((s,c,l)=>{switch(s.category){case"arithmetic":return rt(()=>_j(s,c,l));case"basic_math":return rt(()=>Cj(s,c,l));case"control":return Fj(s,c,l);case"convolution":return rt(()=>Rj(s,c,l));case"creation":return rt(()=>Pj(s,c,l));case"dynamic":return Oj(s,c,l);case"evaluation":return rt(()=>Lj(s,c,l));case"image":return rt(()=>Wj(s,c,l));case"graph":return rt(()=>Mj(s,c,l));case"logical":return rt(()=>Vj(s,c,l));case"matrices":return rt(()=>Gj(s,c,l));case"normalization":return rt(()=>Uj(s,c,l));case"reduction":return rt(()=>qj(s,c,l));case"slice_join":return rt(()=>Hj(s,c,l));case"spectral":return rt(()=>jj(s,c,l));case"transformation":return rt(()=>Kj(s,c,l));case"hash_table":return zj(s,c,l,r);case"custom":let p=x$(s.op);if(p&&p.customExecutor)return p.customExecutor(new Nj(s,c,l));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(n,t,e);return Ji(o)?o.then(s=>[].concat(s)):[].concat(o)}class C${constructor(t={},e={},r={},o={}){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=r,this.functionMap=o,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let r=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(r))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}}function S$(n,t,e,r){let o=new Set,s=[],c=null,l=null,p=new Set,f=Object.keys(n).map(b=>Nr(b)[0]),m=[];r!=null&&(m=r.map(b=>Nr(b.name)[0]));let y=[...t];for(;y.length>0;){let b=y.pop();if(($$(b)||Qj(b)||t6(b))&&(c==null&&(c=b,l=c.children.map(v=>v.name).filter(v=>o.has(v)))),o.add(b.name),e[b.name]!=null)continue;if(f.indexOf(b.name)!==-1)continue;if(m.indexOf(b.name)!==-1)continue;if(b.inputs.length===0){s.push(b.name);continue}b.inputs.forEach(v=>{if(p.has(v.name))return;p.add(v.name),y.push(v)})}return{inputs:n,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:c,syncInputs:l}}function Xj(n,t,e){let{usedNodes:r,inputs:o}=e,s=[],c=Object.keys(o).map(m=>Nr(m)[0]).map(m=>n.nodes[m]),l=n.initNodes;c.forEach(m=>{r.has(m.name)&&s.push(m)}),n.weights.forEach(m=>{r.has(m.name)&&s.push(m)}),l!=null&&l.forEach(m=>{r.has(m.name)&&s.push(m)});let p=new Set,f=[];for(;s.length>0;){let m=s.pop();p.add(m.name),t[m.name]||f.push(m),m.children.forEach(y=>{!p.has(y.name)&&r.has(y.name)&&y.inputs.every(b=>p.has(b.name))&&s.push(y)})}return f}let Yj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Jj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Zj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function $$(n){return Yj.indexOf(n.op)>=0}function Qj(n){return Jj.indexOf(n.op)>=0}function t6(n){return Zj.indexOf(n.op)>=0}class Gm{constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(r=>{this._functionExecutorMap[r]=new Gm(t.functions[r],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(t){let e=Object.keys(t).map(r=>t[r].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}getCompilationKey(t,e){let r=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return r.join(this.SEPERATOR)+"--"+o.join(this.SEPERATOR)}compile(t,e){let r=S$(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:c}=r;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${c}]`);if(o.length>0){let l=e.map(f=>f.name),p=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${p}]. Missing the following inputs: [${o}]`)}return Xj(this.graph,this.weightMap,r)}execute(t,e){t=this.mapInputs(t);let r=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=r.map(y=>this.graph.nodes[Nr(y)[0]]),s=e.map(y=>Nr(y)[0]),c=s.map(y=>this.graph.nodes[y]);c.length===0&&(c=this._outputs);let l=this.getCompilationKey(o,c),p=this.compiledMap.get(l);p==null&&(p=this.compile(t,c),this.compiledMap.set(l,p));let f={},m={};return rt(()=>{let y=new C$(this.weightMap,f,m,this.functionExecutorMap),b=Object.assign({},this.weightMap);Object.keys(t).forEach(N=>{let[S,D]=Nr(N),I=[];I[D]=t[N],b[S]=I});let v=this.getFrozenTensorIds(b),T={};for(let N=0;N<p.length;N++){let S=p[N];if(!b[S.name]){let D=_$(S,b,y,this._resourceManager);if(Ji(D))throw new Error(`The execution of the op '${S.op}' returned a promise. Please use model.executeAsync() instead.`);b[S.name]=D,this.checkTensorForDisposal(S.name,S,b,y,v,s,T)}}return this.parent==null&&y.dispose(v),e.map(N=>dr(N,b,y))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(r=>t[r]).map(r=>r.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,r,o,s,c,l){if(e.category==="control"||c.indexOf(t)!==-1)return;r[t].forEach(p=>{p!=null&&(l[p.id]=(l[p.id]||0)+e.children.length)}),e.inputs.forEach(p=>{if(p.category!=="control"){let f=VH(p.name,r,o);f!=null&&f.forEach(m=>{if(m&&!s.has(m.id)){let y=l[m.id];y===1?(m.dispose(),delete l[m.id]):y!=null&&l[m.id]--}})}})}async executeAsync(t,e){return this._executeAsync(t,e)}async _executeAsync(t,e,r=!1,o={},s={}){r||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));let c=new C$(this.weightMap,o,s,this.functionExecutorMap),l=await this.executeWithControlFlow(t,c,e,r),p=e.map(b=>dr(b,l,c)),f=p.map(b=>b.id),m=Object.keys(t).map(b=>t[b].id),y=new Set([...f,...m,...this.weightIds]);return Object.keys(l).forEach(b=>{let v=l[b];v.forEach(T=>{T&&!T.isDisposed&&!y.has(T.id)&&T.dispose()})}),this.parent==null&&c.dispose(y),p}async executeFunctionAsync(t,e,r){let o=t.reduce((s,c,l)=>(s[this.inputs[l].name]=c,s),{});return this._executeAsync(o,this.outputNodes,!0,e,r)}async executeWithControlFlow(t,e,r,o){let s=Object.keys(t),c=s.map(P=>this.graph.nodes[Nr(P)[0]]),l=r.map(P=>Nr(P)[0]),p=l.map(P=>this.graph.nodes[P]);p.length===0&&(p=this._outputs);let{usedNodes:f,missingInputs:m,dynamicNode:y,syncInputs:b}=S$(t,p,this.weightMap,this._initNodes),v=[...c,...this.graph.weights,...this._initNodes||[]].map(P=>({node:P,contexts:e.currentContext})),T=Object.assign({},this.weightMap);Object.keys(t).forEach(P=>{let[E,L]=Nr(P),B=[];B[L]=t[P],T[E]=B});let N={},S=this.getFrozenTensorIds(T),D={};for(;v.length>0;){let P=this.processStack(c,v,e,T,D,S,l,N,f);await Promise.all(P)}y==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let I=p.filter(P=>!$$(P)&&!dr(P.name,T,e)).map(P=>P.name);if(I.length>0){let P="";throw y!=null&&(P=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${b}]`),new Error(`Cannot compute the outputs [${I}] from the provided inputs [${s}]. Consider providing the following inputs: [${m}]. ${P}`)}return T}processStack(t,e,r,o,s,c,l,p,f){let m=[];for(;e.length>0;){let y=e.pop();r.currentContext=y.contexts;let b="";if(y.node.op==="Enter"&&A("isConstant",y.node,o,r)&&([b]=Ns(y.node.name,r)),o[y.node.name]==null){let v=_$(y.node,o,r,this._resourceManager);b||([b]=Ns(y.node.name,r));let T=r.currentContext;Ji(v)?m.push(v.then(N=>(o[b]=N,r.currentContext=T,this.checkTensorForDisposal(b,y.node,o,r,c,l,p),this.processChildNodes(y.node,e,r,o,s,f),N))):(o[b]=v,this.checkTensorForDisposal(b,y.node,o,r,c,l,p),this.processChildNodes(y.node,e,r,o,s,f))}else this.processChildNodes(y.node,e,r,o,s,f)}return m}processChildNodes(t,e,r,o,s,c){t.children.forEach(l=>{let[p]=Ns(l.name,r);if(s[p]||!c.has(l.name))return;l.op==="Merge"?l.inputNames.some(f=>!!dr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l})):l.inputNames.every(f=>!!dr(f,o,r))&&(s[p]=!0,e.push({contexts:r.currentContext,node:l}))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let r=t[e],[o]=Nr(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let c=s.attrParams.shape.value,l=c.length===r.shape.length&&r.shape.every((p,f)=>c[f]===-1||c[f]===p);_(l,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${c}], but was [${r.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&_(r.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(t){let e={};for(let r in t)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let o=this._signature.inputs[r];e[o.name]=t[r]}else e[r]=t[r];return e}checkInputs(t){let e=Object.keys(t).filter(r=>{let[o]=Nr(r);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{if(this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[e]!=null){let r=this._signature.outputs[e];return r.name}return e},{})}checkOutputs(t){t.forEach(e=>{let[r]=Nr(e);if(!this.graph.nodes[r])throw new Error(`The output '${e}' is not found in the graph`)})}}class e6{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}}let n6="?tfjs-format=file",r6="model.json";class I${constructor(t,e={}){this.modelUrl=t,this.loadOptions=e,this.version="n/a",e==null&&(this.loadOptions={}),this.resourceManager=new e6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=fd(t,this.loadOptions);else{let e=nw(t,this.loadOptions);if(e.length===0)e.push(fd(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[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 t=await this.handler.load();return this.loadSync(t)}loadSync(t){this.artifacts=t;let e=this.artifacts.modelTopology,r={};this.artifacts.userDefinedMetadata!=null&&(r=this.artifacts.userDefinedMetadata.signature),this.version=`${e.versions.producer}.${e.versions.minConsumer}`;let o=ld(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Gm(w$.Instance.transformGraph(e,r)),this.executor.weightMap=this.convertTensorMapToTensorsMap(o),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null){let s=w$.Instance.transformGraph(t.modelInitializer);this.initializer=new Gm(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(t,e){if(typeof t=="string"){let r=ew(t);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${t}'`);t=r[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}predict(t,e){return this.execute(t,this.outputNodes)}normalizeInputs(t){if(!(t instanceof ot)&&!Array.isArray(t))return t;if(t=Array.isArray(t)?t:[t],t.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${t.length} input tensors.`);return this.inputNodes.reduce((e,r,o)=>(e[r]=t[o],e),{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}execute(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=this.executor.execute(t,e);return r.length>1?r:r[0]}async executeAsync(t,e){t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let r=await this.executor.executeAsync(t,e);return r.length>1?r:r[0]}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,r)=>(e[r]=[t[r]],e),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function o6(n,t={}){if(n==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&(n.load==null&&(n.endsWith("/")||(n=n+"/"),n=`${n}${r6}${n6}`));let e=new I$(n,t);return await e.load(),e}let E$="2.7.0";function s6(n,t){return Um(n,t)}function Um(n,t,e=new Map,r=new Set){if(n==null)return null;if(r.has(n))throw new Error("Circular references are not supported.");if(e.has(n))return e.get(n);let o=t(n);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(cl(n)){let s=Array.isArray(n)?[]:{};r.add(n);for(let c in n){let l=n[c],p=Um(l,t,e,r);s[c]=p}return r.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return e.set(n,o.value),o.value}function i6(n,t=A$){return D$(n,t)}function D$(n,t,e=new Set){let r=n[0];if(e.has(r))throw new Error("Circular references are not supported.");let o=t(n);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(cl(r)){let s=Array.isArray(r)?[]:{};e.add(r);for(let c in r){let l=n.map(f=>f[c]),p=D$(l,t,e);s[c]=p}return e.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return o.value}function A$(n){return n===null?null:cl(n[0])?{value:null,recurse:!0}:{value:n,recurse:!1}}async function F$(n,t){let e=new Map;Um(n,t,e);for(let o of Array.from(e.keys())){let s=e.get(o);if(Ji(s)){let c=await s;e.set(o,c)}}let r=Um(n,t,e);return r}function cl(n){return n!=null&&!ArrayBuffer.isView(n)&&(Array.isArray(n)||typeof n=="object"&&!(n instanceof ot))}function a6(n){return n==null||c6(n)||Array.isArray(n)||typeof n=="object"&&n instanceof ot||gn(n)}function c6(n){return n===null||typeof n!="object"&&typeof n!="function"}function l6(n){return s6(n,u6)}function u6(n){return n instanceof ot?{value:n.clone(),recurse:!1}:cl(n)?{value:null,recurse:!0}:{value:n,recurse:!1}}class R${constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),r=this.get(e);return this.set(e,this.pop()),r}}class qm extends R${constructor(){super(qm.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),r=this.length();for(let o=0;o<r;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}}qm.INITIAL_CAPACITY=32;function P$(n){return new h6(n)}function zst(n){let t=n;return nh(()=>({value:t++,done:!1}))}function nh(n){return new f6(n)}function O$(n,t){return new M$(n,t)}function Wst(n,t,e){return O$(nh(n).take(t),e)}function p6(n,t=_i.FAIL){return new T6(n,t)}class En{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],r=await t.next();for(;!r.done;)e.push(r.value),r=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),r=t(e.value);for(;!e.done&&r;)e=await this.next(),r=t(e.value)}handleErrors(t){return new w6(this,t)}filter(t){return new b6(this,t)}map(t){return new x6(this,t)}mapAsync(t){return new L$(this,t)}serialMapAsync(t){return new L$(this,t).serial()}flatmap(t){return new v6(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new y6(this,t,e)}columnMajorBatch(t,e=!0,r=A$){let o=this.rowMajorBatch(t,e);return o.map(s=>i6(s,r))}concatenate(t,e){return new M$(P$([this,t]),e)}take(t){return t<0||t==null?this:new g6(this,t)}skip(t){return t<0||t==null?this:new m6(this,t)}prefetch(t){return new B$(this,t)}shuffle(t,e){return new k6(this,t,e)}serial(){return new d6(this)}}class h6 extends En{constructor(t){super();this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:l6(t),done:!1}}}class f6 extends En{constructor(t){super();this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}}class d6 extends En{constructor(t){super();this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}}class m6 extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Xt(t.value)}return this.upstream.next()}}class g6 extends En{constructor(t,e){super();this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class y6 extends En{constructor(t,e,r=!0){super();this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}}class b6 extends En{constructor(t,e){super();this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Xt(t.value)}}}class x6 extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=ps(t.value),r=this.transform(t.value),o=ps(r);for(let s of e)ad(s,o)||s.dispose();return{value:r,done:!1}}}class w6 extends En{constructor(t,e){super();this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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(t){if(!this.handler(t))return{value:null,done:!0}}}}class L$ extends En{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=ps(t.value),r=await this.transform(t.value),o=ps(r);for(let s of e)ad(s,o)||s.dispose();return{value:r,done:!1}}}class y1 extends En{constructor(){super();this.outputQueue=new qm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class v6 extends y1{constructor(t,e){super();this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=ps(t.value),r=this.transform(t.value),o=ps(r);this.outputQueue.pushAll(r);for(let s of e)ad(s,o)||s.dispose();return!0}}class M$ extends En{constructor(t,e){super();this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){let t="TODO: fill in upstream of chained summaries";return`${t} -> Chained`}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}}var _i;(function(n){n[n.FAIL=0]="FAIL",n[n.SHORTEST=1]="SHORTEST",n[n.LONGEST=2]="LONGEST"})(_i||(_i={}));class T6 extends En{constructor(t,e=_i.FAIL){super();this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){let t="TODO: fill in upstream of zip summaries";return`{${t}} -> Zip`}async nextState(t){await t;let e=0,r=0;function o(c){if(c instanceof En){let l=c.next();return{value:l.then(p=>(e++,p.done&&r++,p.value)),recurse:!1}}else return{value:null,recurse:!0}}let s=await F$(this.iterators,o);if(e===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case _i.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case _i.SHORTEST:return{value:null,done:!0};case _i.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class B$ extends En{constructor(t,e){super();this.upstream=t,this.bufferSize=e,this.buffer=new R$(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}}class k6 extends B${constructor(t,e,r){super(t,e);this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=Uc(r||sr().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}}class ll{constructor(){this.size=null}batch(t,e=!0){let r=this;_(t>0,()=>`batchSize needs to be positive, but it is
      ${t}`);let o;return this.size===Infinity||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),_r(async()=>(await r.iterator()).columnMajorBatch(t,e,C6),o)}concatenate(t){let e=this,r;return this.size===Infinity||t.size===Infinity?r=Infinity:this.size!=null&&t.size!=null?r=this.size+t.size:r=null,_r(async()=>(await e.iterator()).concatenate(await t.iterator()),r)}filter(t){let e=this,r;return this.size===Infinity?r=Infinity:r=null,_r(async()=>(await e.iterator()).filter(o=>rt(()=>t(o))),r)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return _r(async()=>(await e.iterator()).map(r=>rt(()=>t(r))),this.size)}mapAsync(t){let e=this;return _r(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return _r(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,r;return this.size!=null&&t>0?r=this.size*t:t===0?r=0:this.size!=null&&(t===void 0||t<0)?r=Infinity:r=null,_r(async()=>{let o=nh(async()=>({value:await e.iterator(),done:!1}));return O$(o.take(t))},r)}skip(t){let e=this,r;return this.size!=null&&t>=0&&this.size>=t?r=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?r=0:r=null,_r(async()=>(await e.iterator()).skip(t),r)}shuffle(t,e,r=!0){if(t==null||t<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 (${this.size} elements)`);let o=this,s=Uc(e||sr().toString());return _r(async()=>{let c=s.int32();return r&&(c+=s.int32()),(await o.iterator()).shuffle(t,c.toString())},this.size)}take(t){let e=this,r;return this.size!=null&&this.size>t?r=t:this.size!=null&&this.size<=t?r=this.size:r=null,_r(async()=>(await e.iterator()).take(t),r)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}ll.MAX_BUFFER_SIZE=1e4;function _r(n,t=null){return new class extends ll{constructor(){super(...arguments);this.size=t}async iterator(){return n()}}}function N6(n){return _r(async()=>P$(n),n.length)}function _6(n){if(!cl(n))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(n))for(let e=0;e<n.length;e++)t=t==null?n[e].size:Math.min(t,n[e].size);else if(n instanceof Object)for(let e in n)t=t==null?n[e].size:Math.min(t,n[e].size);return _r(async()=>{let e=await F$(n,r=>{if(r instanceof ll)return{value:r.iterator(),recurse:!1};if(cl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return p6(e,_i.SHORTEST)},t)}function C6(n){if(n===null)return null;let t=n[0];if(a6(t)){let e=S6(n);return{value:e,recurse:!1}}return{value:null,recurse:!0}}function S6(n){if(n.length===0)throw new Error("Can't make a batch of zero elements.");return n[0]instanceof ot?pr(n):un(n)}class z$ extends ll{constructor(t){super();this.input=t}async iterator(){let t=await this.input.iterator(),e=t.decodeUTF8(),r=e.split(`
`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o));return r}}let Hm='"',rh=Symbol("out"),W$=Symbol("field"),jm=Symbol("quote"),b1=Symbol("quoteafterquote"),V$=Symbol("quoteinquote");class G$ extends ll{constructor(t,e){super();this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new z$(t),e||(e={}),this.hasHeader=!(e.hasHeader===!1),this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(_(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&_(t.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 ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),r=Object.keys(e).filter(o=>e[o]>1);if(_(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs)for(let o of Object.keys(this.columnConfigs)){let s=this.fullColumnNames.indexOf(o);if(s===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await this.base.iterator(),e=await t.next();if(e.done)throw new Error("No data was found for CSV parsing.");let r=e.value,o=this.parseRow(r,!1);return o}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),r={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let c=this.fullColumnNames[s],l=this.columnConfigs?this.columnConfigs[c]:null;if(this.configuredColumnsOnly&&!l)continue;{let p=e[s],f=null;if(p==="")if(l&&l.default!==void 0)f=l.default;else{if(l&&(l.required||l.isLabel))throw new Error(`Required column ${c} is empty in this line: ${t}`);f=void 0}else{let m=Number(p);if(isNaN(m))l&&l.dtype==="bool"?f=this.getBoolean(p):f=p;else if(!l||!l.dtype)f=m;else switch(l.dtype){case"float32":f=m;break;case"int32":f=Math.floor(m);break;case"bool":f=this.getBoolean(p);break;default:f=m}}l&&l.isLabel?o[c]=f:r[c]=f}}return Object.keys(o).length===0?r:{xs:r,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let r=[],o=0,s=t.length,c=rh;for(let l=0;l<s;l++)switch(c){case rh:switch(t.charAt(l)){case Hm:o=l+1,c=jm;break;case this.delimiter:if(o=l+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),c=rh;break;default:c=W$,o=l;break}break;case W$:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l)),c=rh,o=l+1;break;default:}break;case jm:switch(t.charAt(l)){case Hm:c=b1;break;default:}break;case b1:switch(t.charAt(l)){case this.delimiter:r.push(t.substring(o,l-1)),c=rh,o=l+1;break;case Hm:c=jm;break;default:c=V$;break}break;case V$:switch(t.charAt(l)){case Hm:c=jm;break;default:}break;default:}if(c===b1?r.push(t.substring(o,s-1)):r.push(t.substring(o)),e&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}}class x1 extends En{constructor(t){super();this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))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=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=!(t.includeSpectrogram===!1),this.includeWaveform=t.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(t={}){if(ct().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let e=new x1(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!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 e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize);return}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,r=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(r.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(r.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],r=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},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(t){let e=t[0].length,r=new Float32Array(t.length*e);return t.forEach((o,s)=>r.set(o,s*e)),r}getTensorFromAudioDataArray(t,e){let r=new Float32Array(G(e));return r.set(t,r.length-t.length),un(r,e)}}class w1 extends En{constructor(t,e){super();if(this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=vr([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-r)/2,c=(1-o)/2,l=s+r,p=o+c;this.cropBox=di([c,s,p,l],[1,4])}else this.cropBox=di([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(ct().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let r=new w1(t,e);return await r.start(),r}async start(){this.webcamConfig.facingMode&&_(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(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=p_(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return rt(()=>{let e=t.toFloat().expandDims(0),r;r=mi.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=r.shape;return r.reshape(o.slice(1))})}async capture(){return(await this.next()).value}stop(){let t=this.stream.getTracks();t.forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class U${}class q$ extends En{split(t){return new $6(this,t)}}class $6 extends q${constructor(t,e){super();this.upstream=t,this.impl=new I6(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class I6 extends y1{constructor(t,e){super();this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let r of e.slice(0,-1))this.outputQueue.push(r);return this.carryover=e[e.length-1],!0}}class E6 extends En{decodeUTF8(){return new D6(this)}}class D6 extends q${constructor(t){super();this.upstream=t,this.impl=new A6(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class A6 extends y1{constructor(t){super();if(this.upstream=t,ct().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=require("string_decoder");this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let r;return ct().get("IS_BROWSER")?r=this.decoder.decode(e,{stream:!0}):r=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(r),!0}}class H$ extends E6{constructor(t,e={}){super();this.file=t,this.options=e,_(t instanceof Uint8Array||(ct().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};let t=new Promise((e,r)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=l=>{let p=s.result;if(p instanceof ArrayBuffer&&(p=new Uint8Array(p)),!(p instanceof Uint8Array))return r(new TypeError("FileReader returned unknown type."));e(p)},s.onabort=l=>r(new Error("Aborted")),s.onerror=l=>r(new Error(l.type));let c=this.file.slice(this.offset,o);s.readAsArrayBuffer(c)}this.offset=o});return{value:await t,done:!1}}}async function F6(n,t={}){let e,r;typeof n=="string"?e=n:(e=n.url,r=R6(n));let o=await LN(e,r);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new H$(s,t)}else throw new Error(o.statusText)}let R6=n=>{let t={method:n.method,headers:n.headers,body:n.body,mode:n.mode,credentials:n.credentials,cache:n.cache,redirect:n.redirect,referrer:n.referrer,integrity:n.integrity};return t};function j$(n){return typeof n=="string"&&n.substr(0,7)==="file://"}class K$ extends U${constructor(t,e={}){super();this.input=t,this.options=e}async iterator(){if(j$(this.input)&&ct().get("IS_NODE")){let t=require("fs");this.input=t.readFileSync(this.input.substr(7))}return new H$(this.input,this.options)}}class X$ extends U${constructor(t,e={}){super();this.url=t,this.fileOptions=e}async iterator(){return j$(this.url)?new K$(this.url,this.fileOptions).iterator():F6(this.url,this.fileOptions)}}function P6(n,t={}){return new G$(new X$(n),t)}function O6(n){let t=nh(n);return _r(async()=>t)}function L6(n){return _r(async()=>{let t=await n();return nh(()=>t.next())})}async function M6(n,t){return w1.create(n,t)}async function B6(n){return x1.create(n)}let Y$="2.7.0";var z6=Object.freeze({__proto__:null,array:N6,Dataset:ll,zip:_6,CSVDataset:G$,TextLineDataset:z$,csv:P6,func:O6,generator:L6,microphone:B6,webcam:M6,FileDataSource:K$,URLDataSource:X$,version_data:Y$});function Ct(n,t){Array.isArray(n)||(n=[n]),n.forEach(e=>{e!=null&&_(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}let W6=tm,V6=Tv,G6=kv,U6=Nv,q6=jd;class H6 extends d{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new h(this,ds())}write(t,e,r){this.firstUse&&(this.firstUse=!1,ct().get("IS_NODE")&&Yc(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));let o={};return this.data.set(o,{values:t,dtype:r,refCount:1}),o}makeTensorInfo(t,e,r){let o;if(e==="string"&&r!=null&&r.length>0&&us(r[0])){let s=r.map(c=>od(c));o=this.write(s,t,e)}else o=this.write(r,t,e);return{dataId:o,shape:t,dtype:e}}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,r,o){this.data.set(t,{values:e,dtype:o,refCount:1})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:r}=this.data.get(t);if(e==="complex64"){let o=this.readSync(r.real.dataId),s=this.readSync(r.imag.dataId);return ws(o,s)}return this.data.get(t).values}bufferSync(t){let e=this.readSync(t.dataId),r=e;if(t.dtype==="string")try{r=e.map(o=>Yu(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Se(t.shape,t.dtype,r)}makeOutput(t,e,r){let o=this.write(t,e,r);return ds().makeTensorFromDataId(o,e,r,this)}disposeData(t){if(this.data.has(t)){let{complexTensorInfos:e}=this.data.get(t);e!=null&&(this.disposeData(e.real.dataId),this.disposeData(e.imag.dataId)),this.data.delete(t)}}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.data.has(e)){let r=this.data.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}async time(t){let e=sr();t();let r=sr()-e;return{kernelMs:r}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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yt=f[ht+bt],xt=f[dt+bt],kt=f[ft+bt],Nt=f[ut+bt],At=yt+(kt-yt)*it,It=xt+(Nt-xt)*it,St=At+(It-At)*E;m[v++]=St}}}return un(m,[s,e,r,p])}resizeBilinearBackprop(t,e,r){Ct([t,e],"resizeBilinearBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=[r&&p>1?s-1:s,r&&f>1?c-1:c],b=[r&&p>1?p-1:p,r&&f>1?f-1:f],v=y[0]/b[0],T=y[1]/b[1],N=this.readSync(t.dataId),S=0;for(let D=0;D<o;D++){let I=D*e.strides[0];for(let P=0;P<p;P++){let E=P*v,L=Math.floor(E),B=Math.min(Math.ceil(E),s-1),q=I+L*e.strides[1],H=I+B*e.strides[1],Z=E-L,J=1-Z;for(let it=0;it<f;it++){let pt=it*T,ht=Math.floor(pt),dt=Math.min(Math.ceil(pt),c-1),ft=pt-ht,ut=1-ft,bt=q+ht*e.strides[2],yt=q+dt*e.strides[2],xt=H+ht*e.strides[2],kt=H+dt*e.strides[2],Nt=J*ut,At=J*ft,It=Z*ut,St=Z*ft;for(let Lt=0;Lt<l;Lt++){let Ht=N[S++];m[bt+Lt]+=Ht*Nt,m[yt+Lt]+=Ht*At,m[xt+Lt]+=Ht*It,m[kt+Lt]+=Ht*St}}}}return jc(m,[o,c,s,l],e.dtype)}resizeNearestNeighbor(t,e,r,o){Ct(t,"resizeNearestNeighbor");let[s,c,l,p]=t.shape,f=this.readSync(t.dataId),m=new Float32Array(s*e*r*p),y=[o&&e>1?c-1:c,o&&r>1?l-1:l],b=[o&&e>1?e-1:e,o&&r>1?r-1:r],v=y[0]/b[0],T=y[1]/b[1],N=0;for(let S=0;S<s;S++){let D=S*t.strides[0];for(let I=0;I<e;I++){let P=v*I,E=Math.min(c-1,o?Math.round(P):Math.floor(P)),L=D+E*t.strides[1];for(let B=0;B<r;B++){let q=T*B,H=Math.min(l-1,o?Math.round(q):Math.floor(q)),Z=L+H*t.strides[2];for(let J=0;J<p;J++){let it=f[Z+J];m[N++]=it}}}}return un(m,[s,e,r,p],t.dtype)}resizeNearestNeighborBackprop(t,e,r){Ct([t,e],"resizeNearestNeighborBackprop");let[o,s,c,l]=e.shape,[,p,f]=t.shape,m=new Float32Array(o*s*c*l),y=this.readSync(t.dataId),b=[r&&p>1?s-1:s,r&&f>1?c-1:c],v=[r&&p>1?p-1:p,r&&f>1?f-1:f],T=b[0]/v[0],N=b[1]/v[1],S=1/T,D=1/N,I=Math.ceil(S)*2+2,P=Math.ceil(D)*2+2;for(let E=0;E<o;E++){let L=E*e.strides[0];for(let B=0;B<s;B++){let q=L+B*e.strides[1],H=Math.floor(B*S),Z=Math.floor(H-I/2);for(let J=0;J<c;J++){let it=q+J*e.strides[2],pt=Math.floor(J*D),ht=Math.floor(pt-P/2);for(let dt=0;dt<l;dt++){let ft=0;for(let ut=0;ut<I;ut++){let bt=ut+Z;if(bt<0||bt>=p)continue;let yt=L+bt*t.strides[1],xt=bt*T,kt=Math.min(s-1,r?Math.round(xt):Math.floor(xt));if(B!==kt)continue;for(let Nt=0;Nt<P;Nt++){let At=Nt+ht;if(At<0||At>=f)continue;let It=yt+At*t.strides[2],St=At*N,Lt=Math.min(c-1,r?Math.round(St):Math.floor(St));J===Lt&&(ft+=y[It+dt])}}m[it+dt]=ft}}}}return jc(m,e.shape,e.dtype)}localResponseNormalization4D(t,e,r,o,s){Ct(t,"localResponseNormalization4D");let c=t.shape[3],l=c-1,p=this.readSync(t.dataId),f=t.size,m=new Float32Array(f);function y(b){let v=b%c,T=b-v+Math.max(0,v-e),N=b-v+Math.min(v+e,l),S=0;for(;T<=N;T++){let D=p[T];S+=D*D}return S}for(let b=0;b<f;b++){let v=y(b),T=p[b]*Math.pow(r+o*v,-s);m[b]=T}return jc(m,t.shape)}LRNGrad(t,e,r,o,s,c,l){Ct(t,"LRNGrad");let p=t.shape[3],f=this.readSync(t.dataId),m=this.readSync(e.dataId),y=this.readSync(r.dataId),b=new Float32Array(t.size),v=t.size;for(let T=0;T<v;T++){let N=T%p,S=T-N+Math.max(0,N-o),D=T-N+Math.min(p,N+o+1),I=0;for(let P=S;P<D;P++)I+=Math.pow(m[P],2);I=c*I+s;for(let P=S;P<D;P++){let E=-2*c*l*m[P]*y[T]/I;T===P&&(E+=Math.pow(I,-l)),E*=f[T],b[P]+=E}}return jc(b,t.shape)}multinomial(t,e,r,o){Ct(t,"multinomial");let s=e?t:da(t),c=s.shape[0],l=s.shape[1],p=xe([c,r],"int32"),f=this.readSync(p.dataId),m=this.readSync(s.dataId);for(let y=0;y<c;++y){let b=y*l,v=new Float32Array(l-1);v[0]=m[b];for(let S=1;S<v.length;++S)v[S]=v[S-1]+m[b+S];let T=Uc(o.toString()),N=y*r;for(let S=0;S<r;++S){let D=T();f[N+S]=v.length;for(let I=0;I<v.length;I++)if(D<v[I]){f[N+S]=I;break}}}return p}oneHot(t,e,r,o){Ct(t,"oneHot");let s=new Float32Array(t.size*e);s.fill(o);let c=this.readSync(t.dataId);for(let l=0;l<t.size;++l)c[l]>=0&&c[l]<e&&(s[l*e+c[l]]=r);return di(s,[t.size,e],"int32")}nonMaxSuppression(t,e,r,o,s){Ct(t,"nonMaxSuppression");let c=this.readSync(t.dataId),l=this.readSync(e.dataId);return W6(c,l,r,o,s)}depthToSpace(t,e,r){_(r==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${r}`),_(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`);let o=t.shape[0],s=t.shape[1],c=t.shape[2],l=t.shape[3],p=s*e,f=c*e,m=l/(e*e),y=this.readSync(t.dataId),b=new Float32Array(o*p*f*m),v=0;for(let T=0;T<o;++T)for(let N=0;N<p;++N){let S=Math.floor(N/e),D=N%e;for(let I=0;I<f;++I){let P=Math.floor(I/e),E=I%e,L=(D*e+E)*m;for(let B=0;B<m;++B){let q=B+L,H=q+l*(P+c*(S+s*T));b[v++]=y[H]}}}return jc(b,[o,p,f,m])}broadcastedBinaryOp(t,e,r,o){let s=le(t.shape,e.shape),c=Se(s,r),l=this.readSync(t.dataId),p=this.readSync(e.dataId),f=la(t.shape,s),m=la(e.shape,s),y=c.values;if(f.length+m.length===0)for(let b=0;b<y.length;++b)y[b]=o(l[b%l.length],p[b%p.length]);else{let b=this.bufferSync(t),v=this.bufferSync(e);for(let T=0;T<y.length;++T){let N=c.indexToLoc(T),S=N.slice(-t.rank);f.forEach(E=>S[E]=0);let D=b.locToIndex(S),I=N.slice(-e.rank);m.forEach(E=>I[E]=0);let P=v.locToIndex(I);y[T]=o(l[D],p[P])}}return c.toTensor()}split(t,e,r){return V6(t,e,r)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}cropAndResize(t,e,r,o,s,c){let[l,p,f,m]=t.shape,y=e.shape[0],[b,v]=o,T=Se([y,b,v,m],"float32"),N=this.readSync(e.dataId),S=this.readSync(r.dataId),D=this.readSync(t.dataId),I=t.strides,P=T.strides;for(let E=0;E<y;E++){let L=E*4,B=N[L],q=N[L+1],H=N[L+2],Z=N[L+3],J=S[E];if(J>=l)continue;let it=b>1?(H-B)*(p-1)/(b-1):0,pt=v>1?(Z-q)*(f-1)/(v-1):0;for(let ht=0;ht<b;ht++){let dt=b>1?B*(p-1)+ht*it:.5*(B+H)*(p-1);if(dt<0||dt>p-1){for(let ft=0;ft<v;ft++)for(let ut=0;ut<m;ut++){let bt=ut+ft*P[2]+ht*P[1]+E*P[0];T.values[bt]=c}continue}if(s==="bilinear"){let ft=Math.floor(dt),ut=Math.ceil(dt),bt=dt-ft;for(let yt=0;yt<v;yt++){let xt=v>1?q*(f-1)+yt*pt:.5*(q+Z)*(f-1);if(xt<0||xt>f-1){for(let It=0;It<m;It++){let St=It+yt*P[2]+ht*P[1]+E*P[0];T.values[St]=c}continue}let kt=Math.floor(xt),Nt=Math.ceil(xt),At=xt-kt;for(let It=0;It<m;It++){let St=It+kt*I[2]+ft*I[1]+J*I[0],Lt=D[St];St=It+Nt*I[2]+ft*I[1]+J*I[0];let Ht=D[St];St=It+kt*I[2]+ut*I[1]+J*I[0];let oe=D[St];St=It+Nt*I[2]+ut*I[1]+J*I[0];let de=D[St],ie=Lt+(Ht-Lt)*At,we=oe+(de-oe)*At;St=It+yt*P[2]+ht*P[1]+E*P[0],T.values[St]=ie+(we-ie)*bt}}}else for(let ft=0;ft<v;++ft){let ut=v>1?q*(f-1)+ft*pt:.5*(q+Z)*(f-1);if(ut<0||ut>f-1){for(let xt=0;xt<m;xt++){let kt=xt+ft*P[2]+ht*P[1]+E*P[0];T.values[kt]=c}continue}let bt=Math.round(ut),yt=Math.round(dt);for(let xt=0;xt<m;xt++){let kt=xt+bt*I[2]+yt*I[1]+J*I[0],Nt=xt+ft*P[2]+ht*P[1]+E*P[0];T.values[Nt]=D[kt]}}}}return T.toTensor()}sparseToDense(t,e,r,o){let{sliceRank:s,numUpdates:c,sliceSize:l,strides:p,outputSize:f}=Fc(e,t,r),m=!1;return this.scatter(t,e,r,f,l,c,s,p,o,m)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=dd(t,e);if(c===0)return un([],s,t.dtype);let f=new hn([c,l],t.dtype),m=this.readSync(e.dataId),y=this.readSync(t.dataId);for(let b=0;b<c;b++){let v=[],T=0;for(let N=0;N<o;N++){let S=m[b*o+N];T+=S*p[N],v.push(S)}if(T<0||T>=t.size/l)throw new Error(`Invalid indices: ${v} does not 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Co(n){let{inputs:t,backend:e}=n,{real:r,imag:o}=t,s=e.data.get(r.dataId).values,c=e.data.get(o.dataId).values,l=e.makeTensorInfo(r.shape,"complex64"),p=e.data.get(l.dataId);return p.complexTensorInfos={real:e.makeTensorInfo(r.shape,"float32",s),imag:e.makeTensorInfo(o.shape,"float32",c)},l}let X6={kernelName:Sf,backendName:"cpu",kernelFunc:Co};function Na(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let Y6={kernelName:Nu,backendName:"cpu",kernelFunc:Na};function oh(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.data.get(r.dataId).complexTensorInfos.real,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}let J6={kernelName:Hf,backendName:"cpu",kernelFunc:oh};function sh(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{dtype:s}=r;if(s==="complex64"){if(o.dtype==="complex64")return Na({inputs:{x:o},backend:e});let 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rI=pl(n=>Math.log(n)),lK=hl($u,rI),uK={kernelName:$u,backendName:"cpu",kernelFunc:lK};function oI(n,t,e,r){let o=Ce(r,G(e));for(let s=0;s<o.length;++s){let c=s*t,l=n[c];for(let p=0;p<t;++p){let f=n[c+p];f>l&&(l=f)}o[s]=l}return o}let sI=Ci((n,t)=>n*t),pK=v1((n,t,e,r)=>({real:n*e-t*r,imag:n*r+t*e})),iI=ul(Tc,sI,pK),hK={kernelName:Tc,backendName:"cpu",kernelFunc:iI};let aI=Ci((n,t)=>n!==t?1:0),fK=ul(Fu,aI,null,"bool"),dK={kernelName:Fu,backendName:"cpu",kernelFunc:fK};let cI=pl(n=>1/Math.sqrt(n)),mK=hl(Bu,cI),gK={kernelName:Bu,backendName:"cpu",kernelFunc:mK};function lI(n,t,e,r,o){let s=fw(r,t,e),c=G(e),l=Jt(r);if(s){let f=dw(t,l);return n.subarray(f,f+c)}let p=Ce(o,c);for(let f=0;f<c;++f){let m=e.length,y=Jt(e),b=Yi(f,m,y),v=b.map((N,S)=>N+t[S]),T=Yr(v,r.length,l);p[f]=n[T]}return p}function T1(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{begin:s,size:c}=r;Ct(o,"slice");let[l,p]=yd(o,s,c);hw(o,l,p);let f=e.data.get(o.dataId).values,m=lI(f,l,p,o.shape,o.dtype);return 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y=s.slice();y[1]=Object.keys(c).length;let b=new hn(y,r);f.forEach((T,N)=>{for(let S=0;S<s[0];S++)for(let D=0;D<s[2];D++)b.set(p.get(S,T,D),S,N,D)});let v=e.slice();return v[o]=y[1],{outputValues:b.values,outputShape:v,indices:l}}var TK=Object.freeze({__proto__:null,simpleAbsImpl:J$,addImpl:Z$,ceilImpl:Q$,expImpl:tI,expm1Impl:eI,floorImpl:nI,logImpl:rI,maxImpl:oI,multiplyImpl:sI,notEqualImpl:aI,rsqrtImpl:cI,sliceImpl:lI,squaredDifferenceImpl:uI,subImpl:pI,transposeImpl:k1,uniqueImpl:fI});let kK="2.7.0";bw("cpu",()=>new H6,1);let dI=Ee(bu,n=>n>=0?n:Math.exp(n)-1),NK={kernelName:bu,backendName:"cpu",kernelFunc:dI};let _K=Ci((n,t)=>n<0?t*n:n);function mI(n){let{inputs:t,backend:e}=n,{x:r,alpha:o}=t;Ct([r,o],"prelu");let s=e.data.get(r.dataId).values,c=e.data.get(o.dataId).values,[l,p]=_K(r.shape,o.shape,s,c,r.dtype);return e.makeTensorInfo(p,r.dtype,l)}let CK={kernelName:qf,backendName:"cpu",kernelFunc:mI};let gI=Ee(Pu,n=>Math.max(0,n)),SK={kernelName:Pu,backendName:"cpu",kernelFunc:gI};let yI=Ee(Lu,n=>Math.min(Math.max(0,n),6)),$K={kernelName:Lu,backendName:"cpu",kernelFunc:yI};function N1(n,t,e,r){if(e==="linear")return Na({inputs:{x:t},backend:n});if(e==="relu")return gI({inputs:{x:t},backend:n});if(e==="elu")return dI({inputs:{x:t},backend:n});if(e==="relu6")return yI({inputs:{x:t},backend:n});if(e==="prelu")return mI({inputs:{x:t,alpha:r},backend:n});throw new Error(`Activation ${e} has not been implemented for the CPU backend.`)}function Ko(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=G(o.shape),l=Ue(s,c),p=G(l);_(c===p,()=>`The new shape (${l}) has ${p} elements and the old shape (${o.shape}) has ${c} elements. 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bt=(ht+ut)/y;if(bt<0||bt>=m.outHeight||Math.floor(bt)!==bt)continue;for(let yt=0;yt<I;yt+=S){let xt=(dt+yt)/b;if(xt<0||xt>=m.outWidth||Math.floor(xt)!==xt)continue;let kt=H.get(Z,bt,xt,J);ft+=kt}}L.set(ft*B,Z,it,pt,J)}return e.makeTensorInfo(L.shape,L.dtype,L.values)}let KK={kernelName:_f,backendName:"cpu",kernelFunc:jK};function XK(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,scale:s,offset:c,mean:l,variance:p}=t;_(l.shape.length===p.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||l.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(s==null||l.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ct([o,l,p,s,c],"batchNorm");let{varianceEpsilon:f}=r;f==null&&(f=.001);let m=e.data.get(o.dataId).values,y=e.data.get(l.dataId).values,b=e.data.get(p.dataId).values,v=s?e.data.get(s.dataId).values:new 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p=l.map(v=>v.shape);if(Nd(p,s),l[0].dtype==="complex64"){let v=l.map(I=>oh({inputs:{input:I},backend:e})),T=l.map(I=>Km({inputs:{input:I},backend:e})),N=ah({inputs:v,backend:e,attrs:{axis:s}}),S=ah({inputs:T,backend:e,attrs:{axis:s}}),D=Co({inputs:{real:N,imag:S},backend:e});return v.forEach(I=>e.disposeIntermediateTensorInfo(I)),T.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(N),e.disposeIntermediateTensorInfo(S),D}let f=l.map(v=>{let T=G(v.shape.slice(s)),N=[-1,T];return Ko({inputs:{x:v},backend:e,attrs:{shape:N}})});c=ms(f.map(v=>v.shape),1);let m=Ce(l[0].dtype,G(c));if(f[0].shape[0]===1){let v=0;f.forEach(T=>{let N=e.data.get(T.dataId).values,S=G(T.shape);m.set(N,v),v+=S})}else{let v=0;f.forEach(T=>{let N=e.data.get(T.dataId).values,S=0;for(let D=0;D<T.shape[0];++D){let I=D*c[1]+v;for(let P=0;P<T.shape[1];++P)m[I+P]=N[S++]}v+=T.shape[1]})}let y=ms(l.map(v=>v.shape),s),b=e.makeTensorInfo(y,t[0].dtype,m);return f.forEach(v=>e.disposeIntermediateTensorInfo(v)),b}let t5={kernelName:gu,backendName:"cpu",kernelFunc:ah};function wI(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dataFormat:p,dilations:f,dimRoundingMode:m}=r;Ct([o,s],"conv2d");let y=li(p),b=Un(o.shape,s.shape,c,f,l,m,!1,y),v=b.filterHeight,T=b.filterWidth,N=b.dilationHeight,S=b.dilationWidth,D=b.padInfo.left,I=b.padInfo.top,P=b.dataFormat==="channelsLast",E=new hn(b.outShape,o.dtype),L=Jt(o.shape),B=Jt(s.shape),q=L[0],H=P?L[1]:L[2],Z=P?L[2]:1,J=P?1:L[1],it=E.strides[0],pt=P?E.strides[1]:E.strides[2],ht=P?E.strides[2]:1,dt=P?1:E.strides[1],ft=e.data.get(o.dataId).values,ut=e.data.get(s.dataId).values,bt=E.values;for(let yt=0;yt<b.batchSize;++yt){let xt=yt*q,kt=yt*it;for(let Nt=0;Nt<b.outHeight;++Nt){let At=kt+Nt*pt,It=Nt*b.strideHeight-I;for(let St=0;St<v;++St){let Lt=It+St*N;if(Lt<0||Lt>=b.inHeight)continue;let Ht=St*B[0],oe=xt+Lt*H;for(let de=0;de<b.outWidth;++de){let ie=At+de*ht,we=de*b.strideWidth-D;for(let pe=0;pe<T;++pe){let Xe=we+pe*S;if(Xe<0||Xe>=b.inWidth)continue;let on=Ht+pe*B[1],sn=oe+Xe*Z,Fe=on;for(let Ye=0;Ye<b.inChannels;++Ye){let Le=ft[sn+Ye*J];for(let Mn=0;Mn<b.outChannels;++Mn)bt[ie+Mn*dt]+=Le*ut[Fe+Mn];Fe+=b.outChannels}}}}}}return e.makeTensorInfo(E.shape,E.dtype,bt)}let e5={kernelName:$f,backendName:"cpu",kernelFunc:wI};function n5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,dataFormat:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"conv2dBackpropFilter");let y=li(p),b=Un(o.shape,m,c,1,l,f,!1,y),{strideHeight:v,strideWidth:T,filterHeight:N,filterWidth:S}=b,D=b.dataFormat==="channelsLast",I=new hn(b.filterShape,"float32"),P=b.padInfo.left,E=b.padInfo.top,L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=new hn(o.shape,o.dtype,L),H=new hn(s.shape,s.dtype,B);for(let Z=0;Z<N;++Z){let J=Math.max(0,Math.ceil((E-Z)/v)),it=Math.min(b.outHeight,(b.inHeight+E-Z)/v);for(let pt=0;pt<S;++pt){let ht=Math.max(0,Math.ceil((P-pt)/T)),dt=Math.min(b.outWidth,(b.inWidth+P-pt)/T);for(let ft=0;ft<b.inChannels;++ft)for(let ut=0;ut<b.outChannels;++ut){let bt=0;for(let yt=0;yt<b.batchSize;++yt)for(let xt=J;xt<it;++xt){let kt=Z+xt*v-E;for(let Nt=ht;Nt<dt;++Nt){let At=pt+Nt*T-P;D?bt+=q.get(yt,kt,At,ft)*H.get(yt,xt,Nt,ut):bt+=q.get(yt,ft,kt,At)*H.get(yt,ut,xt,Nt)}}I.set(bt,Z,pt,ft,ut)}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let r5={kernelName:lx,backendName:"cpu",kernelFunc:n5};function o5(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{inputShape:c,strides:l,pad:p,dataFormat:f,dimRoundingMode:m}=r;Ct([o,s],"conv2dBackpropInput");let y=Jt(s.shape),b=Jt(o.shape),v=li(f),T=Un(c,s.shape,l,1,p,m,!1,v),N=new hn(T.inShape,"float32"),S=N.values,D=e.data.get(o.dataId).values,I=e.data.get(s.dataId).values,[P,E,L]=y,{batchSize:B,filterHeight:q,filterWidth:H,inChannels:Z,inHeight:J,inWidth:it,outChannels:pt,outHeight:ht,outWidth:dt,strideHeight:ft,strideWidth:ut}=T;v=T.dataFormat;let bt=q-1-T.padInfo.top,yt=H-1-T.padInfo.left,xt=v==="channelsLast",kt=N.strides[0],Nt=xt?N.strides[1]:N.strides[2],At=xt?N.strides[2]:1,It=xt?1:N.strides[1],St=b[0],Lt=xt?b[1]:b[2],Ht=xt?b[2]:1,oe=xt?1:b[1];for(let de=0;de<B;++de)for(let ie=0;ie<Z;++ie)for(let we=0;we<J;++we){let pe=we-bt,Xe=Math.max(0,Math.ceil(pe/ft)),on=Math.min(ht,(q+pe)/ft);for(let sn=0;sn<it;++sn){let Fe=sn-yt,Ye=Math.max(0,Math.ceil(Fe/ut)),Le=Math.min(dt,(H+Fe)/ut),Mn=0;for(let Bn=Xe;Bn<on;++Bn){let So=Bn*ft-pe;for(let zr=Ye;zr<Le;++zr){let Sa=zr*ut-Fe,$o=St*de+Lt*Bn+Ht*zr,Yo=P*(q-1-So)+E*(H-1-Sa)+L*ie;for(let $i=0;$i<pt;++$i){let Ii=D[$o+oe*$i],Ei=I[Yo+$i];Mn+=Ii*Ei}}}let Ds=kt*de+Nt*we+At*sn+It*ie;S[Ds]=Mn}}return e.makeTensorInfo(N.shape,N.dtype,N.values)}let s5={kernelName:If,backendName:"cpu",kernelFunc:o5};function i5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p}=r;Ct([o,s],"conv3d");let f=ai(o.shape,s.shape,c,p,l),{filterDepth:m,filterHeight:y,filterWidth:b,dilationDepth:v,dilationHeight:T,dilationWidth:N,padInfo:S}=f,D=S.front,I=S.left,P=S.top,E=new hn(f.outShape,o.dtype),L=e.data.get(o.dataId).values,B=e.data.get(s.dataId).values,q=E.values,H=Jt(o.shape),Z=Jt(s.shape);for(let J=0;J<f.batchSize;++J){let it=J*H[0],pt=J*E.strides[0];for(let ht=0;ht<f.outDepth;++ht){let dt=pt+ht*E.strides[1],ft=ht*f.strideDepth-D;for(let ut=0;ut<m;++ut){let bt=ft+ut*v;if(bt<0||bt>=f.inDepth)continue;let yt=ut*Z[0],xt=it+bt*H[1];for(let kt=0;kt<f.outHeight;++kt){let Nt=dt+kt*E.strides[2],At=kt*f.strideHeight-P;for(let It=0;It<y;++It){let St=At+It*T;if(St<0||St>=f.inHeight)continue;let Lt=yt+It*Z[1],Ht=xt+St*H[2];for(let oe=0;oe<f.outWidth;++oe){let de=Nt+oe*f.outChannels,ie=oe*f.strideWidth-I;for(let we=0;we<b;++we){let pe=ie+we*N;if(pe<0||pe>=f.inWidth)continue;let Xe=Lt+we*Z[2],on=Ht+pe*f.inChannels,sn=Xe;for(let Fe=0;Fe<f.inChannels;++Fe){let Ye=L[on+Fe];for(let Le=0;Le<f.outChannels;++Le)q[de+Le]+=Ye*B[sn+Le];sn+=f.outChannels}}}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}let a5={kernelName:Ef,backendName:"cpu",kernelFunc:i5};function c5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,pad:l,filterShape:p}=r;Ct([o,s],"conv3dBackpropFilterV2");let f=Jt(o.shape),m=Jt(s.shape),y=ai(o.shape,p,c,1,l),b=y.strideDepth,v=y.strideHeight,T=y.strideWidth,N=y.filterDepth,S=y.filterHeight,D=y.filterWidth,I=new hn(y.filterShape,"float32"),P=I.values,[E,L,B,q]=I.strides,H=e.data.get(s.dataId).values,[Z,J,it,pt]=m,ht=e.data.get(o.dataId).values,[dt,ft,ut,bt]=f,yt=y.padInfo.front,xt=y.padInfo.left,kt=y.padInfo.top;for(let Nt=0;Nt<N;++Nt){let At=Math.max(0,Math.ceil((yt-Nt)/b)),It=Math.min(y.outDepth,(y.inDepth+yt-Nt)/b),St=Nt*E;for(let Lt=0;Lt<S;++Lt){let Ht=Math.max(0,Math.ceil((kt-Lt)/v)),oe=Math.min(y.outHeight,(y.inHeight+kt-Lt)/v),de=Lt*L+St;for(let ie=0;ie<D;++ie){let we=Math.max(0,Math.ceil((xt-ie)/T)),pe=Math.min(y.outWidth,(y.inWidth+xt-ie)/T),Xe=ie*B+de;for(let on=0;on<y.inChannels;++on){let sn=on*q+Xe;for(let Fe=0;Fe<y.outChannels;++Fe){let Ye=0;for(let Le=0;Le<y.batchSize;++Le){let Mn=Le*dt,Ds=Le*Z;for(let Bn=At;Bn<It;++Bn){let So=Nt+Bn*b-yt,zr=So*ft+Mn,Sa=Bn*J+Ds;for(let $o=Ht;$o<oe;++$o){let Yo=Lt+$o*v-kt,$i=Yo*ut+zr,Ii=$o*it+Sa;for(let Ei=we;Ei<pe;++Ei){let Nl=ie+Ei*T-xt,V1=Nl*bt+$i,G1=Ei*pt+Ii;Ye+=ht[V1+on]*H[G1+Fe]}}}}P[sn+Fe]=Ye}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}let l5={kernelName:ux,backendName:"cpu",kernelFunc:c5};function u5(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{pad:c,strides:l,inputShape:p}=r;Ct([o],"conv3dBackpropInputV2");let f=Jt(o.shape),m=Jt(s.shape),y=ai(p,s.shape,l,1,c),b=new hn(y.inShape,"float32"),v=b.values,[T,N,S,D]=b.strides,I=e.data.get(o.dataId).values,[P,E,L,B]=f,q=e.data.get(s.dataId).values,[H,Z,J,it]=m,{batchSize:pt,filterDepth:ht,filterHeight:dt,filterWidth:ft,inChannels:ut,inDepth:bt,inHeight:yt,inWidth:xt,outChannels:kt,outDepth:Nt,outHeight:At,outWidth:It,strideDepth:St,strideHeight:Lt,strideWidth:Ht}=y,oe=ht-1-y.padInfo.front,de=dt-1-y.padInfo.top,ie=ft-1-y.padInfo.left;for(let we=0;we<pt;++we)for(let pe=0;pe<ut;++pe)for(let Xe=0;Xe<bt;++Xe){let on=Xe-oe,sn=Math.max(0,Math.ceil(on/St)),Fe=Math.min(Nt,(ht+on)/St);for(let Ye=0;Ye<yt;++Ye){let Le=Ye-de,Mn=Math.max(0,Math.ceil(Le/Lt)),Ds=Math.min(At,(dt+Le)/Lt);for(let Bn=0;Bn<xt;++Bn){let So=Bn-ie,zr=Math.max(0,Math.ceil(So/Ht)),Sa=Math.min(It,(ft+So)/Ht),$o=0;for(let Yo=sn;Yo<Fe;++Yo){let $i=Yo*St-on;for(let Ii=Mn;Ii<Ds;++Ii){let Ei=Ii*Lt-Le;for(let Nl=zr;Nl<Sa;++Nl){let V1=Nl*Ht-So,G1=P*we+E*Yo+L*Ii+B*Nl,cQ=H*(ht-1-$i)+Z*(dt-1-Ei)+J*(ft-1-V1)+it*pe;for(let cg=0;cg<kt;++cg){let lQ=I[G1+cg],uQ=q[cQ+cg];$o+=lQ*uQ}}}}v[T*we+N*Xe+S*Ye+D*Bn+pe]=$o}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}let p5={kernelName:px,backendName:"cpu",kernelFunc:u5};let h5=Ee(wc,n=>Math.cos(n)),f5={kernelName:wc,backendName:"cpu",kernelFunc:h5};let d5=Ee(yu,n=>Math.cosh(n)),m5={kernelName:yu,backendName:"cpu",kernelFunc:d5};function vI(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,filter:s}=t,{strides:c,pad:l,dilations:p,dimRoundingMode:f}=r;Ct([o,s],"depthwiseConv2DNative");let m=Jt(o.shape),y=Jt(s.shape),b=p;b==null&&(b=[1,1]),_(fn(c,b),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${c} and dilations '${b}'`);let v=Un(o.shape,s.shape,c,b,l,f,!0),{filterHeight:T,filterWidth:N,dilationHeight:S,dilationWidth:D,padInfo:I}=v,P=I.left,E=I.top,L=v.outChannels/v.inChannels,B=new hn(v.outShape,o.dtype),q=e.data.get(o.dataId).values,H=e.data.get(s.dataId).values,Z=B.values;for(let J=0;J<v.batchSize;++J){let it=J*m[0],pt=J*B.strides[0];for(let ht=0;ht<v.outHeight;++ht){let dt=pt+ht*B.strides[1],ft=ht*v.strideHeight-P;for(let ut=0;ut<T;++ut){let bt=ft+ut*S;if(bt<0||bt>=v.inHeight)continue;let yt=ut*y[0],xt=it+bt*m[1];for(let kt=0;kt<v.outWidth;++kt){let Nt=dt+kt*B.strides[2],At=kt*v.strideWidth-E;for(let It=0;It<N;++It){let St=At+It*D;if(St<0||St>=v.inWidth)continue;let Lt=yt+It*y[1],Ht=xt+St*v.inChannels,oe=Nt,de=Lt;for(let ie=0;ie<v.inChannels;++ie){let we=q[Ht+ie];for(let pe=0;pe<L;++pe)Z[oe+pe]+=we*H[de+pe];oe+=L,de+=L}}}}}}return e.makeTensorInfo(B.shape,B.dtype,B.values)}let g5={kernelName:Df,backendName:"cpu",kernelFunc:vI};function y5(n){let{inputs:t,backend:e,attrs:r}=n,{x:o,dy:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,filterShape:m}=r;Ct([o,s],"depthwiseConv2dNativeBackpropFilter");let y=Un(o.shape,m,c,l,p,f,!0),{strideHeight:b,strideWidth:v,filterHeight:T,filterWidth:N}=y,S=new hn(y.filterShape,"float32"),D=y.padInfo.left,I=y.padInfo.top,P=y.outChannels/y.inChannels,E=e.data.get(o.dataId).values,L=new hn(o.shape,o.dtype,E),B=e.data.get(s.dataId).values,q=new hn(s.shape,s.dtype,B);for(let H=0;H<T;++H){let Z=Math.max(0,Math.ceil((I-H)/b)),J=Math.min(y.outHeight,(y.inHeight+I-H)/b);for(let it=0;it<N;++it){let pt=Math.max(0,Math.ceil((D-it)/v)),ht=Math.min(y.outWidth,(y.inWidth+D-it)/v);for(let dt=0;dt<y.outChannels;++dt){let ft=Math.trunc(dt/P),ut=dt%P,bt=0;for(let yt=0;yt<y.batchSize;++yt)for(let xt=Z;xt<J;++xt){let kt=H+xt*b-I;for(let Nt=pt;Nt<ht;++Nt){let At=it+Nt*v-D;bt+=L.get(yt,kt,At,ft)*q.get(yt,xt,Nt,dt)}}S.set(bt,H,it,ft,ut)}}}return e.makeTensorInfo(S.shape,S.dtype,S.values)}let b5={kernelName:fx,backendName:"cpu",kernelFunc:y5};function x5(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,filter:s}=t,{strides:c,dilations:l,pad:p,dimRoundingMode:f,inputShape:m}=r;Ct([o,s],"depthwiseConv2DNativeBackpropInput");let y=Jt(o.shape),b=Jt(s.shape),v=Un(m,s.shape,c,l,p,f,!0),T=new hn(v.inShape,"float32"),N=T.values,[S,D,I]=T.strides,P=e.data.get(o.dataId).values,[E,L,B]=y,q=e.data.get(s.dataId).values,[H,Z,J]=b,{batchSize:it,filterHeight:pt,filterWidth:ht,inChannels:dt,inHeight:ft,inWidth:ut,outChannels:bt,outHeight:yt,outWidth:xt,strideHeight:kt,strideWidth:Nt}=v,At=pt-1-v.padInfo.top,It=ht-1-v.padInfo.left,St=bt/dt;for(let Lt=0;Lt<it;++Lt)for(let Ht=0;Ht<dt;++Ht)for(let oe=0;oe<ft;++oe){let de=oe-At,ie=Math.max(0,Math.ceil(de/kt)),we=Math.min(yt,(pt+de)/kt);for(let pe=0;pe<ut;++pe){let Xe=pe-It,on=Math.max(0,Math.ceil(Xe/Nt)),sn=Math.min(xt,(ht+Xe)/Nt),Fe=0;for(let Ye=ie;Ye<we;++Ye){let Le=Ye*kt-de;for(let Mn=on;Mn<sn;++Mn){let Ds=Mn*Nt-Xe,Bn=E*Lt+L*Ye+B*Mn,So=H*(pt-1-Le)+Z*(ht-1-Ds)+J*Ht;for(let zr=0;zr<St;++zr){let Sa=Ht*St+zr,$o=P[Bn+Sa],Yo=q[So+zr];Fe+=$o*Yo}}}N[S*Lt+D*oe+I*pe+Ht]=Fe}}return e.makeTensorInfo(T.shape,T.dtype,T.values)}let w5={kernelName:dx,backendName:"cpu",kernelFunc:x5};let v5={kernelName:Af,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o}=n,{strides:s,pad:c,dilations:l}=e,p=t,f=p.data.get(r.dataId).values,m=r.shape.length,y=p.data.get(o.dataId).values,b=o.shape.length,{batchSize:v,inHeight:T,inWidth:N,inChannels:S,outHeight:D,outWidth:I,padInfo:P,strideHeight:E,strideWidth:L,filterHeight:B,filterWidth:q,dilationHeight:H,dilationWidth:Z,outShape:J}=Td(r.shape,o.shape,s,c,"NHWC",l),it=G(J),pt=J.length,ht=or(r.dtype,it);for(let ft=0;ft<v;++ft)for(let ut=0;ut<D;++ut){let bt=ut*E-P.top;for(let yt=0;yt<I;++yt){let xt=yt*L-P.left;for(let kt=0;kt<S;++kt){let Nt=Number.MIN_SAFE_INTEGER;for(let It=0;It<B;++It){let St=bt+It*H;if(St>=0&&St<T)for(let Lt=0;Lt<q;++Lt){let Ht=xt+Lt*Z;if(Ht>=0&&Ht<N){let oe=Yr([ft,St,Ht,kt],m,Jt(r.shape)),de=Yr([It,Lt,kt],b,Jt(o.shape)),ie=f[oe]+y[de];ie>Nt&&(Nt=ie)}}}let At=Yr([ft,ut,yt,kt],pt,Jt(J));ht[At]=Nt}}}let dt=p.write(ri(ht,r.dtype),J,r.dtype);return{dataId:dt,shape:J,dtype:r.dtype}}};let T5={kernelName:Rf,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o,dy:s}=n,{strides:c,pad:l,dilations:p}=e,f=t,m=Dr(r.shape,f.data.get(r.dataId).values),y=Dr(o.shape,f.data.get(o.dataId).values),{batchSize:b,inHeight:v,inWidth:T,inChannels:N,outHeight:S,outWidth:D,padInfo:I,strideHeight:P,strideWidth:E,filterHeight:L,filterWidth:B,dilationHeight:q,dilationWidth:H,outShape:Z}=Td(r.shape,o.shape,c,l,"NHWC",p);_(s.rank===Z.length,()=>`Error in ${Rf}, dy must have the same rank as output ${Z.length}, but got ${s.rank}`);let J=Dr(Z,f.data.get(s.dataId).values),it=tx(o.shape,o.dtype);for(let ht=0;ht<b;++ht)for(let dt=0;dt<S;++dt){let ft=dt*P-I.top;for(let ut=0;ut<D;++ut){let bt=ut*E-I.left;for(let yt=0;yt<N;++yt){let xt=Number.MIN_SAFE_INTEGER,kt=0,Nt=0;for(let At=0;At<L;++At){let It=ft+At*q;if(It>=0&&It<v)for(let St=0;St<B;++St){let Lt=bt+St*H;if(Lt>=0&&Lt<T){let Ht=m[ht][It][Lt][yt]+y[At][St][yt];Ht>xt&&(xt=Ht,kt=At,Nt=St)}}}it[kt][Nt][yt]+=J[ht][dt][ut][yt]}}}let pt=f.write(ri(it,r.dtype),o.shape,o.dtype);return{dataId:pt,shape:o.shape,dtype:o.dtype}}};let k5={kernelName:Ff,backendName:"cpu",kernelFunc:({inputs:n,backend:t,attrs:e})=>{let{x:r,filter:o,dy:s}=n,{strides:c,pad:l,dilations:p}=e,f=t,m=Dr(r.shape,f.data.get(r.dataId).values),y=Dr(o.shape,f.data.get(o.dataId).values),{batchSize:b,inHeight:v,inWidth:T,inChannels:N,outHeight:S,outWidth:D,padInfo:I,strideHeight:P,strideWidth:E,filterHeight:L,filterWidth:B,dilationHeight:q,dilationWidth:H,outShape:Z}=Td(r.shape,o.shape,c,l,"NHWC",p);_(s.rank===Z.length,()=>`Error in ${Ff}, dy must have the same rank as output ${Z.length}, but got ${s.rank}`);let J=Dr(Z,f.data.get(s.dataId).values),it=tx(r.shape,r.dtype);for(let ht=0;ht<b;++ht)for(let dt=0;dt<S;++dt){let ft=dt*P-I.top;for(let ut=0;ut<D;++ut){let bt=ut*E-I.left;for(let yt=0;yt<N;++yt){let xt=Number.MIN_SAFE_INTEGER,kt=ft<0?0:ft,Nt=bt<0?0:bt;for(let At=0;At<L;++At){let It=ft+At*q;if(It>=0&&It<v)for(let St=0;St<B;++St){let Lt=bt+St*H;if(Lt>=0&&Lt<T){let Ht=m[ht][It][Lt][yt]+y[At][St][yt];Ht>xt&&(xt=Ht,kt=It,Nt=Lt)}}}it[ht][kt][Nt][yt]+=J[ht][dt][ut][yt]}}}let pt=f.write(ri(it,r.dtype),r.shape,r.dtype);return{dataId:pt,shape:r.shape,dtype:r.dtype}}};let N5=Ci((n,t)=>n/t),_5=ul(vc,N5),C1={kernelName:vc,backendName:"cpu",kernelFunc:_5};let C5=fv,S5=dv,$5=mv,I5=gv,E5=yv,D5=bv,A5=Ee(xu,n=>{let t=Math.sign(n),e=Math.abs(n),r=1/(1+C5*e);return t*(1-((((D5*r+E5)*r+I5)*r+$5)*r+S5)*r*Math.exp(-e*e))}),F5={kernelName:xu,backendName:"cpu",kernelFunc:A5};function TI(n,t,e){let r=n.shape,o=r[0],s=r[1],c=e.data.get(n.dataId),l=c.complexTensorInfos.real,p=c.complexTensorInfos.imag,f=[o,s],m=G(f),y=Ce("float32",m),b=Ce("float32",m);for(let S=0;S<o;S++){let D=T1({inputs:{x:l},backend:e,attrs:{begin:[S,0],size:[1,s]}}),I=T1({inputs:{x:p},backend:e,attrs:{begin:[S,0],size:[1,s]}}),P=Co({inputs:{real:D,imag:I},backend:e}),{real:E,imag:L}=R5(P,t,e),B=ws(E,L);for(let q=0;q<s;q++){let H=xv(B,q);y[S*s+q]=H.real,b[S*s+q]=H.imag}e.disposeIntermediateTensorInfo(D),e.disposeIntermediateTensorInfo(I),e.disposeIntermediateTensorInfo(P)}let v=e.makeTensorInfo(f,"float32",y),T=e.makeTensorInfo(f,"float32",b),N=Co({inputs:{real:v,imag:T},backend:e});return e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),N}function R5(n,t,e){let r=G(n.shape),o=e.data.get(n.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,c=e.data.get(o.complexTensorInfos.imag.dataId).values;if(P5(r)){let l=S1(s,c,r,t,e),p=[n.shape[0],n.shape[1]];if(t){let f=e.makeTensorInfo(p,"float32",l.real),m=e.makeTensorInfo(p,"float32",l.imag),y=e.makeTensorInfo([],"float32",ON(r,"float32")),b=Na({inputs:{x:y},backend:e}),v=C1.kernelFunc({inputs:{a:f,b:y},backend:e}),T=C1.kernelFunc({inputs:{a:m,b},backend:e}),N=e.data.get(v.dataId).values,S=e.data.get(T.dataId).values;return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),e.disposeIntermediateTensorInfo(T),{real:N,imag:S}}return l}else{let l=ws(s,c),p=O5(l,r,t);return CC(p)}}function P5(n){return(n&n-1)===0}function S1(n,t,e,r,o){if(e===1)return{real:n,imag:t};let 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        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:n,attribute:t,varyingVs:e,varyingFs:r,texture2D:o,output:s,defineOutput:c,defineSpecialNaN:l,defineSpecialInf:p,defineRound:f}}function _a(n,t,e="index"){let r=Jt(t);return r.map((o,s)=>{let c=`int ${n[s]} = ${e} / ${o}`,l=s===r.length-1?`int ${n[s+1]} = ${e} - ${n[s]} * ${o}`:`index -= ${n[s]} * ${o}`;return`${c}; ${l};`}).join("")}function eg(n){return n.length===1?`${n[0]}`:`vec${n.length}(${n.join(",")})`}function Jst(n,t){if(n.length!==t.length)throw new Error(`Vectors to be dotted must be of the same length -got ${n.length} and ${t.length}`);let e=[],r=Math.floor(n.length/4),o=n.length%4;for(let s=0;s<r;s++){let c=n.slice(s*4,s*4+4),l=t.slice(s*4,s*4+4);e.push(`${eg(c)}, ${eg(l)}`)}if(o!==0){let s=n.slice(r*4),c=t.slice(r*4);s.length===1&&(s=s.map(l=>`float(${l})`),c=c.map(l=>`float(${l})`)),e.push(`${eg(s)}, ${eg(c)}`)}return e.map((s,c)=>`dot(${s})`).join("+")}function P1(n){let t=Jt(n).map(e=>e.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}let DI=`
  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;
  }
`;let{getBroadcastDims:AI}=vv;function JX(n,t,e,r){let o=[];n.forEach(T=>{let N=G(T.shapeInfo.logicalShape);T.shapeInfo.isUniform?o.push(`uniform float ${T.name}${N>1?`[${N}]`:""};`):(o.push(`uniform sampler2D ${T.name};`),o.push(`uniform int offset${T.name};`))});let s=o.join(`
`),c=n.map(T=>ZX(T,t,r)).join(`
`),l=t.texShape,p=Zn(),f=eY(p),m,y,b=oY(p);t.isPacked?(m=QX(t.logicalShape,l),y=rY(p)):(m=tY(t.logicalShape,l),y=nY(p)),r&&(b+=cY);let v=[b,f,y,s,m,c,e].join(`
`);return v}function gl(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return wY(n);case 1:return TY(n);case 2:return NY(n);case 3:return CY(n);case 4:return $Y(n);case 5:return IY(n);case 6:return EY(n);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function FI(n){let t=n.shapeInfo.logicalShape;switch(t.length){case 0:return xY(n);case 1:return vY(n);case 2:return kY(n);case 3:return _Y(n);default:return SY(n)}}function ZX(n,t,e=!1){let r="";e?r+=FI(n):r+=gl(n);let o=n.shapeInfo.logicalShape,s=t.logicalShape;return o.length<=s.length&&(e?r+=DY(n,t):r+=AY(n,t)),r}function QX(n,t){switch(n.length){case 0:return RI();case 1:return lY(n,t);case 2:return yY(n,t);case 3:return pY(n,t);default:return fY(n,t)}}function tY(n,t){switch(n.length){case 0:return RI();case 1:return uY(n,t);case 2:return bY(n,t);case 3:return hY(n,t);case 4:return dY(n,t);case 5:return mY(n,t);case 6:return gY(n,t);default:throw new Error(`${n.length}-D output sampling is not yet supported`)}}function eY(n){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${n.texture2D}(textureSampler, uv).r;
    }
  `}function nY(n){return`
    void setOutput(float val) {
      ${n.output} = vec4(val, 0, 0, 0);
    }
  `}function rY(n){return`
    void setOutput(vec4 val) {
      ${n.output} = val;
    }
  `}function oY(n){let t=`${n.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${n.varyingFs} vec2 resultUV;
    ${n.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;
    ${n.defineSpecialNaN}
    ${n.defineSpecialInf}
    ${n.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);
    }

    ${sY}
    ${iY}
    ${aY}
  `;return t}let sY=`
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);
}
`,iY=`
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);
}
`,aY=`
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);
}
`,cY=`
  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 RI(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function lY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return e[0]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${e[1]}.0);
      }
    `:e[1]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${e[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      return 2 * (resTexRC.x * ${e[1]} + resTexRC.y);
    }
  `}function uY(n,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 * ${t[1]} + resTexRC.y;
    }
  `}function pY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[2]/2),o=r*Math.ceil(n[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `}function hY(n,t){let e=_a(["r","c","d"],n);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${e}
      return ivec3(r, c, d);
    }
  `}function fY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(n[n.length-1]/2),o=r*Math.ceil(n[n.length-2]/2),s=o,c="",l="b, r, c";for(let p=2;p<n.length-1;p++)s*=n[n.length-p-1],c=`
      int b${p} = index / ${s};
      index -= b${p} * ${s};
    `+c,l=`b${p}, `+l;return`
    ivec${n.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;

      ${c}

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

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

      return ivec${n.length}(${l});
    }
  `}function dY(n,t){let e=_a(["r","c","d","d2"],n);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${e}
      return ivec4(r, c, d, d2);
    }
  `}function mY(n,t){let e=_a(["r","c","d","d2","d3"],n);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${e}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function gY(n,t){let e=_a(["r","c","d","d2","d3","d4"],n);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${e}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function yY(n,t){let e=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(lt(n,t))return`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
      }
    `;let r=Math.ceil(n[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));

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

      return ivec2(r, c);
    }
  `}function bY(n,t){return lt(n,t)?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:n[1]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:n[0]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${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 / ${n[1]};
      int c = index - r * ${n[1]};
      return ivec2(r, c);
    }
  `}function Ca(n){return`offset${n}`}function xY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Zn();return`
    vec4 ${e}() {
      return ${r.texture2D}(${t}, halfCR);
    }
  `}function wY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`float ${e}() {return ${t};}`;let[r,o]=n.shapeInfo.texShape;if(r===1&&o===1)return`
      float ${e}() {
        return sampleTexture(${t}, halfCR);
      }
    `;let[s,c]=n.shapeInfo.texShape,l=Ca(t);return`
    float ${e}() {
      vec2 uv = uvFromFlat(${s}, ${c}, ${l});
      return sampleTexture(${t}, uv);
    }
  `}function vY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),r=n.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=Zn();return`
    vec4 ${e}(int index) {
      vec2 uv = packedUVfrom1D(
        ${o[0]}, ${o[1]}, index);
      return ${s.texture2D}(${t}, uv);
    }
  `}function TY(n){let t=n.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1);if(n.shapeInfo.isUniform)return`
      float ${e}(int index) {
        ${yl(n)}
      }
    `;let r=n.shapeInfo.texShape,o=r[0],s=r[1];if(s===1&&o===1)return`
      float ${e}(int index) {
        return sampleTexture(${t}, halfCR);
      }
    `;let c=Ca(t);return s===1?`
      float ${e}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${c}) + 0.5) / ${o}.0);
        return sampleTexture(${t}, uv);
      }
    `:o===1?`
      float ${e}(int index) {
        vec2 uv = vec2((float(index + ${c}) + 0.5) / ${s}.0, 0.5);
        return sampleTexture(${t}, uv);
      }
    `:`
    float ${e}(int index) {
      vec2 uv = uvFromFlat(${o}, ${s}, index + ${c});
      return sampleTexture(${t}, uv);
    }
  `}function kY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=o[0],c=o[1],l=Zn();if(o!=null&&lt(t,o))return`
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${s}.0);

        return ${l.texture2D}(${e}, uv);
      }
    `;let p=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],f=Math.ceil(t[1]/2);return`
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${f}, ${p[0]}, ${p[1]}, row, col);
      return ${l.texture2D}(${e}, uv);
    }
  `}function NY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape;if(o!=null&&lt(t,o)){let y=o[0],b=o[1];return`
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${b}.0, ${y}.0);
      return sampleTexture(${e}, uv);
    }
  `}let{newShape:s,keptDims:c}=ln(t),l=s;if(l.length<t.length){let y=bl(n,l),b=["row","col"];return`
      ${gl(y)}
      float ${r}(int row, int col) {
        return ${r}(${xl(b,c)});
      }
    `}if(n.shapeInfo.isUniform)return`
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
        ${yl(n)}
      }
    `;let p=o[0],f=o[1],m=Ca(e);return f===1?`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${p}.0);
      return sampleTexture(${e}, uv);
    }
  `:p===1?`
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${m}), vec3(${t[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${f}.0, 0.5);
      return sampleTexture(${e}, uv);
    }
  `:`
  float ${r}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${t[1]} + col + ${m};
    vec2 uv = uvFromFlat(${p}, ${f}, index);
    return sampleTexture(${e}, uv);
  }
`}function _Y(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=n.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(t[0]===1){let y=t.slice(1),b=[1,2],v=bl(n,y),T=["b","row","col"];return`
        ${FI(v)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${xl(T,b)});
        }
      `}let c=s[0],l=s[1],p=Math.ceil(t[2]/2),f=p*Math.ceil(t[1]/2),m=Zn();return`
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${c}, ${l}, ${f}, ${p}, b, row, col);
      return ${m.texture2D}(${e}, uv);
    }
  `}function CY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[1]*t[2],s=t[2],{newShape:c,keptDims:l}=ln(t),p=c;if(p.length<t.length){let T=bl(n,p),N=["row","col","depth"];return`
        ${gl(T)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${xl(N,l)});
        }
      `}if(n.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${o}, ${s}, 1)));
        ${yl(n)}
      }
    `;let f=n.shapeInfo.texShape,m=f[0],y=f[1],b=n.shapeInfo.flatOffset;if(y===o&&b==null)return`
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${s}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${y}.0, ${m}.0);
          return sampleTexture(${e}, uv);
        }
      `;if(y===s&&b==null)return`
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}.0, ${m}.0);
      return sampleTexture(${e}, uv);
    }
  `;let v=Ca(e);return`
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${o} + col * ${s} + depth + ${v};
        vec2 uv = uvFromFlat(${m}, ${y}, index);
        return sampleTexture(${e}, uv);
      }
  `}function SY(n){let t=n.shapeInfo.logicalShape,e=t.length,r=n.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n.shapeInfo.texShape,c=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],l=c[0],p=c[1],f=Math.ceil(t[e-1]/2),m=f*Math.ceil(t[e-2]/2),y="int b, int row, int col",b=`b * ${m} + (row / 2) * ${f} + (col / 2)`;for(let T=2;T<e-1;T++)y=`int b${T}, `+y,m*=t[e-T-1],b=`b${T} * ${m} + `+b;let v=Zn();return`
    vec4 ${o}(${y}) {
      int index = ${b};
      int texR = index / ${p};
      int texC = index - texR * ${p};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${l});
      return ${v.texture2D}(${r}, uv);
    }
  `}function $Y(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[3],s=t[2]*o,c=t[1]*s,{newShape:l,keptDims:p}=ln(t);if(l.length<t.length){let T=bl(n,l),N=["row","col","depth","depth2"];return`
      ${gl(T)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${xl(N,p)});
      }
    `}if(n.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${c}, ${s}, ${o}, 1)));
        ${yl(n)}
      }
    `;let f=n.shapeInfo.flatOffset,m=n.shapeInfo.texShape,y=m[0],b=m[1];if(b===c&&f==null)return`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${s}, ${o}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${b}.0, ${y}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(b===o&&f==null)return`
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t[1]*t[2]}, ${t[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${b}.0, ${y}.0);
        return sampleTexture(${e}, uv);
      }
    `;let v=Ca(e);return`
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${s} +
          depth * ${o} + depth2;
      vec2 uv = uvFromFlat(${y}, ${b}, index + ${v});
      return sampleTexture(${e}, uv);
    }
  `}function IY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,c=t[2]*s,l=t[1]*c,{newShape:p,keptDims:f}=ln(t);if(p.length<t.length){let N=bl(n,p),S=["row","col","depth","depth2","depth3"];return`
      ${gl(N)}
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        return ${r}(${xl(S,f)});
      }
    `}if(n.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${l}, ${c}, ${s}, ${o})) +
          depth3;
        ${yl(n)}
      }
    `;let m=n.shapeInfo.flatOffset,y=n.shapeInfo.texShape,b=y[0],v=y[1];if(v===l&&m==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${c}, ${s}, ${o}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${v}.0, ${b}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(v===o&&m==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${v}.0, ${b}.0);
        return sampleTexture(${e}, uv);
      }
    `;let T=Ca(e);return`
    float ${r}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${l} + col * ${c} + depth * ${s} +
          depth2 * ${o} + depth3 + ${T};
      vec2 uv = uvFromFlat(${b}, ${v}, index);
      return sampleTexture(${e}, uv);
    }
  `}function EY(n){let t=n.shapeInfo.logicalShape,e=n.name,r="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=ln(t);if(o.length<t.length){let S=bl(n,o),D=["row","col","depth","depth2","depth3","depth4"];return`
      ${gl(S)}
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${r}(${xl(D,s)});
      }
    `}let c=t[5],l=t[4]*c,p=t[3]*l,f=t[2]*p,m=t[1]*f;if(n.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${m}, ${f}, ${p}, ${l})) +
          dot(
            vec2(depth3, depth4),
            vec2(${c}, 1)));
        ${yl(n)}
      }
    `;let y=n.shapeInfo.flatOffset,b=n.shapeInfo.texShape,v=b[0],T=b[1];if(T===m&&y==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${f}, ${p}, ${l}, ${c})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${T}.0, ${v}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(T===c&&y==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${T}.0, ${v}.0);
        return sampleTexture(${e}, uv);
      }
    `;let N=Ca(e);return`
    float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${m} + col * ${f} + depth * ${p} +
          depth2 * ${l} + depth3 * ${c} + depth4 + ${N};
      vec2 uv = uvFromFlat(${v}, ${T}, index);
      return sampleTexture(${e}, uv);
    }
  `}function yl(n){let t=n.name,e=G(n.shapeInfo.logicalShape);return e<2?`return ${t};`:`
    for (int i = 0; i < ${e}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function DY(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=n.shapeInfo.logicalShape.length,c=t.logicalShape.length,l=AI(n.shapeInfo.logicalShape,t.logicalShape),p=Oe(c),f=c-s,m,y=["x","y","z","w","u","v"];s===0?m="":c<2&&l.length>=1?m="coords = 0;":m=l.map(I=>`coords.${y[I+f]} = 0;`).join(`
`);let b="";c<2&&s>0?b="coords":b=n.shapeInfo.logicalShape.map((I,P)=>`coords.${y[P+f]}`).join(", ");let v="return outputValue;",T=G(n.shapeInfo.logicalShape),N=T===1,S=G(t.logicalShape),D=S===1;if(s===1&&!N&&!D)v=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(N&&!D)c===1?v=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:v=`
        return vec4(outputValue.x);
      `;else if(l.length){let I=s-2,P=s-1;l.indexOf(I)>-1&&l.indexOf(P)>-1?v="return vec4(outputValue.x);":l.indexOf(I)>-1?v="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":l.indexOf(P)>-1&&(v="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${o}() {
      ${p} coords = getOutputCoords();
      ${m}
      vec4 outputValue = get${r}(${b});
      ${v}
    }
  `}function AY(n,t){let e=n.name,r=e.charAt(0).toUpperCase()+e.slice(1),o="get"+r+"AtOutCoords",s=t.texShape,c=n.shapeInfo.texShape,l=n.shapeInfo.logicalShape.length,p=t.logicalShape.length;if(!n.shapeInfo.isUniform&&l===p&&n.shapeInfo.flatOffset==null&&lt(c,s))return`
      float ${o}() {
        return sampleTexture(${e}, resultUV);
      }
    `;let f=Oe(p),m=AI(n.shapeInfo.logicalShape,t.logicalShape),y=p-l,b,v=["x","y","z","w","u","v"];l===0?b="":p<2&&m.length>=1?b="coords = 0;":b=m.map(N=>`coords.${v[N+y]} = 0;`).join(`
`);let T="";return p<2&&l>0?T="coords":T=n.shapeInfo.logicalShape.map((N,S)=>`coords.${v[S+y]}`).join(", "),`
    float ${o}() {
      ${f} coords = getOutputCoords();
      ${b}
      return get${r}(${T});
    }
  `}function Oe(n){if(n<=1)return"int";if(n===2)return"ivec2";if(n===3)return"ivec3";if(n===4)return"ivec4";if(n===5)return"ivec5";if(n===6)return"ivec6";throw Error(`GPU for rank ${n} is not yet supported`)}function bl(n,t){let e=JSON.parse(JSON.stringify(n));return e.shapeInfo.logicalShape=t,e}function xl(n,t){return t.map(e=>n[e]).join(", ")}class FY{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_(t.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],c=Math.ceil(s/e);this.outputShape=t.slice(0,-1),c>1&&this.outputShape.push(c),o||this.variableNames.push("bestIndicesA");let l=this.outputShape,p=l.length,f=Oe(p),m=Jn("coords",p),y,b;if(c===1){b=p+1;let H=Oe(b);y=`
        ${H} sourceLocR = ${H}(${m.join()}, 0);
        ++${m[p-1]};
        ${H} sourceLocG = ${H}(${m.join()}, 0);
        ++${m[p-2]};
        ${H} sourceLocA = ${H}(${m.join()}, 0);
        --${m[p-1]};
        ${H} sourceLocB = ${H}(${m.join()}, 0);
        --${m[p-2]};`}else b=p,y=`
        ${f} sourceLocR = coords;
        ++${m[p-1]};
        ${f} sourceLocG = coords;
        ++${m[p-2]};
        ${f} sourceLocA = coords;
        --${m[p-1]};
        ${f} sourceLocB = coords;
        --${m[p-2]};`;let v=["x","y","z","w","u","v"].slice(0,b),T="."+v[b-1],N=v.map(H=>"int "+H),S=Jn("sourceLocR",b-1).concat("inIdx.r"),D=Jn("sourceLocG",b-1).concat("inIdx.g"),I=Jn("sourceLocB",b-1).concat("inIdx.b"),P=Jn("sourceLocA",b-1).concat("inIdx.a"),E=r==="max"?"greaterThan":"lessThan",L=o?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${S.join()}),
                             getBestIndicesAChannel(${D.join()}),
                             getBestIndicesAChannel(${I.join()}),
                             getBestIndicesAChannel(${P.join()})));`,B=`vec4(
            getAChannel(${S.join()}),
            hasNextCol ? getAChannel(${D.join()}) : 0.,
            hasNextRow ? getAChannel(${I.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${P.join()}) : 0.)`,q=o?"":`
      float getBestIndicesAChannel(${N.join()}) {
        return getChannel(getBestIndicesA(${v.join()}),
                                          vec2(${v.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${N.join()}) {
        return getChannel(getA(${v.join()}),
                               vec2(${v.slice(-2).join()}));
      }
      ${q}
      void main() {
        ${f} coords = getOutputCoords();
        bool hasNextCol = ${m[p-1]} < ${l[p-1]-1};
        bool hasNextRow = ${m[p-2]} < ${l[p-2]-1};
        ${y}
        ivec4 srcIdx = ivec4(sourceLocR${T}, sourceLocG${T},
          sourceLocB${T}, sourceLocA${T}) * ${e};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${B};

        for (int i = 0; i < ${e}; i++) {
          inIdx = srcIdx;
          ${L}
          vec4 candidate = ${B};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${E}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}}class RY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterHeight,f=t.effectiveFilterWidth,m=p-1-t.padInfo.top,y=f-1-t.padInfo.left,b=1/(e*r);this.userCode=`
      const ivec2 pads = ivec2(${m}, ${y});
      const float avgMultiplier = float(${b});

      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 < ${p};
            wR += ${c}) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

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

          for (int wC = 0; wC < ${f};
            wC+= ${l}) {
            float dyC = float(dyCCorner + wC) / ${s}.0;

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

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

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}}class PY{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterDepth,b=t.effectiveFilterHeight,v=t.effectiveFilterWidth,T=y-1-t.padInfo.front,N=b-1-t.padInfo.top,S=v-1-t.padInfo.left,D=1/(e*r*o);this.userCode=`
      const ivec3 pads = ivec3(${T}, ${N}, ${S});
      const float avgMultiplier = float(${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(?, ?, ?, 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 < ${y};
            wD += ${p}) {
          float dyD = float(dyDCorner + wD) / ${s}.0;

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

          for (int wR = 0; wR < ${b};
              wR += ${f}) {
            float dyR = float(dyRCorner + wR) / ${c}.0;

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

            for (int wC = 0; wC < ${v};
                wC += ${m}) {
              float dyC = float(dyCCorner + wC) / ${l}.0;

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

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

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}let PI=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,OY=`
  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;
  }
`,LY=`
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);
`,Zst="return (a - b) * (a - b);",MY="return float(a == b);",BY="return float(a < b);",zY="return float(a <= b);",WY="return float(a > b);",VY="return float(a >= b);",GY="return float(a >= 1.0 && b >= 1.0);",UY="return float(a >= 1.0 || b >= 1.0);",qY=PI+`
  return max(a, b);
`,HY=PI+`
  return min(a, b);
`,jY=`if (b == 0.0) return NAN;
  return mod(a, b);`,KY="return (b >= 1.0) ? a : a * (b + 1.0);",OI="return (a < 0.) ? b * a : a;";class Hn{constructor(t,e,r){this.variableNames=["A","B"],this.outputShape=le(e,r),this.userCode=`
      float binaryOperation(float a, float b) {
        ${t}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}}let ng=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`,XY=`
  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);
`,YY=`
  // 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));
  `+ng+`
  return result;
`,LI=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`,JY=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,ZY=`
  return vec4(equal(a, b));
`,Qst=`
  return vec4(notEqual(a, b));
`,QY=`
  return vec4(lessThan(a, b));
`,t7=`
  return vec4(lessThanEqual(a, b));
`,e7=`
  return vec4(greaterThan(a, b));
`,n7=`
  return vec4(greaterThanEqual(a, b));
`,r7=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,o7=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,s7=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+ng+`
  return result;
`,i7=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+ng+`
  return result;
`,a7=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+ng+`
  return result;
`;class Ss{constructor(t,e,r,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=le(e,r);let s=this.outputShape.length,c="";if(o)if(s===0||G(this.outputShape)===1)c=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else{let l=Oe(s);if(c=`
          ${l} coords = getOutputCoords();
        `,s===1)c+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let p=Jn("coords",s);c+=`
            bool nextRowOutOfBounds =
              (${p[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${p[s-1]} + 1) >= ${this.outputShape[s-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${t}
      }

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

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

        setOutput(result);
      }
    `}}class c7{constructor(t){this.variableNames=["A"],this.outputShape=t,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(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class l7{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,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(t,e){return(r,o)=>{this.minLoc==null&&(this.minLoc=r.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=r.getUniformLocationNoThrow(o,"maxVal")),r.gl.uniform1f(this.minLoc,t),r.gl.uniform1f(this.maxLoc,e)}}}class u7{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode=`
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `}}class p7{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.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 < ${t.batchSize}; b++) {
          for (int yR = 0; yR < ${t.outHeight}; yR++) {
            int xR = wR + yR * ${e} - ${o};

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

            for (int yC = 0; yC < ${t.outWidth}; yC++) {
              int xC = wC + yC * ${r} - ${s};

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

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

            }
          }
        }
        setOutput(dotProd);
      }
    `}}class h7{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=t.dataFormat==="channelsLast",l=e-1-t.padInfo.top,p=r-1-t.padInfo.left,f=c?1:2,m=c?2:3,y=c?3:1;this.userCode=`
      const ivec2 pads = ivec2(${l}, ${p});

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

        ivec2 dyCorner = ivec2(coords[${f}], coords[${m}]) - 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 < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

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

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

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

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

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

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

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

            }
          }
        }
        setOutput(dotProd);
      }
    `}}class f7{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,c=t.padInfo.top,l=t.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 < ${t.batchSize}; b++) {
          for (int yF = 0; yF < ${t.outDepth}; yF++) {
            int xF = wF + yF * ${e} - ${s};

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

            for (int yR = 0; yR < ${t.outHeight}; yR++) {
              int xR = wR + yR * ${r} - ${c};

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

              for (int yC = 0; yC < ${t.outWidth}; yC++) {
                int xC = wC + yC * ${o} - ${l};

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

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class d7{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,r=t.filterHeight,o=t.filterWidth,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=e-1-t.padInfo.front,f=r-1-t.padInfo.top,m=o-1-t.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${f}, ${m});

      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 < ${e}; wF++) {
          float dyF = float(dyFCorner + wF) / ${s}.0;

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

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

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

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

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

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

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

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

              for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class m7{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,r=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,c=t.outChannels/t.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 * ${c} + dm;

        float dotProd = 0.0;

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

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

            for (int yC = 0; yC < ${t.outWidth}; yC++) {
              int xC = wC + yC * ${r} - ${s};

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

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class g7{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,r=t.filterWidth,o=t.strideHeight,s=t.strideWidth,c=e-1-t.padInfo.top,l=r-1-t.padInfo.left,p=t.outChannels/t.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${c}, ${l});

      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 < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

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

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

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

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${p}; dm++) {
              int d2 = d1 * ${p} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class MI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.padInfo.top,c=t.padInfo.left,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterHeight,b=t.filterWidth,v=Math.floor(t.inChannels/4)*4,T=t.inChannels%4,N=t.dataFormat==="channelsLast",S=N?1:2,D=N?2:3,I=N?3:1,P="",E="";r&&(o?P=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:P=`
          float activation(float x) {
            ${r}
          }
        `,E="result = activation(result);");let L=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${P}

      const ivec2 strides = ivec2(${l}, ${p});
      const ivec2 pads = ivec2(${s}, ${c});

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

        ivec2 xRCCorner =
            ivec2(coords[${S}], coords[${D}]) * 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 < ${y}; wR++) {
          int xR = xRCorner + wR * ${f};

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

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

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

            for (int d1 = 0; d1 < ${v}; 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 (${N}) {
                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 (${T===1}) {

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

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

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

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

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

            }
          }
        }

        float result = dotProd;
        ${L}
        ${E}
        setOutput(result);
      }
    `}}class y7{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,r=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,c=t.strideHeight,l=t.strideWidth,p=t.dilationDepth,f=t.dilationHeight,m=t.dilationWidth,y=t.filterDepth,b=t.filterHeight,v=t.filterWidth,T=Math.floor(t.inChannels/4)*4,N=t.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${s}, ${c}, ${l});
      const ivec3 pads = ivec3(${e}, ${r}, ${o});

      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 < ${y}; wF++) {
          int xF = xFCorner + wF * ${p};

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

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

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

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

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

              for (int d1 = 0; d1 < ${T}; 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 (${N===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${T}) *
                  getW(wF, wR, wC, ${T}, d2);
              } else if (${N===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${T}),
                  getX(batch, xF, xR, xC, ${T} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${T}, d2),
                  getW(wF, wR, wC, ${T} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${N===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${T}),
                  getX(batch, xF, xR, xC, ${T} + 1),
                  getX(batch, xF, xR, xC, ${T} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${T}, d2),
                  getW(wF, wR, wC, ${T} + 1, d2),
                  getW(wF, wR, wC, ${T} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class BI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=t.outChannels/t.inChannels,S="",D="";r&&(o?S=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:S=`
          float activation(float x) {
            ${r}
          }
        `,D="result = activation(result);");let I=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${S}

      const ivec2 strides = ivec2(${f}, ${m});
      const ivec2 pads = ivec2(${l}, ${p});

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

        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 < ${v}; wR++) {
          int xR = xRCorner + wR * ${y};

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

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

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

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

        float result = dotProd;
        ${I}
        ${D}
        setOutput(result);
      }
    `}}class zI{constructor(t,e=!1,r=null,o=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.outShape;let s=t.inHeight,c=t.inWidth,l=t.padInfo.top,p=t.padInfo.left,f=t.strideHeight,m=t.strideWidth,y=t.dilationHeight,b=t.dilationWidth,v=t.filterHeight,T=t.filterWidth,N=T,S="int xR; int xC; int xCOffset;";for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`
          vec4 xTexelR${E}C${L*2} = vec4(0.);
          vec4 wR${E}C${L} = vec4(0.);
          vec4 xR${E}C${L} = vec4(0.);`;for(let E=0;E<v;E++)for(let L=0;L<N;L++){let B=L*2;if(S+=`
          xR = xRCorner + ${E*y};
          xC = xCCorner + ${B*b};
        `,m===1){if(B<T&&(p%2===1?S+=`
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${c}) {
                  xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);

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

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

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

                  xR${E}C${B} = vec4(previous.zw, xTexelR${E}C${B}.xy);
                } else {
                  xR${E}C${B} = vec4(0, 0, xTexelR${E}C${B}.xy);
                }
              `:S+=`
                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${c}) {
                  xTexelR${E}C${B} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${E}C${B} = vec4(0.);
                }

                xR${E}C${B} = xTexelR${E}C${B};
              `,B+1<T)){let q=p%2===0?k(b):b;b%2===0&&p%2===1||b%2!==0&&p%2!==1?(S+=`
                  xCOffset = xC + ${p%2} + ${q};

                  if(xR >= 0 && xR < ${s} &&
                    xCOffset >= 0 && xCOffset < ${c}) {
                    xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
                  }
                `,b>1&&(S+=`
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${s} &&
                      xCOffset >= 0 && xCOffset < ${c}) {
                      xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${E}C${B} = vec4(0.);
                    }
                  `),S+=`
                  xR${E}C${B+1} = vec4(
                    xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.xy);
                `):S+=`
                  xCOffset = xC + ${q};

                  if(xR >= 0 && xR < ${s} &&
                    xCOffset >= 0 && xCOffset < ${c}) {
                    xTexelR${E}C${B+2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${E}C${B+1} = xTexelR${E}C${B+2};
                `}}else B<T&&(S+=`
              if(xR >= 0 && xR < ${s}) {
            `,p%2===1?(S+=`
                xCOffset = xC + 1 - ${m};
                if(xCOffset >= 0 && xCOffset < ${c}) {
                  xTexelR${E}C${B} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${E}C${B} = vec4(0.);
                }

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

                xR${E}C${B} = vec4(
                  xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
              `,B+1<T&&(S+=`
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${m};
                  if(xCOffset >= 0 && xCOffset < ${c}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${E}C${B+1} = vec4(xTexelR${E}C${B+2}.xy, final.xy);
                `)):(S+=`
                if(xC >= 0 && xC < ${c}) {
                  xTexelR${E}C${B} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${E}C${B} = vec4(0.);
                }

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

                xR${E}C${B} = vec4(
                  xTexelR${E}C${B}.xy, xTexelR${E}C${B+2}.xy);
              `,B+1<T&&(S+=`
                  xR${E}C${B+1} = vec4(
                    xTexelR${E}C${B}.zw, xTexelR${E}C${B+2}.zw);
                `)),S+="}");B<T&&(S+=`
            vec4 wTexelR${E}C${B} = getW(${E}, ${B}, d1, q);
            wR${E}C${B} = vec4(wTexelR${E}C${B}.xz, wTexelR${E}C${B}.xz);
          `,B+1<T&&(S+=`
              vec4 wTexelR${E}C${B+1} = getW(${E}, ${B+1}, d1, q);
              wR${E}C${B+1} =
                vec4(wTexelR${E}C${B+1}.xz, wTexelR${E}C${B+1}.xz);`))}for(let E=0;E<v;E++)for(let L=0;L<T;L++)S+=`dotProd += xR${E}C${L} * wR${E}C${L};`;let D="",I="";r&&(o?D=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${r}
        }`:D=`vec4 activation(vec4 x) {
          ${r}
        }`,I="result = activation(result);");let P=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.userCode=`
      ${D}

      const ivec2 strides = ivec2(${f}, ${m});
      const ivec2 pads = ivec2(${l}, ${p});

      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.);

        ${S}

        vec4 result = dotProd;
        ${P}
        ${I}
        setOutput(result);
      }
    `}}class b7{constructor(t,e,r,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[c,l,p,f]=t,[m]=e,[y,b]=r;this.outputShape=[m,y,b,f];let v=o==="bilinear"?1:0,[T,N]=[`${l-1}.0`,`${p-1}.0`],[S,D,I]=y>1?[`${(l-1)/(y-1)}`,"(y2-y1) * height_ratio",`y1*${T} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${T}`],[P,E,L]=b>1?[`${(p-1)/(b-1)}`,"(x2-x1) * width_ratio",`x1*${N} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${N}`];this.userCode=`
      const float height_ratio = float(${S});
      const float width_ratio = float(${P});
      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 >= ${c}) {
          return;
        }

        float height_scale = ${D};
        float width_scale = ${E};

        float in_y = ${I};
        if( in_y < 0.0 || in_y > ${T} ) {
          setOutput(float(${s}));
          return;
        }
        float in_x = ${L};
        if( in_x < 0.0 || in_x > ${N} ) {
          setOutput(float(${s}));
          return;
        }

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

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

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}}class WI{constructor(t,e,r){this.variableNames=["x"],this.outputShape=t;let o=t.length,s=e?"0.0":`getX(${VI(o,"coords")})`,c=t[t.length-1],l="",p="";e?(l=r?`end != ${c-1}`:"end != 0",p=r?"end + 1":"end - 1"):(l=r?`end + pow2 < ${c}`:"end >= pow2",p=r?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${Oe(o)} coords = getOutputCoords();
        int end = ${GI(o,"coords")};
        float val = ${s};
        int pow2 = int(pow(2.0, index));
        if (${l}) {
          int idx = ${p};
          ${GI(o,"coords")} = idx;
          val += getX(${VI(o,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(t){return(e,r)=>{this.index==null&&(this.index=e.getUniformLocation(r,"index")),e.gl.uniform1f(this.index,t)}}}function VI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.x, ${t}.y`;if(n===3)return`${t}.x, ${t}.y, ${t}.z`;if(n===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}function GI(n,t){if(n===1)return`${t}`;if(n===2)return`${t}.y`;if(n===3)return`${t}.z`;if(n===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${n} is not yet supported`)}class x7{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ch.DENSE;let e=uh(t),r=Zn();this.outputShape=t,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${_a(["r","c","d"],t)}
        return ivec3(r, c, d);
      }

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

        ${r.output} = result;
      }
    `}}class w7{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ch.DENSE;let e=uh(t),r=Zn();this.outputShape=t,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${_a(["r","c","d"],t)}
        return ivec3(r, c, d);
      }

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

        ${r.output} = result;
      }
    `}}class v7{constructor(t,e,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=r,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 / ${e};
      int offset_h = imod(h, ${e});
      int in_w = w / ${e};
      int offset_w = imod(w, ${e});
      int offset_d = (offset_h * ${e} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}class T7{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}}class k7{constructor(t){this.variableNames=["A"],this.outTexUsage=Br.DOWNLOAD;let e=Zn();this.outputShape=t,this.userCode=`
      ${DI}

      void main() {
        float x = getAAtOutCoords();
        ${e.output} = encode_float(x);
      }
    `}}class N7{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Br.DOWNLOAD;let e=Zn();this.outputShape=t,this.userCode=`
      ${DI}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${e.output} = encode_float(x);
      }
    `}}class _7{constructor(t,e,r=!1){this.variableNames=["A"];let o=Zn(),[s,c]=e;this.outputShape=t;let l="result";r&&(l="floor(result * 255. + 0.5)"),this.userCode=`
      ${P1(t)}

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

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

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

        int r = flatIndex / ${c};
        int c = imod(flatIndex, ${c});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${c}.0, ${s}.0);
        vec4 values = ${o.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];
        }

        ${o.output} = vec4(${l}, 0., 0., 0.);
      }
    `}}class C7{constructor(t,e,r=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Zn(),[s,c]=e;this.outputShape=t;let l="",p="result";r&&(p="floor(result * 255. + 0.5)");for(let f=0;f<=1;f++)for(let m=0;m<=1;m++){let y=f*2+m;l+=`
          localCoords = coords;
          if(localCoords[2] + ${m} < ${t[2]}) {
            localCoords[2] += ${m};
            if(localCoords[1] + ${f} < ${t[1]}) {
              localCoords[1] += ${f};

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

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

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

              if(offset == 0) {
                result[${y}] = values[0];
              } else if(offset == 1) {
                result[${y}] = values[1];
              } else if(offset == 2) {
                result[${y}] = values[2];
              } else {
                result[${y}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${P1(t)}

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

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

        ${l}

        ${o.output} = ${p};
      }
    `}}class S7{constructor(t,e){this.outputShape=[],this.variableNames=["x"],this.outputShape=t,this.userCode=`
      uniform float value;
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}getCustomSetupFunc(t){return(e,r)=>{this.valueLoc==null&&(this.valueLoc=e.getUniformLocationNoThrow(r,"value")),e.gl.uniform1f(this.valueLoc,t)}}}class $7{constructor(t,e,r){this.variableNames=["A","indices"];let o=t.slice();o[r]=e,this.outputShape=o,this.rank=o.length;let s=Oe(this.rank),c=I7(t,r);this.userCode=`
      void main() {
        ${s} resRC = getOutputCoords();
        setOutput(getA(${c}));
      }
    `}}function I7(n,t){let e=n.length;if(e>4)throw Error(`Gather for rank ${e} is not yet supported`);if(e===1)return"int(getIndices(resRC))";let r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let s=0;s<n.length;s++)s===t?o.push(`int(getIndices(${r[s]}))`):o.push(`${r[s]}`);return o.join()}class E7{constructor(t,e,r){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=r;let o=Oe(e.length),s=Oe(r.length),c=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${o} strides = ${o}(${this.strides});
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${c};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}}function D7(n){let t=Zn(),e=`${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 uX(n,e)}function A7(n){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 gX(n,t)}function F7(n){let t=new Uint16Array([0,1,2,2,1,3]);return yX(n,t)}function hh(n,t,e,r,o,s){xX(t,e);let c=bX(n),l=n.TEXTURE_2D;return Rt(n,()=>n.bindTexture(l,c)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MIN_FILTER,n.NEAREST)),Rt(n,()=>n.texParameteri(l,n.TEXTURE_MAG_FILTER,n.NEAREST)),Rt(n,()=>n.texImage2D(l,0,r,t,e,0,o,s,null)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null)),c}function UI(n){return n.internalFormatFloat}function R7(n,t,e,r){let[o,s]=lh(t,e);return hh(n,o,s,UI(r),r.textureFormatFloat,n.FLOAT)}function qI(n){return n.internalFormatHalfFloat}function P7(n,t,e,r){let[o,s]=lh(t,e);return hh(n,o,s,qI(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function HI(n){return n.downloadTextureFormat}function O7(n,t,e,r){let[o,s]=lh(t,e);return hh(n,o,s,HI(r),n.RGBA,n.UNSIGNED_BYTE)}function jI(n){return n.internalFormatPackedFloat}function L7(n,t,e,r){let[o,s]=fl(t,e);return hh(n,o,s,jI(r),n.RGBA,n.FLOAT)}function KI(n){return n.internalFormatPackedHalfFloat}function M7(n,t,e,r){let[o,s]=fl(t,e);return hh(n,o,s,KI(r),n.RGBA,r.textureTypeHalfFloat)}function B7(n,t,e){let r=0,o=3*4,s=3*4+2*4;Rt(n,()=>n.bindBuffer(n.ARRAY_BUFFER,e));let c=CI(n,t,"clipSpacePos",e,3,s,r);return c&&CI(n,t,"uv",e,2,s,o)}function z7(n,t,e,r,o,s){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t));let c,l,p;o instanceof Uint8Array?(c=new Uint8Array(e*r*4),l=n.UNSIGNED_BYTE,p=n.RGBA):(c=new Float32Array(e*r*4),l=n.FLOAT,p=s.internalFormatPackedFloat),c.set(o),Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,p,e,r,0,n.RGBA,l,c)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function W7(n,t,e){Rt(n,()=>n.bindTexture(n.TEXTURE_2D,t)),e.data instanceof Uint8Array?Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,e.width,e.height,0,n.RGBA,n.UNSIGNED_BYTE,e.data)):Rt(n,()=>n.texImage2D(n.TEXTURE_2D,0,n.RGBA,n.RGBA,n.UNSIGNED_BYTE,e)),Rt(n,()=>n.bindTexture(n.TEXTURE_2D,null))}function V7(n,t,e,r){let o=n.createBuffer();Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,o));let s=4,c=4,l=s*c*t*e;return Rt(n,()=>n.bufferData(n.PIXEL_PACK_BUFFER,l,n.STREAM_READ)),Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,0)),Rt(n,()=>n.bindBuffer(n.PIXEL_PACK_BUFFER,null)),o}function G7(n,t,e){let r=n,o=new Float32Array(e);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,o),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),o}function U7(n,t,e,r){let[o,s]=lh(t,e),c=4,l=new Uint8Array(rX(t*e,c));return Rt(n,()=>n.readPixels(0,0,o,s,r.downloadTextureFormat,n.UNSIGNED_BYTE,l)),new Float32Array(l.buffer)}function q7(n,t,e,r,o,s,c,l){let p=n,f=new Float32Array(oX(s,c));return p.bindBuffer(p.PIXEL_PACK_BUFFER,t),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,f),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),f}function H7(n,t,e){let r=new Float32Array(t*e*4);return Rt(n,()=>n.readPixels(0,0,e,t,n.RGBA,n.FLOAT,r)),r}class j7{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let e=ct().getNumber("WEBGL_VERSION");t!=null?(this.gl=t,tX(e,t)):this.gl=Xo(e);let r="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(ct().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",c="OES_texture_half_float";if(this.textureFloatExtension=Xm(this.gl,s),ro(this.gl,c))this.textureHalfFloatExtension=Xm(this.gl,c);else if(ct().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(r),ro(this.gl,o))this.colorBufferHalfFloatExtension=Xm(this.gl,o);else if(ct().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(r="EXT_color_buffer_float",ro(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(ro(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=A7(this.gl),this.indexBuffer=F7(this.gl),this.framebuffer=wX(this.gl),this.textureConfig=I1(this.gl,this.textureHalfFloatExtension)}get debug(){return ct().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 t=this.gl;Rt(t,()=>t.finish()),Rt(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),Rt(t,()=>t.deleteFramebuffer(this.framebuffer)),Rt(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),Rt(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),Rt(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),R7(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),P7(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),O7(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),W7(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,r,o){this.throwIfDisposed(),z7(this.gl,t,e,r,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),M7(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),L7(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(SI(this.gl,this.framebuffer),this.outputTexture=null),Rt(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,r){return this.downloadMatrixDriver(t,()=>U7(this.gl,e,r,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,r,o,s,c){return q7(this.gl,t,e,r,o,s,c,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return G7(this.gl,t,e)}createBufferFromTexture(t,e,r){this.bindTextureToFrameBuffer(t);let o=V7(this.gl,e,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,r;if(ct().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),r=()=>{let c=o.clientWaitSync(s,0,0);return c===o.ALREADY_SIGNALED||c===o.CONDITION_SATISFIED},e=s}else ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(e,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:e,isFencePassed:r}}downloadMatrixFromPackedTexture(t,e,r){return this.downloadMatrixDriver(t,()=>H7(this.gl,e,r))}createProgram(t){this.throwIfDisposed();let e=this.gl,r=pX(e,t),o=D7(e),s=dX(e);return Rt(e,()=>e.attachShader(s,o)),Rt(e,()=>e.attachShader(s,r)),mX(e,s),this.debug&&E1(e,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=B7(e,this.program,this.vertexBuffer)),s}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&Rt(this.gl,()=>this.gl.deleteProgram(t))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&E1(this.gl,this.program),Rt(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,r=!0){return this.throwIfDisposed(),r?TX(this.gl,t,e):kX(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),Rt(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,r){this.throwIfDisposed(),this.throwIfNoProgram(),NX(this.gl,t,e,r)}setOutputMatrixTexture(t,e,r){this.setOutputMatrixTextureDriver(t,r,e)}setOutputPackedMatrixTexture(t,e,r){this.throwIfDisposed();let[o,s]=fl(e,r);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,r,o){this.setOutputMatrixWriteRegionDriver(r,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,r,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&E1(this.gl,this.program),Ym(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;this.debug&&this.debugValidate(),Rt(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Xm(this.gl,ct().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(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.createQuery();return r.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,r=this.getQueryTimerExtensionWebGL2();e.endQuery(r.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await _e(()=>this.disposed||this.isQueryAvailable(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let r=this.gl,o=r.getQueryParameter(t,r.QUERY_RESULT);return o/1e6}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT);return o/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let r=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=r.getQueryParameter(t,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),o=r.getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=K7(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:r}=this.itemsToPoll[e];r()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;_e(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(t){this.throwIfDisposed(),D1(this.gl,t,this.framebuffer),this.debug&&Ym(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(D1(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ym(this.gl)):SI(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let r=e();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(t,e,r){this.throwIfDisposed();let o=this.gl;D1(o,t,this.framebuffer),this.debug&&Ym(o),this.outputTexture=t,Rt(o,()=>o.viewport(0,0,e,r)),Rt(o,()=>o.scissor(0,0,e,r))}setOutputMatrixWriteRegionDriver(t,e,r,o){this.throwIfDisposed(),Rt(this.gl,()=>this.gl.scissor(t,e,r,o))}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 K7(n){let t=0;for(;t<n.length;++t){let e=n[t]();if(!e)break}return t-1}function X7(n,t,e,r){let o=t.userCode,s=e.map((v,T)=>{let N={logicalShape:v.shape,texShape:v.isUniform?null:v.texData.texShape,isUniform:v.isUniform,isPacked:v.isUniform?!1:v.texData.isPacked,flatOffset:null};return v.texData!=null&&v.texData.slice!=null&&v.texData.slice.flatOffset>0&&(N.flatOffset=v.texData.slice.flatOffset),{name:t.variableNames[T],shapeInfo:N}}),c=s.map(v=>v.shapeInfo),l={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},p=JX(s,l,o,t.packedInputs),f=n.createProgram(p),m=null,y=n.getUniformLocation(f,"NAN",!1);ct().getNumber("WEBGL_VERSION")===1&&(m=n.getUniformLocation(f,"INFINITY",!1));let b={};for(let v=0;v<t.variableNames.length;v++){let T=t.variableNames[v],N=!1;b[T]=n.getUniformLocation(f,T,N),b[`offset${T}`]=n.getUniformLocation(f,`offset${T}`,N)}return{program:t,source:p,webGLProgram:f,uniformLocations:b,inShapeInfos:c,outShapeInfo:l,infLoc:m,nanLoc:y}}function XI(n,t){if(n.length!==t.length)throw Error(`Binary was compiled with ${n.length} inputs, but was executed with ${t.length} inputs`);n.forEach((e,r)=>{let o=e.logicalShape,s=t[r],c=s.shape;if(!lt(o,c))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${c} must match`);if(e.isUniform&&s.isUniform)return;let l=e.texShape,p=s.isUniform?null:s.texData.texShape;if(!lt(l,p))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${l} and ${p} must match`)})}function Y7(n,t,e,r,o){XI(t.inShapeInfos,e),XI([t.outShapeInfo],[r]);let s=r.texData.texture,c=r.texData.texShape;r.texData.isPacked?n.setOutputPackedMatrixTexture(s,c[0],c[1]):n.setOutputMatrixTexture(s,c[0],c[1]),n.setProgram(t.webGLProgram),ct().getNumber("WEBGL_VERSION")===1&&(t.infLoc!==null&&n.gl.uniform1f(t.infLoc,Infinity)),t.nanLoc!==null&&n.gl.uniform1f(t.nanLoc,NaN),e.forEach((l,p)=>{let f=t.program.variableNames[p],m=t.uniformLocations[f],y=t.uniformLocations[`offset${f}`];if(m==null)return;if(l.isUniform){if(G(l.shape)<2)n.gl.uniform1f(m,l.uniformValues[0]);else{let b=l.uniformValues;b instanceof Float32Array||(b=new Float32Array(b)),n.gl.uniform1fv(m,b)}return}l.texData.slice!=null&&y!=null&&n.gl.uniform1i(y,l.texData.slice.flatOffset),n.setInputMatrixTexture(l.texData.texture,m,p)}),o!=null&&o(n,t.webGLProgram),n.executeProgram()}function J7(n,t,e){let r="";t.concat(e).forEach(c=>{let l=c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0,p=c.isUniform?"uniform":c.texData.texShape;r+=`${c.shape}_${p}_${l}`});let o=n.userCode,s=n.constructor.name;return s+="_"+r+"_"+o,s}class Z7{constructor(t,e,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let{filterWidth:o,inChannels:s,strideWidth:c,strideHeight:l,padInfo:p,outWidth:f,dilationWidth:m,dilationHeight:y,dataFormat:b}=r,{left:v,top:T}=p,N=s*o,S=Zn(),D=b==="channelsLast",I=D?0:1,P=D?1:2,E="";for(let L=0;L<=1;L++)for(let B=0;B<=1;B++)E+=`
          blockIndex = rc.y + ${B};
          pos = rc.x + ${L};

          if(blockIndex < ${t[1]} && pos < ${t[0]}) {
            offsetY = int(blockIndex / (${f})) * ${l} - ${T};
            d0 = offsetY + ${y} * (pos / ${N});

            if(d0 < ${e[I]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${f}.) * ${c}. - ${v}.);
              d1 = offsetX + ${m} * (int(mod(float(pos), ${N}.) / ${s}.));

              if(d1 < ${e[P]} && d1 >= 0) {

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

                if (${D}) {
                  innerDims = vec2(d1, ch);
                  result[${L*2+B}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${L*2+B}] = 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;

        ${E}

        ${S.output} = result;
      }
    `}}class Q7{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[];let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${c}; j <= ${c}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${l}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${p};
        setOutput(val);
      }
    `}}class tJ{constructor(t,e,r,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=r,this.alpha=o,this.beta=s,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${e})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${e} + 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(${o}) * norm + float(${r});

          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(${o})
                * float(${s})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}}class eJ{constructor(t,e,r,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let c=e,l=t[3]-1;this.outputShape=t;let p,f=`float(${r}) + float(${o}) * sum`;s===.5?p=`inversesqrt(${f})`:s===1?p=`1.0/(${f})`:p=`exp(log(${f}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

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

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

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

        int firstChannel = d - ${c};
        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 = - ${c}; j <= ${c}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${l}));

          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 * ${p};
        setOutput(result);
      }
    `}}class nJ{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,r=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=s-1-t.padInfo.top,p=c-1-t.padInfo.left,f=s*c-1;this.userCode=`
      const ivec2 pads = ivec2(${l}, ${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 < ${s};
          wR += ${o}) {
          float dyR = float(dyRCorner + wR) / ${e}.0;

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

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

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

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

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

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}}class rJ{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,r=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,c=t.dilationHeight,l=t.dilationWidth,p=t.effectiveFilterDepth,f=t.effectiveFilterHeight,m=t.effectiveFilterWidth,y=p-1-t.padInfo.front,b=f-1-t.padInfo.top,v=m-1-t.padInfo.left,T=p*f*m-1;this.userCode=`
      const ivec3 pads = ivec3(${y}, ${b}, ${v});

      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 < ${p};
           wD += ${s}) {
          float dyD = float(dyDCorner + wD) / ${e}.0;

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

          for (int wR = 0; wR < ${f};
              wR += ${c}) {
            float dyR = float(dyRCorner + wR) / ${r}.0;

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

            for (int wC = 0; wC < ${m};
                wC += ${l}) {
              float dyC = float(dyCCorner + wC) / ${o}.0;

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}}class O1{constructor(t,e,r,o=!1,s=!1,c=!1,l=null,p=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r;let f=o?t[1]:t[2],m=Math.ceil(f/2),y=o?"i * 2, rc.y":"rc.y, i * 2",b=s?"rc.z, i * 2":"i * 2, rc.z",v=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],T=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],N="",S="";l&&(p?N=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${l}
        }`:N=`vec4 activation(vec4 x) {
          ${l}
        }`,S="result = activation(result);");let D=c?"result += getBiasAtOutCoords();":"";c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights");let I="rc.x",P="rc.x";t[0]<e[0]?I=`int(min(float(rc.x), ${t[0]-1}.))`:e[0]<t[0]&&(P=`int(min(float(rc.x), ${e[0]-1}.))`),this.userCode=`
      ${N}

      const float sharedDimension = ${m}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${m}; i++) {
          int batchA = ${I};
          int batchB = ${P};
          vec4 a = getMatrixA(batchA, ${y});
          vec4 b = getMatrixB(batchB, ${b});

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

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

        ${D}

        ${S}

        setOutput(result);
      }
    `}}class oJ{constructor(t,e,r){this.variableNames=["probs"],this.outputShape=[t,r],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 < ${e-1}; i++) {
          cdf += getProbs(batch, i);

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

        // If no other event happened, last event happened.
        setOutput(float(${e-1}));
      }
    `}getCustomSetupFunc(t){return(e,r)=>{this.seedLoc==null&&(this.seedLoc=e.getUniformLocation(r,"seed")),e.gl.uniform1f(this.seedLoc,t)}}}class sJ{constructor(t,e,r,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${o}), float(${r}),
                      float(index == coords.y)));
      }
    `}}class iJ{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t;let e=t.length;if(e===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let r=Jn("rc",e),o=Oe(e),s=cJ(e,t,r),c=lJ(e,t[t.length-1],t[t.length-2],r),l=uJ(t,r);this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();

          if(${s}) {
            setOutput(vec4(0));
          } else {
            ${c}

            setOutput(vec4(${l}));
          }
        }
      `}}}function aJ(n,t){let e=[];for(let r=0;r<=1;r++)for(let o=0;o<=1;o++){let s=`${r===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let c=2;c<n;c++)s=`${t[t.length-1-c]},`+s;e.push(s)}return e}function cJ(n,t,e){if(n===1)return`rc > ${t[0]}`;let r="";for(let o=n-2;o<n;o++)r+=`${e[o]} >= ${t[o]}`,o<n-1&&(r+="||");return r}function lJ(n,t,e,r){if(n===1)return"";let o=r.slice(-2);return`
    int r = ${o[0]};
    int c = ${o[1]};
    int rp1 = r + 1;
    int cp1 = c + 1;

    bool cEdge = cp1 >= ${t};
    bool rEdge = rp1 >= ${e};
  `}function uJ(n,t){let e=n.length,r=aJ(e,t);return e===1?`getA(rc),
            rc + 1 >= ${n[0]} ? 0. : getA(rc + 1),
            0, 0`:`getA(${r[0]}),
          cEdge ? 0. : getA(${r[1]}),
          rEdge ? 0. : getA(${r[2]}),
          rEdge || cEdge ? 0. : getA(${r[3]})`}class pJ{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((f,m)=>f[0]+t[m]+f[1]);let o=t.length,s=Oe(o),c=e.map(f=>f[0]).join(","),l=e.map((f,m)=>f[0]+t[m]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
        int start = ${c};
        int end = ${l};

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(float(${r}));
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${p}));
        }
      }
    `}}class hJ{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((N,S)=>N[0]+t[S]+N[1]);let o=t.length,s=Oe(o),c=e.map(N=>N[0]).join(","),l=e.map((N,S)=>N[0]+t[S]).join(","),p=Jn("rc",o),f=Jn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=[`${s} rc = outputLoc;`,`${p[o-1]} += 1;
       if(${m}) {
      `,o===1?"":`}
       rc = outputLoc;
       ${p[o-2]} += 1;
       if(${p[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${p[o-1]} += 1;
         if(${m}) {`],v=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",T="";for(let N=0,S=o===1?2:4;N<S;N++)T+=`
        ${b[N]}
        if (${v}) {
          result[${N}] = float(${r});
        } else {
          ${s} source = rc - start;
          result[${N}] = getChannel(getX(${f.join()}), ${y});
        }
      `;T+=o===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${c});
      const ${s} end = ${s}(${l});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${T}
        setOutput(result);
      }
    `}}class fh{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideHeight,p=t.strideWidth,f=t.dilationHeight,m=t.dilationWidth,y=t.effectiveFilterHeight,b=t.effectiveFilterWidth,v=t.padInfo.top,T=t.padInfo.left;this.outputShape=t.outShape;let N=e==="avg",S=`((batch  * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,D=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,I="0.0";if(N||(I="-1.0 / 1e-20"),r){let H=">=";this.userCode=`
        const ivec2 strides = ivec2(${l}, ${p});
        const ivec2 pads = ivec2(${v}, ${T});

        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 < ${y};
              wR += ${f}) {
            int xR = xRCorner + wR;

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

            for (int wC = 0; wC < ${b};
                wC += ${m}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${t.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 ${H} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${o?s?S:D:`wR * ${b} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let P="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / count");let L=Math.floor(c/4)*4,B=c%4,q=`
      if (${N}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${P}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${l}, ${p});
      const ivec2 pads = ivec2(${v}, ${T});
      const float initializationValue = ${I};
      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 >= ${t.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(${I});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${y};
            wR += ${f}) {
          int xR = xRCorner + wR;

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

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

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

            ${q}
          }

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

            ${q}
          } else if (${B===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${m}, d),
              initializationValue,
              initializationValue
            );

            ${q}
          } else if (${B===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${m}, d),
              getValue(batch, xR, xC + 2 * ${m}, d),
              initializationValue
            );

            ${q}
          }
        }
        setOutput(${E});
      }
    `}}class L1{constructor(t,e,r,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let c=t.filterWidth,l=t.strideDepth,p=t.strideHeight,f=t.strideWidth,m=t.dilationDepth,y=t.dilationHeight,b=t.dilationWidth,v=t.effectiveFilterDepth,T=t.effectiveFilterHeight,N=t.effectiveFilterWidth,S=t.padInfo.front,D=t.padInfo.top,I=t.padInfo.left;this.outputShape=t.outShape;let P=e==="avg",E="0.0";if(P||(E="-1.0 / 1e-20"),r){let J=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${l}, ${p}, ${f});
        const ivec3 pads = ivec3(${S}, ${D}, ${I});

        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 < ${v};
              wD += ${m}) {
            int xD = xDCorner + wD;

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

            for (int wR = 0; wR < ${T};
                wR += ${y}) {
              int xR = xRCorner + wR;

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

              for (int wC = 0; wC < ${N};
                  wC += ${b}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${t.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 ${J} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${T} * ${N} +
                      wR * ${N} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let L="max",B=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(B="avgValue / count");let q=Math.floor(c/4)*4,H=c%4,Z=`
      if (${P}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${L}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${l}, ${p}, ${f});
      const ivec3 pads = ivec3(${S}, ${D}, ${I});
      const float initializationValue = ${E};
      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 >= ${t.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(${E});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${v};
            wD += ${m}) {
          int xD = xDCorner + wD;

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

          for (int wR = 0; wR < ${T};
            wR += ${y}) {
            int xR = xRCorner + wR;

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

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

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

              ${Z}
            }

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

              ${Z}
            } else if (${H===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${b}, ch),
                initializationValue,
                initializationValue
              );

              ${Z}
            } else if (${H===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${b}, ch),
                getValue(batch, xD, xR, xC + 2 * ${b}, ch),
                initializationValue
              );

              ${Z}
            }
          }
          setOutput(${B});
        }
      }
    `}}class YI{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l="0.0",p="";e==="prod"?l="1.0":e==="min"?(l="1.0 / 1e-20",p="min"):e==="max"&&(l="-1.0 / 1e-20",p="max");let f=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?f="sumValue":e==="prod"?f="prodValue":e==="all"?f="allValue":e==="any"&&(f="anyValue");let m=Math.floor(r/4)*4,y=r%4,b=`
      if (${e==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${e==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${p}(values, minMaxValue);
      }
    `,v="vec4";e==="all"?(l="1.0",b=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,v="bvec4"):e==="any"&&(l="0.0",b=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,v="bvec4");let T="";s%r>0&&(T=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${l};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

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

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

          ${b}
        }

        int inIdx = inOffset + ${m};
        if (${y===1}) {
          ${v} values = ${v}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${b}
        } else if (${y===2}) {
          ${v} values = ${v}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${b}
        } else if (${y===3}) {
          ${v} values = ${v}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${b}
        }
        setOutput(${f});
      }
    `}}class JI{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t;let r="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),r+=`
        ${s}
        ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
          int flatIndex = getFlatIndex(thisRC);

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

          result[${o}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${o>0?"}":""}
      `}this.userCode=`
      ${fJ(e)}
      ${P1(t)}

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

        vec4 result = vec4(0.);

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

        ${r}

        setOutput(result);
      }
    `}}function fJ(n){let t=_a(["r","c","d"],n);return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t}
      return ivec3(r, c, d);
    }
  `}class dJ{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*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(${m});
        const float widthScale = float(${y});

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

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

        // 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 >= ${c}) {
            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 >= ${l}) {
              continue;
            }

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

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

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

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

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

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

        setOutput(accumulator);
      }
    `}}class mJ{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${f[0]/m[0]},
          ${f[1]/m[1]});
      const vec2 inputShapeRC = vec2(${c}.0, ${l}.0);

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

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

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

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

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

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

        setOutput(newValue);
      }
    `}}class gJ{constructor(t,e,r,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r];this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${f[0]/m[0]},
          ${f[1]/m[1]},
          ${f[1]/m[1]});
      const vec3 inputShapeRC = vec3(${c}.0, ${l}.0,
                                     ${l}.0);

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

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

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

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

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

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

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

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

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

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

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

        setOutput(newValue);
      }
    `}}class yJ{constructor(t,e,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;let[,o,s]=e.shape,[,c,l]=t.shape,p=[r&&c>1?o-1:o,r&&l>1?s-1:s],f=[r&&c>1?c-1:c,r&&l>1?l-1:l],m=p[0]/f[0],y=p[1]/f[1],b=1/m,v=1/y,T=Math.ceil(b)*2+2,N=Math.ceil(v)*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(${m});
        const float widthScale = float(${y});

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

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

        // 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 >= ${c}) {
            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 >= ${l}) {
              continue;
            }

            float sourceFracRow =
              float(${p[0]}) *
                (float(dyR) / float(${f[0]}));

            float sourceFracCol =
                float(${p[1]}) *
                  (float(dyC) / float(${f[1]}));

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

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

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

        setOutput(accumulator);
      }
    `}}class bJ{constructor(t,e,r,o){this.variableNames=["A"],this.outputShape=[];let[s,c,l,p]=t;this.outputShape=[s,e,r,p];let f=[o&&e>1?c-1:c,o&&r>1?l-1:l],m=[o&&e>1?e-1:e,o&&r>1?r-1:r],y=o?"0.5":"0.0";this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${f[0]/m[0]},
          ${f[1]/m[1]});
      const vec2 inputShapeRC = vec2(${c}.0, ${l}.0);

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

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

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

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

        setOutput(newValue);
      }
    `}}class xJ{constructor(t,e){this.variableNames=["x"];let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=t,r===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${t[0]} - coord - 1));
        }
      `;return}let o=l=>e.indexOf(l)!==-1&&t[l]!==1?`${t[l]} - coords[${l}] - 1`:`coords[${l}]`,s=t.map((l,p)=>o(p)).join(","),c=Oe(r);this.userCode=`
      void main() {
        ${c} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}}class wJ{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=t.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=t;let o=Jn("rc",r),s=`${o[r-1]} + 1 < ${this.outputShape[r-1]}`,c=`${o[r-2]} + 1 < ${this.outputShape[r-2]}`,l=Oe(r);r===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${t[0]} - rc - 1),
            ${t[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${t[0]} - (rc  + 1) - 1),
                ${t[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${l} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${p(o.slice())};
          if(${s}){
            result.g = ${f(o.slice())};
          }
          if(${c}) {
            result.b = ${m(o.slice())};
            if(${s}) {
              result.a = ${y(o.slice())};
            }
          }
          setOutput(result);
        }
    `;function p(T){return b(T)}function f(T){return T[r-1]="("+T[r-1]+" + 1)",b(T)}function m(T){return T[r-2]="("+T[r-2]+" + 1)",b(T)}function y(T){return T[r-1]="("+T[r-1]+" + 1)",T[r-2]="("+T[r-2]+" + 1)",b(T)}function b(T){let N=t.map((I,P)=>v(P,T)),S=N.join(","),D=N.slice(-2).join(",");return`getChannel(getX(${S}), vec2(${D}))`}function v(T,N){return e.indexOf(T)!==-1&&t[T]!==1?`${t[T]} - ${N[T]} - 1`:`${N[T]}`}}}class ZI{constructor(t,e,r,o,s,c,l=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=c;let p=Oe(s.length),f=Oe(c.length),m="";r===1?m="i":r===2&&(m="i, j");let y=`getIndices(${m})`,b="";o===1?b="i":o===2&&(b="i, coords[1]");let v=`getUpdates(${b})`,T=e>1?"strides[j]":"strides";this.userCode=`
        ${p} strides = ${p}(${s});

        void main() {
          ${f} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${t}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${e}; j++) {
              int index = round(${y});
              flattenedIndex += index * ${T};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${v};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}}class vJ{constructor(t,e){this.variableNames=["x","segmentIds"];let r=t.windowSize,o=t.batchSize,s=t.inSize,c=t.numSegments,l=c*Math.ceil(s/r);this.outputShape=[o,l];let p="0.0",f="sumValue",m=Math.floor(r/4)*4,y=r%4,b=`
        sumValue += dot(values, segFilter);
    `,v="";s%r>0&&(v=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);let T="";s%r>0&&(T=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${p};

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

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

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

        float sumValue = 0.0;

        for (int i = 0; i < ${m}; 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
          );

          ${b}
        }

        int inIdx = inOffset + ${m};
        if (${y===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
          );

          ${b}
        } else if (${y===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
          );

          ${b}
        } else if (${y===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
          );

          ${b}
        }
        setOutput(${f});
      }
    `}}class TJ{constructor(t,e,r){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)s="resRC",o="resRC";else{let l=["resRC.x","resRC.y","resRC.z","resRC.w"],p=[],f=[];for(let m=0;m<e.length;m++)f.push(`${l[m]}`),m<t&&p.push(`${l[m]}`);o=p.join(),s=f.join()}let c=Oe(r);this.userCode=`
      void main() {
        ${c} resRC = getOutputCoords();
        float cVal = getC(${o});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}}class kJ{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=`uniform int start[${this.rank}];`,o=NJ(this.rank),s,c=t.map((l,p)=>`sourceLoc.${M1[p]} = start[${p}] + coords.${M1[p]};`);s=`
        ${e} sourceLoc;
        ${e} coords = getOutputCoords();
        ${c.join(`
`)}
      `,this.userCode=`
      ${r}
      void main() {
        ${s}
        setOutput(getSource(${o}));
      }
    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}let M1=["x","y","z","w","u","v"];function NJ(n){if(n===1)return"sourceLoc";if(n<=6)return M1.slice(0,n).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${n} is not yet supported`)}class _J{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length;let e=Oe(this.rank),r=Jn("coords",this.rank),o=Jn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,c=`getChannel(getSource(${o.join()}), ${s})`,l=`
      result.x = ${c};
      if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
        ++${o[this.rank-1]};
        result.y = ${c};
        --${o[this.rank-1]};
      }
    `,p=this.rank===1?"":`
      --${r[this.rank-1]};
      if (++${r[this.rank-2]} < ${t[this.rank-2]}) {
        ++${o[this.rank-2]};
        result.z = ${c};
        if (++${r[this.rank-1]} < ${t[this.rank-1]}) {
          ++${o[this.rank-1]};
          result.w = ${c};
        }
      }
    `,f=this.rank<=4?`sourceLoc = coords +
            ${e}(${t.map((m,y)=>`start[${y}]`).join()});`:t.map((m,y)=>`${o[y]} = ${r[y]} + start[${y}];`).join(`
`);this.userCode=`
      uniform int start[${this.rank}];
      void main() {
        ${e} coords = getOutputCoords();
        ${e} sourceLoc;
        ${f}
        vec4 result = vec4(0.);
        ${l}
        ${p}
        setOutput(result);
      }
    `}getCustomSetupFunc(t){if(t.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${t.length})`);return(e,r)=>{if(this.startLoc==null&&(this.startLoc=e.getUniformLocationNoThrow(r,"start"),this.startLoc==null))return;e.gl.uniform1iv(this.startLoc,t)}}}class CJ{constructor(t,e,r){this.variableNames=["x"],this.outputShape=r;let o=r.length,s=Oe(r.length),c=Oe(r.length),l="";if(o===1)l="coords * strides + begin";else{let p=0;l=r.map((f,m)=>(p++,r.length===1?`coords * strides[${m}] + begin[${m}]`:`coords[${p-1}] * strides[${m}] + begin[${m}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${t});
      ${s} strides = ${s}(${e});

      void main() {
        ${c} coords = getOutputCoords();
        setOutput(getX(${l}));
      }
    `}}class SJ{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(t,e,r){let o=tE(e,r),s=eE(t,o,r);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let c=QI(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=c,this.log();let p=this.freeTextures[s].shift();return this.usedTextures[s].push(p),p}let l;return o===Ln.PACKED_2X2_FLOAT32?l=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Ln.PACKED_2X2_FLOAT16?l=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Ln.UNPACKED_FLOAT32?l=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Ln.UNPACKED_FLOAT16?l=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Ln.PACKED_4X1_UNSIGNED_BYTE&&(l=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(l),this.numUsedTextures++,this._numBytesAllocated+=c,this.log(),l}releaseTexture(t,e,r,o){if(this.freeTextures==null)return;let s=tE(r,o),c=eE(e,s,o);c in this.freeTextures||(this.freeTextures[c]=[]);let l=QI(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),p=ct().get("WEBGL_DELETE_TEXTURE_THRESHOLD");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(t),this._numBytesAllocated-=l):(this.freeTextures[c].push(t),this.numFreeTextures++,this._numBytesFree+=l),this.numUsedTextures--;let f=this.usedTextures[c],m=f.indexOf(t);if(m<0)throw new Error("Cannot release a texture that was never provided by this texture manager");f.splice(m,1),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures==null)return;for(let t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}function $J(n,t){let e=n;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===n.RGBA)return 16;if(t===e.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function QI(n,t,e,r,o){let s=IJ(t,r),c;if(o){let[p,f]=fl(n[0],n[1]);c=p*f}else{let[p,f]=lh(n[0],n[1]);c=p*f}let l=$J(e,s);return c*l}function IJ(n,t){switch(n){case Ln.PACKED_2X2_FLOAT32:return jI(t);case Ln.PACKED_2X2_FLOAT16:return KI(t);case Ln.UNPACKED_FLOAT32:return UI(t);case Ln.UNPACKED_FLOAT16:return qI(t);case Ln.PACKED_4X1_UNSIGNED_BYTE:return HI(t);default:throw new Error(`Unknown physical texture type ${n}`)}}function EJ(n){return ct().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?n?Ln.PACKED_2X2_FLOAT32:Ln.UNPACKED_FLOAT32:n?Ln.PACKED_2X2_FLOAT16:Ln.UNPACKED_FLOAT16}function tE(n,t){if(n===Br.UPLOAD)return Ln.PACKED_2X2_FLOAT32;if(n===Br.RENDER||n==null)return EJ(t);if(n===Br.DOWNLOAD||n===Br.PIXELS)return Ln.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${n}`)}function eE(n,t,e){return`${n[0]}_${n[1]}_${t}_${e}`}class DJ{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[c]*e[c];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=AJ(t);this.userCode=`
      void main() {
        ${o} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}}function AJ(n){let t=n.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${n[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let o=0;o<n.length;o++)r.push(`imod(${e[o]}, ${n[o]})`);return r.join()}class ue{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.userCode=`
      float unaryOperation(float x) {
        ${e}
      }

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

        setOutput(y);
      }
    `}}let $s="if (isnan(x)) return x;",FJ="return x;",nE="return abs(x);",rE=$s+`
  return (x < 0.0) ? 0.0 : x;
`,oE=$s+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,sE="return (x >= 0.0) ? x : (exp(x) - 1.0);",RJ=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${om};
  float scale = ${sm};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;function PJ(n=0){return $s+`
    return x > 0.0 ? 1.0 : float(${n});
  `}let iE="return -x;",aE="return ceil(x);",cE="return floor(x);",OJ=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,LJ="return float(isnan(x));",MJ="return float(isinf(x));",BJ="return float(!isnan(x) && !isinf(x));",zJ=`
  // 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;
    }
  }
`,lE="return exp(x);",uE="return exp(x) - 1.0;",WJ=`if (x < 0.0) return NAN;
  return log(x);`,VJ="return log(1.0 + x);",GJ="return sqrt(x);",UJ="return inversesqrt(x);",qJ="return 1.0 / (1.0 + exp(-1.0 * x));",HJ=`
  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;
`,jJ=$s+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,KJ=$s+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,XJ=$s+`
  return atan(x);
`,YJ=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,JJ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,ZJ=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,QJ=$s+"return log(x + sqrt(x * x + 1.0));",tZ=$s+`
  if (x < 1.0) return NAN;
  return log(x + sqrt(x * x - 1.0));`,eZ=$s+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,nZ=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${fv};
  float a1 = ${dv};
  float a2 = ${mv};
  float a3 = ${gv};
  float a4 = ${yv};
  float a5 = ${bv};

  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));
`,rZ="return 1.0 / x;",oZ="return float(!(x >= 1.0));",rg="return x;";let sZ="return x;",iZ=`
  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;
`,pE=`
  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;
`,hE=`
  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;
`,fE=`
  vec4 result;

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

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

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

        setOutput(y);
      }
    `}}class aZ{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t;let e=t.length,r=Jn("rc",e),o=Oe(e),s=YX(e,r),c=r.slice(-2),l=e<=1?"rc":`vec2(${c.join(",")})`;this.userCode=`
      void main() {
        ${o} rc = getOutputCoords();
        vec4 packedInput = getA(${s});

        setOutput(getChannel(packedInput, ${l}));
      }
    `}}let{segment_util:dE}=vv,cZ=Tv,lZ=kv,uZ=Nv,pZ=jd,hZ=1e-7,fZ=1e-4,og={};function dZ(n){return n in og||(og[n]={}),og[n]}function sg(n,t=!1){if(n==="linear")return t?sZ:FJ;if(n==="relu")return t?pE:rE;if(n==="elu")return t?fE:sE;if(n==="relu6")return t?hE:oE;if(n==="prelu")return t?LI:OI;throw new Error(`Activation ${n} has not been implemented for the WebGL backend.`)}let mZ=128,gZ=600;function yZ(){return ct().global.screen==null?1024:ct().global.screen.height*ct().global.screen.width*window.devicePixelRatio*gZ/1024/1024}let mE=1e3;class bZ extends d{constructor(t){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!ct().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(t==null){let e=Xo(ct().getNumber("WEBGL_VERSION"));this.binaryCache=dZ(ct().getNumber("WEBGL_VERSION")),this.gpgpu=new j7(e),this.canvas=e.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=t,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=t.gl.canvas;this.textureManager=new SJ(this.gpgpu),this.numMBBeforeWarning=yZ(),this.texData=new h(this,ds())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(t,e,r){if((ct().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||ct().getBool("DEBUG"))&&this.checkNumericalProblems(t),r==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={};return this.texData.set(o,{shape:e,dtype:r,values:t,usage:Br.UPLOAD,refCount:1,complexParentRefCount:0}),o}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,r,o){if(ct().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:r,dtype:o,values:e,usage:Br.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(t){let e=t.dataId;if(this.texData.has(e)){let r=this.texData.get(e);r.refCount--,r.refCount<1&&this.disposeData(e)}}readSync(t){let e=this.texData.get(t),{values:r,dtype:o,complexTensorInfos:s,slice:c,shape:l,isPacked:p}=e;if(c!=null){let b;p?b=new dh(l,rg):b=new ue(l,rg);let v=this.runWebGLProgram(b,[{dataId:t,shape:l,dtype:o}],o),T=this.readSync(v.dataId);return this.disposeIntermediateTensorInfo(v),T}if(r!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return r;let f=this.activeTimers!=null,m;f&&(m=sr());let y;if(o==="complex64"){let b=this.readSync(s.real.dataId),v=this.readSync(s.imag.dataId);y=ws(b,v)}else y=this.getValuesFromTexture(t);return f&&(this.downloadWaitMs+=sr()-m),this.convertAndCacheOnCPU(t,y)}async read(t){if(this.pendingRead.has(t)){let T=this.pendingRead.get(t);return new Promise(N=>T.push(N))}let e=this.texData.get(t),{values:r,shape:o,slice:s,dtype:c,complexTensorInfos:l,isPacked:p}=e;if(s!=null){let T;p?T=new dh(o,rg):T=new ue(o,rg);let N=this.runWebGLProgram(T,[{dataId:t,shape:o,dtype:c}],c),S=this.read(N.dataId);return this.disposeIntermediateTensorInfo(N),S}if(r!=null)return this.convertAndCacheOnCPU(t);if(!ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&ct().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let f=null,m;if(c!=="complex64"&&ct().get("WEBGL_BUFFER_SUPPORTED")){m=this.decode(t);let T=this.texData.get(m.dataId);f=this.gpgpu.createBufferFromTexture(T.texture,...uh(o))}this.pendingRead.set(t,[]),c!=="complex64"&&await this.gpgpu.createAndWaitForFence();let y;if(c==="complex64"){let T=await Promise.all([this.read(l.real.dataId),this.read(l.imag.dataId)]),N=T[0],S=T[1];y=ws(N,S)}else if(f==null)y=this.getValuesFromTexture(t);else{let T=G(o);y=this.gpgpu.downloadFloat32MatrixFromBuffer(f,T)}m!=null&&this.disposeIntermediateTensorInfo(m);let b=this.convertAndCacheOnCPU(t,y),v=this.pendingRead.get(t);return this.pendingRead.delete(t),v.forEach(T=>T(b)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t),this.pendingDeletes--),b}checkNumericalProblems(t){if(t==null)return;for(let e=0;e<t.length;e++){let r=t[e];if(!cX(r))throw ct().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:r,isPacked:o}=this.texData.get(t),s=G(e);if(ct().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let b=this.decode(t),v=this.texData.get(b.dataId),T=this.gpgpu.downloadMatrixFromPackedTexture(v.texture,...uh(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(b),T}let c=ct().getBool("WEBGL_PACK")&&o===!0,l=c?A1(e):e,p=c?new N7(l):new k7(l),f=this.runWebGLProgram(p,[{shape:l,dtype:r,dataId:t}],"float32"),m=this.texData.get(f.dataId),y=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(m.texture,m.texShape[0],m.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(f),y}async time(t){let e=this.activeTimers,r=[],o=!1;this.programTimersStack==null?(this.programTimersStack=r,o=!0):this.activeTimers.push(r),this.activeTimers=r,t();let s=tt(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),c=tt(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let l={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let p=await Promise.all(s);l.kernelMs=C(p),l.getExtraProfileInfo=()=>p.map((f,m)=>({name:c[m],ms:f})).map(f=>`${f.name}: ${f.ms}`).join(", ")}else l.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,l}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:sr(),endMs:null}}endTimer(t){return ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=sr(),t)}async getQueryTime(t){if(ct().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t){if(this.pendingDisposal.has(t))return;if(this.pendingRead.has(t)){this.pendingDisposal.add(t),this.pendingDeletes++;return}if(!this.texData.has(t))return;if(this.texData.get(t).complexParentRefCount>0){this.texData.get(t).refCount--;return}this.releaseGPUData(t);let{complexTensorInfos:e}=this.texData.get(t);e!=null&&(this.texData.get(e.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.real),this.texData.get(e.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(e.imag)),this.texData.delete(t)}releaseGPUData(t){let{texture:e,dtype:r,texShape:o,usage:s,isPacked:c,slice:l}=this.texData.get(t),p=l&&l.origDataId||t,f=this.dataRefCount.get(p);f>1?this.dataRefCount.set(p,f-1):(this.dataRefCount.delete(p),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,r),this.textureManager.releaseTexture(e,o,s,c)));let m=this.texData.get(t);m.texture=null,m.texShape=null,m.isPacked=!1,m.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture}getDataInfo(t){return this.texData.get(t)}getCPUBackend(){return ct().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=ds().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(t,e=mZ){let r=this.getCPUBackend();return!this.warnedAboutCPUBackend&&r==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. 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y.reshape(r)}gatherND(t,e){let r=e.shape,o=r[r.length-1],[s,c,l,p]=dd(t,e),f=e.reshape([c,o]),m=t.reshape([t.size/l,l]),y=new E7(o,p,[c,l]),b=this.compileAndRun(y,[m,f]);return b.reshape(s)}fill(t,e,r){if(r=r||yc(e),r==="string"){let o=or(r,G(t));return o.fill(e),ds().makeTensor(o,t,r,this)}else{let o=new S7(t,e),s=o.getCustomSetupFunc(e);return this.compileAndRun(o,[],r,s)}}onesLike(t){if(t.dtype==="string")throw new Error("onesLike is not supported under string dtype");return this.fill(t.shape,1,t.dtype)}zerosLike(t){return this.fill(t.shape,t.dtype==="string"?"":0,t.dtype)}linspace(t,e,r){return wv(t,e,r)}makeTensorInfo(t,e,r){let o=this.write(r,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,r){let{dataId:o}=this.makeTensorInfo(t,e,r);return ds().makeTensorFromDataId(o,t,e,this)}unpackTensor(t){let e=new aZ(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new iJ(t.shape),r=!0;return this.runWebGLProgram(e,[t],t.dtype,null,r)}packedReshape(t,e){let r=[dl(t.shape),...ml(t.shape)],o={dtype:t.dtype,shape:r,dataId:t.dataId},s=[dl(e),...ml(e)],c=new JI(s,r),l=!0,p=this.runWebGLProgram(c,[o],t.dtype,null,l);return{dataId:p.dataId,shape:e,dtype:p.dtype}}decode(t){let e=this.texData.get(t),{isPacked:r,shape:o,dtype:s}=e,c=A1(o),l;r?l=new w7(c):l=new x7(c);let p=!0,f=this.runWebGLProgram(l,[{shape:c,dtype:s,dataId:t}],s,null,p);return{dtype:s,shape:o,dataId:f.dataId}}runWebGLProgram(t,e,r,o,s=!1){let c=this.makeTensorInfo(t.outputShape,r),l=this.texData.get(c.dataId);if(t.packedOutput&&(l.isPacked=!0),t.outPackingScheme===ch.DENSE){let N=uh(t.outputShape);l.texShape=N.map(S=>S*2)}if(t.outTexUsage!=null&&(l.usage=t.outTexUsage),G(c.shape)===0)return l.values=Ce(c.dtype,0),c;let p=[],f=e.map(N=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let S=this.texData.get(N.dataId);if(S.texture==null){if(!t.packedInputs&&G(N.shape)<=ct().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:N.shape,texData:null,isUniform:!0,uniformValues:S.values};t.packedInputs&&(S.isPacked=!0,S.shape=N.shape)}else if(!!S.isPacked!==!!t.packedInputs)N=S.isPacked?this.unpackTensor(N):this.packTensor(N),p.push(N),S=this.texData.get(N.dataId);else if(S.isPacked&&!Zm(S.shape,N.shape)){let D=N,I=N.shape;N.shape=S.shape,N=this.packedReshape(N,I),p.push(N),S=this.texData.get(N.dataId),D.shape=I}return this.uploadToGPU(N.dataId),{shape:N.shape,texData:S,isUniform:!1}});this.uploadToGPU(c.dataId);let m={shape:c.shape,texData:l,isUniform:!1},y=J7(t,f,m),b=this.getAndSaveBinary(y,()=>X7(this.gpgpu,t,f,m)),v=this.activeTimers!=null,T;if(v&&(T=this.startTimer()),Y7(this.gpgpu,b,f,m,o),p.forEach(N=>this.disposeIntermediateTensorInfo(N)),v&&(T=this.endTimer(T),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(T)})),!ct().getBool("WEBGL_LAZILY_UNPACK")&&l.isPacked&&s===!1){let N=this.unpackTensor(c);return this.disposeIntermediateTensorInfo(c),N}return c}compileAndRun(t,e,r,o,s=!1){r=r||e[0].dtype;let c=this.runWebGLProgram(t,e,r,o,s);return ds().makeTensorFromDataId(c.dataId,c.shape,c.dtype)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){if(this.disposed)return;if(!ct().getBool("IS_TEST")){let t=Object.keys(this.binaryCache);t.forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})}this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=rt(()=>{if(!ct().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=ct().getBool("DEBUG");ct().set("DEBUG",!1);let e=this.abs(Et(1e-8)).dataSync()[0];if(ct().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hZ:fZ}uploadToGPU(t){let e=this.texData.get(t),{shape:r,dtype:o,values:s,texture:c,usage:l,isPacked:p}=e;if(c!=null)return;let f=this.activeTimers!=null,m;f&&(m=sr());let y=e.texShape;if(y==null&&(y=CX(r,p),e.texShape=y),s!=null){let b=A1(r),v,T=y[1],N=y[0],S=s instanceof Uint8Array;p?([T,N]=fl(y[0],y[1]),v=new C7(b,[N,T],S)):v=new _7(b,[N,T],S);let D=this.makeTensorInfo([N,T],o);S?this.texData.get(D.dataId).usage=Br.PIXELS:this.texData.get(D.dataId).usage=Br.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(D.dataId),T,N,s);let I=!0,P=this.runWebGLProgram(v,[D],o,null,I),E=this.texData.get(P.dataId);e.texture=E.texture,e.texShape=E.texShape,e.isPacked=E.isPacked,e.usage=E.usage,this.disposeIntermediateTensorInfo(D),this.texData.delete(P.dataId),e.values=null,f&&(this.uploadWaitMs+=sr()-m)}else{let b=this.acquireTexture(y,l,o,p);e.texture=b}}convertAndCacheOnCPU(t,e){let r=this.texData.get(t),{dtype:o}=r;return this.releaseGPUData(t),e!=null&&(r.values=xZ(e,o)),r.values}acquireTexture(t,e,r,o){if(this.numBytesInGPU+=this.computeBytes(t,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(t,e,o)}computeBytes(t,e){return t[0]*t[1]*Zb(e)}tryRunOnCpuOrThrow(t,e){if(this.shouldExecuteOnCPU(t))try{return e()}catch(r){if(ct().getBool("IS_TEST"))throw new Error("CPU forwarding failed")}return null}}function xZ(n,t){if(t==="float32"||t==="complex64")return n;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(n.length):new Uint8Array(n.length);for(let r=0;r<e.length;++r)e[r]=Math.round(n[r]);return e}else throw new Error(`Unknown dtype ${t}`)}let wZ="2.7.0";function vZ(){ct().set("WEBGL_FORCE_F16_TEXTURES",!0)}Jx()&&bw("webgl",()=>new bZ,2);let tit={forceHalfFloat:vZ};function Is(n){let{inputs:t,backend:e}=n,{x:r}=t;return e.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}let TZ={kernelName:Nu,backendName:"webgl",kernelFunc:Is};function wl(n){let{inputs:t,backend:e}=n,{real:r,imag:o}=t,s=e.makeTensorInfo(r.shape,"complex64"),c=e.texData.get(s.dataId),l=Is({inputs:{x:r},backend:e}),p=e.texData.get(l.dataId);p.complexParentRefCount++;let f=Is({inputs:{x:o},backend:e}),m=e.texData.get(f.dataId);return m.complexParentRefCount++,c.complexTensorInfos={real:l,imag:f},s}let kZ={kernelName:Sf,backendName:"webgl",kernelFunc:wl};let gE="if (isnan(x)) return x;",NZ=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,_Z=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;function ig(n){return({inputs:t,backend:e})=>{let{x:r}=t,o=e,s=new ue(r.shape,n);return o.runWebGLProgram(s,[r],r.dtype)}}function vl({opSnippet:n,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:r=!1,cpuKernelImpl:o,dtype:s}){return({inputs:c,backend:l})=>{let{a:p,b:f}=c,m=l;if(r&&p.dtype==="complex64"){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=[[T.complexTensorInfos.real,N.complexTensorInfos.real],[T.complexTensorInfos.imag,N.complexTensorInfos.imag]].map(P=>{let[E,L]=P,B={dataId:E.dataId,dtype:E.dtype,shape:p.shape},q={dataId:L.dataId,dtype:L.dtype,shape:f.shape},H=new Hn(n,p.shape,f.shape);return m.runWebGLProgram(H,[B,q],Kn(E.dtype,L.dtype))}),I=wl({inputs:{real:S,imag:D},backend:m});return m.disposeIntermediateTensorInfo(S),m.disposeIntermediateTensorInfo(D),I}let y=s||Kn(p.dtype,f.dtype);if(m.shouldExecuteOnCPU([p,f])&&o!=null){let T=m.texData.get(p.dataId),N=m.texData.get(f.dataId),[S,D]=o(p.shape,f.shape,T.values,N.values,y),I=m.makeTensorInfo(D,y),P=m.texData.get(I.dataId);return P.values=S,I}let b=ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,v;return b?v=new Ss(t,p.shape,f.shape,e):v=new Hn(n,p.shape,f.shape),m.runWebGLProgram(v,[p,f],y)}}let yE="return a + b;",CZ=vl({opSnippet:yE,packedOpSnippet:yE,supportsComplex:!0,cpuKernelImpl:PX}),SZ={kernelName:Zi,backendName:"webgl",kernelFunc:CZ};let $Z=NZ+`
  return atan(a, b);
`,IZ=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+_Z+`
  return result;
`,EZ=vl({opSnippet:$Z,packedOpSnippet:IZ}),DZ={kernelName:Nf,backendName:"webgl",kernelFunc:EZ};function AZ(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;ph(o,"avgPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Xn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Is({inputs:{x:o},backend:e});let y=new fh(m,"avg",!1);return e.runWebGLProgram(y,[o],"float32")}let FZ={kernelName:fu,backendName:"webgl",kernelFunc:AZ};function RZ(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s}=t,c=s;ph([o,s],"avgPoolBackprop");let{filterSize:l,strides:p,pad:f}=r,m=Xn(c.shape,l,p,1,f),y=new RY(m);return e.runWebGLProgram(y,[o],c.dtype)}let PZ={kernelName:_f,backendName:"webgl",kernelFunc:RZ};class OZ{constructor(t,e,r,o,s,c){this.outputShape=[],this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="0.0";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="1.0";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${l};
        float scale = ${p};
        float inv = scale * inversesqrt(variance + float(${c}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}}class LZ{constructor(t,e,r,o,s,c){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],le(t,e),le(t,r);let l="vec4(0.0)";o!=null&&(le(t,o),this.variableNames.push("offset"),l="getOffsetAtOutCoords()");let p="vec4(1.0)";s!=null&&(le(t,s),this.variableNames.push("scale"),p="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
      void main() {
        vec4 offset = ${l};
        vec4 scale = ${p};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}}let MZ=({inputs:n,backend:t,attrs:e})=>{let{x:r,mean:o,variance:s,offset:c,scale:l}=n;_(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||o.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:p}=e;p==null&&(p=.001);let f=[r,o,s],m=null;c!=null&&(m=c.shape,f.push(c));let y=null;l!=null&&(y=l.shape,f.push(l));let b=ct().getBool("WEBGL_PACK_NORMALIZATION")?new LZ(r.shape,o.shape,s.shape,m,y,p):new OZ(r.shape,o.shape,s.shape,m,y,p),v=t.runWebGLProgram(b,f,f[0].dtype);return v},BZ={kernelName:ku,backendName:"webgl",kernelFunc:MZ};let zZ="return float(a != b);",bE=vl({opSnippet:zZ,dtype:"bool"}),WZ={kernelName:Fu,backendName:"webgl",kernelFunc:bE};function B1(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Is({inputs:{x:o.complexTensorInfos.real},backend:e})}let VZ={kernelName:Hf,backendName:"webgl",kernelFunc:B1};let GZ="return float(int(x));";function UZ(n,t){let e=new ue(n.shape,GZ),r=t.runWebGLProgram(e,[n],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function z1(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{dtype:s}=r;if(s==="complex64"){if(o.dtype==="complex64")return Is({inputs:{x:o},backend:e});let c=xe(o.shape),l=z1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),p=wl({inputs:{real:l,imag:c},backend:e});return c.dispose(),e.disposeIntermediateTensorInfo(l),p}if(o.dtype==="complex64"){let c=B1({inputs:{input:o},backend:e}),l=z1({inputs:{x:c},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(c),l}if(!gc(o.dtype,s)){let c=Is({inputs:{x:o},backend:e});return{dataId:c.dataId,shape:c.shape,dtype:s}}if(s==="int32")return UZ(o,e);if(s==="bool"){let c=e.makeTensorInfo([],"bool",Ce("bool",1)),l={a:o,b:c},p=bE({inputs:l,backend:e});return e.disposeIntermediateTensorInfo(c),p}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}let qZ={kernelName:xc,backendName:"webgl",kernelFunc:z1};class HZ{constructor(t){this.outputShape=[],this.outputShape=ms(t,1),this.variableNames=t.map((c,l)=>`T${l}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let c=1;c<e.length;c++)e[c]=e[c-1]+t[c][1];let r=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let c=1;c<e.length;c++){let l=e[c-1];r.push(`else if (yC < ${e[c]}) setOutput(getT${c}(yR, yC-${l}));`)}let o=e.length,s=e[e.length-1];r.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${r.join(`
        `)}
      }
    `}}class jZ{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=ms(t,e);let r=this.outputShape,o=r.length,s=Oe(o),c=Jn("coords",o),l=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((N,S)=>`T${S}`);let p=new Array(t.length-1);p[0]=t[0][e];for(let N=1;N<p.length;N++)p[N]=p[N-1]+t[N][e];let f=l[e],m=l.slice(-2),y=l.join(),b=`if (${f} < ${p[0]}) {
        return getChannel(
            getT0(${y}), vec2(${m.join()}));
        }`;for(let N=1;N<p.length;N++){let S=p[N-1];b+=`
        if (${f} < ${p[N]}  && ${f} >= ${p[N-1]}) {
          return getChannel(
            getT${N}(${ag(l,f,S)}),
            vec2(${ag(m,f,S)}));
        }`}let v=p.length,T=p[p.length-1];b+=`
        return getChannel(
          getT${v}(${ag(l,f,T)}),
          vec2(${ag(m,f,T)}));`,this.userCode=`
      float getValue(${l.map(N=>"int "+N)}) {
        ${b}
      }

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

        ${c[o-1]} = ${c[o-1]} + 1;
        if (${c[o-1]} < ${r[o-1]}) {
          result.g = getValue(${c});
        }

        ${c[o-2]} = ${c[o-2]} + 1;
        if (${c[o-2]} < ${r[o-2]}) {
          result.a = getValue(${c});
        }

        ${c[o-1]} = ${c[o-1]} - 1;
        if (${c[o-2]} < ${r[o-2]} &&
            ${c[o-1]} < ${r[o-1]}) {
          result.b = getValue(${c});
        }
        setOutput(result);
      }
    `}}function ag(n,t,e){let r=n.indexOf(t),o=n.map((s,c)=>c===r?`${s} - ${e}`:s);return o.join()}function xE(n){let{inputs:t,backend:e}=n,{input:r}=t,o=e.texData.get(r.dataId);return Is({inputs:{x:o.complexTensorInfos.imag},backend:e})}let KZ={kernelName:Mf,backendName:"webgl",kernelFunc:xE};function XZ(n,t,e){let r=[dl(n.shape),...ml(n.shape)],o={dtype:n.dtype,shape:r,dataId:n.dataId},s=[dl(t),...ml(t)],c=new JI(s,r),l=!0,p=e.runWebGLProgram(c,[o],n.dtype,null,l);return{dataId:p.dataId,shape:t,dtype:p.dtype}}function Es(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t,{shape:s}=r,c=e,l=G(o.shape),p=Ue(s,l),f=G(p);_(l===f,()=>`The new shape (${p}) has ${f} elements and the old shape (${o.shape}) has ${l} elements. The new shape and old shape must have the same number of elements.`);let m=c.texData.get(o.dataId);return m.isPacked&&!Zm(o.shape,p)&&!(m.texture!==null&&Zm(m.shape,p))?XZ(o,p,c):(c.incRef(o.dataId),{dataId:o.dataId,shape:p,dtype:o.dtype})}let YZ={kernelName:Ou,backendName:"webgl",kernelFunc:Es};function Tl(n,t,e){let r=n[0].dtype;if(r==="complex64"){let f=n.map(T=>B1({inputs:{input:T},backend:e})),m=n.map(T=>xE({inputs:{input:T},backend:e})),y=Tl(f,t,e),b=Tl(m,t,e),v=wl({inputs:{real:y,imag:b},backend:e});return f.forEach(T=>e.disposeIntermediateTensorInfo(T)),m.forEach(T=>e.disposeIntermediateTensorInfo(T)),e.disposeIntermediateTensorInfo(y),e.disposeIntermediateTensorInfo(b),v}if(n.length>ct().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let f=Math.floor(n.length/2),m=Tl(n.slice(0,f),t,e),y=Tl(n.slice(f),t,e),b=Tl([m,y],t,e);return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(y),b}if(ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&n[0].shape.length>1){let f=new jZ(n.map(m=>m.shape),t);return e.runWebGLProgram(f,n,r)}let o=ms(n.map(f=>f.shape),t),s=n.map(f=>Es({inputs:{x:f},attrs:{shape:[-1,G(f.shape.slice(t))]},backend:e})),c=new HZ(s.map(f=>f.shape)),l=e.runWebGLProgram(c,s,r);s.forEach(f=>e.disposeIntermediateTensorInfo(f));let p=Es({inputs:{x:l},attrs:{shape:o},backend:e});return e.disposeIntermediateTensorInfo(l),p}function JZ(n){let{inputs:t,backend:e,attrs:r}=n,{axis:o}=r,s=Vt(o,t[0].shape)[0],c=ms(t.map(f=>f.shape),s);if(G(c)===0)return e.makeTensorInfo(c,t[0].dtype,[]);let l=t.filter(f=>G(f.shape)>0);if(l.length===1)return l[0];let p=l.map(f=>f.shape);return Nd(p,s),Tl(l,s,e)}let ZZ={kernelName:gu,backendName:"webgl",kernelFunc:JZ};let QZ=gE+`
  return cos(x);
`,t9=ig(QZ),e9={kernelName:wc,backendName:"webgl",kernelFunc:t9};let n9=`
if (a == b) {
  return 1.0;
};
return a / b;`,r9=`
  // 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;
`,o9=vl({opSnippet:n9,packedOpSnippet:r9,checkOutOfBounds:!0}),s9={kernelName:vc,backendName:"webgl",kernelFunc:o9};class wE{constructor(t,e,r){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,c=r?`${o}.0`:"1.0",l;if(t==="real")l="return real * expR - imag * expI;";else if(t==="imag")l="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
      const float exponentMultiplier = ${s};

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

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

        float result = 0.0;

        for (int i = 0; i < ${o}; 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) / ${c};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}}function vE(n,t,e){let r=e.texData.get(n.dataId),o=G(n.shape),s=n.shape[n.shape.length-1],c=o/s,l=Es({inputs:{x:n},backend:e,attrs:{shape:[c,s]}}),p=l.shape,f=new wE("real",p,t),m=new wE("imag",p,t),y=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:p},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:p}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=wl({inputs:{real:b,imag:v},backend:e});e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v);let N=Es({inputs:{x:T},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(N),N}function i9(n){let{inputs:t,backend:e}=n,{input:r}=t;return vE(r,!1,e)}let a9={kernelName:Pf,backendName:"webgl",kernelFunc:i9};class c9{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${e} - x;
          float outputValue;
          if(coordX >= 0 && coordX < ${e}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}}let l9={kernelName:Of,backendName:"webgl",kernelFunc:({inputs:n,backend:t})=>{let{image:e}=n,r=t,o=new c9(e.shape),s=r.runWebGLProgram(o,[e],e.dtype);return s}};class u9{constructor(t){this.variableNames=["A"];let e=Zn(),[r,o]=t;this.outputShape=t,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${r}.0);

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

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}}class p9{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Zn(),[r,o]=t;this.outputShape=t,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(${o}.0, ${r}.0);
            vec4 values = ${e.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);
          }
        }

        ${e.output} = result;
      }
    `}}let h9={kernelName:Jf,backendName:"webgl",kernelFunc:f9},kl;function f9(n){let{inputs:t,backend:e,attrs:r}=n,{pixels:o}=t,{numChannels:s}=r,c=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,l=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[p,f]=c?[o.videoWidth,o.videoHeight]:[o.width,o.height],m=[f,p],y=[f,p,s];(l||c)&&(kl==null&&(kl=document.createElement("canvas").getContext("2d")),kl.canvas.width=p,kl.canvas.height=f,kl.drawImage(o,0,0,p,f),o=kl.canvas);let b=e.makeTensorInfo(m,"int32");e.texData.get(b.dataId).usage=Br.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(b.dataId),o);let v=ct().getBool("WEBGL_PACK")?new p9(y):new u9(y),T=e.runWebGLProgram(v,[b],"int32");return e.disposeData(b.dataId),T}function d9(n){let{inputs:t,backend:e}=n,{input:r}=t;return vE(r,!0,e)}let m9={kernelName:Lf,backendName:"webgl",kernelFunc:d9};class TE{constructor(t,e){this.variableNames=["x"];let{windowSize:r,batchSize:o,inSize:s,outSize:c}=t;this.outputShape=[o,c];let l=Math.floor(r/4)*4,p=r%4,f="sumValue += dot(values, ones);";if(e!=null){let y=1/e;f=`sumValue += dot(values * ${gt(y)?y.toPrecision(2):y}, ones);`}let m="";s%r>0&&(m=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

        float sumValue = 0.0;

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

          ${f}
        }

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

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

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

          ${f}
        }
        setOutput(sumValue);
      }
    `}}function g9(n){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:n[1],r=pp(e);t.push({inSize:e,windowSize:r,outSize:Math.ceil(e/r)})}return t}function kE(n,t,e,r){let o=g9(n.shape),s=n;for(let c=0;c<o.length;c++){let{inSize:l,windowSize:p,outSize:f}=o[c],m,y;e==="mean"?m=c===0?new TE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},l):new TE({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f}):m=new YI({windowSize:p,inSize:l,batchSize:n.shape[0],outSize:f},e),y=s,s=r.runWebGLProgram(m,[s],t),y.dataId!==n.dataId&&r.disposeIntermediateTensorInfo(y)}return s}function y9(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Es({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=kE(l,n.dtype,"max",r),f=Es({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}class b9{constructor(t,e){this.variableNames=["A"];let r=new Array(t.length);for(let c=0;c<r.length;c++)r[c]=t[e[c]];this.outputShape=r,this.rank=r.length;let o=Oe(this.rank),s=x9(e);this.userCode=`
    void main() {
      ${o} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}}function x9(n){let t=n.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let o=0;o<n.length;o++)r[n[o]]=e[o];return r.join()}class w9{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(t.length);for(let m=0;m<r.length;m++)r[m]=t[e[m]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Oe(this.rank),s=EI("rc",this.rank),c=new Array(this.rank);for(let m=0;m<e.length;m++)c[e[m]]=s[m];let l=`vec2(${c.slice(-2).join()})`,p=`++${s[this.rank-1]} < ${r[this.rank-1]}`,f=`getChannel(getA(${c.join()}), ${l})`;this.userCode=`
    void main() {
      ${o} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${f};
      if(${p}) {
        result[1] = ${f};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${r[this.rank-2]}) {
        result[2] = ${f};
        if(${p}) {
          result[3] = ${f};
        }
      }
      setOutput(result);
    }
    `}}function W1(n,t,e){let r=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new w9(n.shape,t):new b9(n.shape,t);return e.runWebGLProgram(r,[n],n.dtype)}let v9={kernelName:Eu,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{reductionIndices:o,keepDims:s}=t,c=e,l=r.shape.length,p=Vt(o,r.shape),f=p,m=ar(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=r;if(y){if(b){let I=c.texData.get(v.dataId),P=I.values,E=new Array(l);for(let q=0;q<E.length;q++)E[q]=r.shape[m[q]];let L=R1(P,r.shape,r.dtype,m,E);v=c.makeTensorInfo(E,r.dtype);let B=c.texData.get(v.dataId);B.values=L}else v=W1(r,m,c);f=xr(f.length,l)}ir("max",f,l);let[T,N]=Rn(v.shape,f),S=T;s&&(S=Pn(T,p));let D;if(b){let I=c.texData.get(v.dataId),P=I.values,E=WX(P,G(N),S,r.dtype);D=c.makeTensorInfo(S,r.dtype);let L=c.texData.get(D.dataId);L.values=E}else D=y9(v,N,S,c);return y&&c.disposeIntermediateTensorInfo(v),D}};function T9(n){let{inputs:t,backend:e,attrs:r}=n,{x:o}=t;ph(o,"maxPool");let{filterSize:s,strides:c,pad:l,dimRoundingMode:p}=r,f=1;_(fn(c,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${c} and dilations '${f}'`);let m=Xn(o.shape,s,c,f,l,p);if(m.filterWidth===1&&m.filterHeight===1&&lt(m.inShape,m.outShape))return Is({inputs:{x:o},backend:e});let y=new fh(m,"max",!1);return e.runWebGLProgram(y,[o],o.dtype)}let k9={kernelName:Du,backendName:"webgl",kernelFunc:T9};function N9(n){let{inputs:t,backend:e,attrs:r}=n,{dy:o,input:s,output:c}=t,l=s;ph([s,c],"maxPoolBackprop");let{filterSize:p,strides:f,pad:m,dimRoundingMode:y}=r,b=Xn(l.shape,p,f,1,m,y),v=!0,T=new fh(b,"max",v),N=e.runWebGLProgram(T,[l],l.dtype),S=new nJ(b),D=e.runWebGLProgram(S,[o,N],l.dtype);return e.disposeIntermediateTensorInfo(N),D}let _9={kernelName:zf,backendName:"webgl",kernelFunc:N9};function C9(n,t,e,r){let o=new fh(e,"max",!1),s=r.runWebGLProgram(o,[n],"float32");o=new fh(e,"max",!0,!0,t);let c=r.runWebGLProgram(o,[n],"float32");return[s,c]}let S9={kernelName:Wf,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{filterSize:o,strides:s,pad:c,includeBatchInIndex:l}=t,p=e;_(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let f=[1,1];_(fn(s,f),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${f}'`);let m=Xn(r.shape,o,s,f,c),[y,b]=C9(r,l,m,p);return[y,b]}};function $9(n,t,e,r){let o=G(t),s=G(n.shape),c=s/o,l=Es({inputs:{x:n},attrs:{shape:[c,o]},backend:r}),p=kE(l,"float32","mean",r),f=Es({inputs:{x:p},attrs:{shape:e},backend:r});return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),f}let I9={kernelName:kx,backendName:"webgl",kernelFunc:({inputs:n,attrs:t,backend:e})=>{let{x:r}=n,{keepDims:o,axis:s}=t,c=e,l=r.shape.length,p=Vt(s,r.shape),f=p,m=ar(f,l),y=m!=null,b=c.shouldExecuteOnCPU([r]),v=[],T=r;if(y){if(b){let P=c.texData.get(T.dataId),E=P.values,L=new Array(l);for(let H=0;H<L.length;H++)L[H]=r.shape[m[H]];let B=R1(E,r.shape,r.dtype,m,L);T=c.makeTensorInfo(L,r.dtype);let q=c.texData.get(T.dataId);q.values=B}else T=W1(r,m,c);v.push(T),f=xr(f.length,l)}ir("sum",f,l);let[N,S]=Rn(T.shape,f),D=N;o&&(D=Pn(N,p));let I=$9(T,S,D,c);for(let P of v)c.disposeIntermediateTensorInfo(P);return I}};class E9{constructor(t,e,r){this.variableNames=["x"],this.outputShape=e.map((m,y)=>m[0]+t[y]+m[1]);let o=t.length,s=Oe(o),c=e.map(m=>m[0]).join(","),l=e.map((m,y)=>m[0]+t[y]).join(","),p=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),f=r==="reflect"?0:1;if(o===1){this.userCode=`
        int start = ${c};
        int end = ${l};

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

      void main() {
        ${s} outC = getOutputCoords();
        for (int i = 0; i < ${o}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${f};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${f};
          }
        }
        ${s} coords = outC - start;
        setOutput(getX(${p}));
      }
    `}}class D9{constructor(t,e,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((T,N)=>T[0]+t[N]+T[1]);let o=t.length,s=Oe(o),c=e.map(T=>T[0]).join(","),l=e.map((T,N)=>T[0]+t[N]).join(","),p=Jn("rc",o),f=Jn("source",o),m=`${p[o-1]} < ${this.outputShape[o-1]}`,y=o===1?"source":`vec2(${f.slice(-2).join()})`,b=r==="reflect"?0:1,v="";if(o===1){let T=`
        ${s} source = rc;
        if (source < start) {
          source = start * 2 - source - ${b};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${b};
        }
        source -= start;
      `;v=`
        ${s} rc = outputLoc;
        ${T}
        result[0] = getChannel(getX(${f.join()}), ${y});
        ${p[o-1]} += 1;
        if(${m}) {
          ${T}
          result[1] = getChannel(getX(${f.join()}), ${y});
        }
      `}else{let T=`
        ${s} source = rc;
        ${s} lt = ${s}(lessThan(source, start));
        ${s} gte = ${s}(greaterThanEqual(source, end));
        ${s} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${b}) +
                gte * ((end - 1) * 2 - source + ${b});
        source -= start;
      `;v=`
        ${s} rc = outputLoc;
        ${T}
        result[0] = getChannel(getX(${f.join()}), ${y});
        ${p[o-1]} += 1;
        if(${m}) {
          ${T}
          result[1] = getChannel(getX(${f.join()}), ${y});
        }
        rc = outputLoc;
        ${p[o-2]} += 1;
        if(${p[o-2]} < ${this.outputShape[o-2]}) {
          ${T}
          result[2] = getChannel(getX(${f.join()}), ${y});
          ${p[o-1]} += 1;
          if(${m}) {
            ${T}
            result[3] = getChannel(getX(${f.join()}), ${y});
          }
        }
      `}this.userCode=`
      const ${s} start = ${s}(${c});
      const ${s} end = ${s}(${l});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${v}
        setOutput(result);
      }
    `}}let A9=({inputs:n,backend:t,attrs:e})=>{let{x:r}=n,{paddings:o,mode:s}=e,c=ct().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new D9(r.shape,o,s):new E9(r.shape,o,s),l=t.runWebGLProgram(c,[r],r.dtype);return l},F9={kernelName:Au,backendName:"webgl",kernelFunc:A9};let NE={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"};class _E{constructor(t,e,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=le(e,r),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${t}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}}let CE="return a * b;";function R9(n){let{inputs:t,backend:e}=n,{a:r,b:o}=t,s=Kn(r.dtype,o.dtype);if(r.dtype==="complex64"){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),f=new _E(NE.REAL,r.shape,o.shape),m=new _E(NE.IMAG,r.shape,o.shape),y=[{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},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}],b=e.runWebGLProgram(f,y,"float32"),v=e.runWebGLProgram(m,y,"float32"),T=wl({inputs:{real:b,imag:v},backend:e});return e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(v),T}if(e.shouldExecuteOnCPU([r,o])){let l=e.texData.get(r.dataId),p=e.texData.get(o.dataId),[f,m]=VX(r.shape,o.shape,l.values,p.values,s),y=e.makeTensorInfo(m,s),b=e.texData.get(y.dataId);return b.values=f,y}let c;return ct().getBool("WEBGL_PACK_BINARY_OPERATIONS")?c=new Ss(CE,r.shape,o.shape):c=new Hn(CE,r.shape,o.shape),e.runWebGLProgram(c,[r,o],s)}let P9={kernelName:Tc,backendName:"webgl",kernelFunc:R9};let O9={kernelName:$x,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Yc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l}=e,p=t,f=p.readSync(r.dataId),m=p.readSync(o.dataId),y=s,b=c,v=l;return tm(f,m,y,b,v)}};let L9=em,M9={kernelName:Vf,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Yc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),{selectedIndices:b,validOutputs:v}=L9(m,y,s,c,l,p);return[b,v]}};let B9=nm,z9={kernelName:Gf,backendName:"webgl",kernelFunc:({inputs:n,backend:t,attrs:e})=>{Yc("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{boxes:r,scores:o}=n,{maxOutputSize:s,iouThreshold:c,scoreThreshold:l,softNmsSigma:p}=e,f=t,m=f.readSync(r.dataId),y=f.readSync(o.dataId),b=s,v=c,T=l,N=p,{selectedIndices:S,selectedScores:D}=B9(m,y,b,v,T,N);return[S,D]}};class W9{constructor(t,e,r,o){this.variableNames=["Image"],this.outputShape=[];let s=t[1],c=t[2],l=Math.sin(e).toFixed(3),p=Math.cos(e).toFixed(3);this.outputShape=t;let[f,m]=uv(o,s,c),y=f.toFixed(3),b=m.toFixed(3),v="";typeof r=="number"?v=`float outputValue = ${r.toFixed(2)};`:v=`
        vec3 fill = vec3(${r.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
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d=++this.pendingBackendInitId,g=h.then(x=>d<this.pendingBackendInitId?!1:(this.registry[a]=x,this.pendingBackendInit=null,!0)).catch(x=>(d<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${a} failed`),console.warn(x.stack||x.message)),!1));return this.pendingBackendInit=g,{success:g,asyncInit:!0}}else return this.registry[a]=h,{success:!0,asyncInit:!1}}catch(h){return console.warn(`Initialization of backend ${a} failed`),console.warn(h.stack||h.message),{success:!1,asyncInit:!1}}}removeBackend(a){if(!(a in this.registryFactory))throw new Error(`${a} backend not found in registry`);this.backendName===a&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,a in this.registry&&(this.disposeRegisteredKernels(a),this.registry[a].dispose(),delete this.registry[a]),delete this.registryFactory[a],this.backendName===a&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((a,u)=>this.registryFactory[u].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let a=this.getSortedBackends();for(let u=0;u<a.length;u++){let h=a[u],{success:d,asyncInit:g}=this.initializeBackend(h);if(g||d)return{name:h,asyncInit:g}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(a,u){let h=this.state.tensorInfo.get(u),d=h.backend,g=this.readSync(u);d.disposeData(u),h.backend=a,a.move(u,g,h.shape,h.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(a,u){let h=null;if(u==null){if(typeof a!="function")throw new Error("Please provide a function to 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this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(a,u,h){let d=this.backend.numDataIds(),g=0;h.forEach(k=>{g+=k.dtype==="complex64"?3:1});let x=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],w=d-u-g-x;if(w>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${w} data ids) after running '${a}'`)}runKernelFunc(a,u,h,d,g,x,w){let k,C=[],$=this.isTapeOn();d==null&&(d=this.state.activeScope!=null?this.state.activeScope.name:"");let F=this.state.numBytes,_=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let W,et=cb(d,this.backendName),tt;if(et!=null)W=()=>{let mt=this.backend.numDataIds();tt=et.kernelFunc({inputs:u,attrs:g,backend:this.backend});let lt=Array.isArray(tt)?tt:[tt];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,mt,lt);let gt=lt.map(({dataId:_t,shape:Gt,dtype:se})=>this.makeTensorFromDataId(_t,Gt,se));if($){let 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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?k=W():(G=this.profiler.profileKernel(d,u,()=>W()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(G),k=G.outputs)}),$&&this.addTapeNode(d,u,k,h,C,g),this.state.profiling&&this.state.activeProfile.kernels.push({name:d,bytesAdded:this.state.numBytes-F,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-_,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(mt=>u[mt]!=null?u[mt].shape:null),outputShapes:k.map(mt=>mt.shape),kernelTimeMs:G.timeMs,extraInfo:G.extraInfo}),Array.isArray(tt)?k:k[0]}saveTensorsForBackwardMode(a){let u=a.map(h=>this.keep(this.clone(h)));return u}getTensorsForGradient(a,u,h){let d=pT(a);if(d!=null){let g=d.inputsToSave||[],x=d.outputsToSave||[],w;d.saveAllInputs?(U(Array.isArray(u),()=>"saveAllInputs is true, expected inputs to be an 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h=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,h===0){this.state.numDataBuffers++;let d=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(d=a.size*GE(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:u||this.backend,dtype:a.dtype,shape:a.shape,bytes:d,refCount:0}),this.state.numBytes+=d}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof _h||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let u=this.state.tensorInfo.get(a.dataId),h=u.refCount;h<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=u.bytes),this.state.numDataBuffers--,u.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let a in this.state.registeredVariables){let u=this.state.registeredVariables[a];this.disposeVariable(u)}}disposeVariable(a){this.disposeTensor(a),this.state.registeredVariables[a.name]!=null&&delete this.state.registeredVariables[a.name]}memory(){let a=this.backend.memory();return a.numTensors=this.state.numTensors,a.numDataBuffers=this.state.numDataBuffers,a.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(a.unreliable=!0,a.reasons==null&&(a.reasons=[]),a.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),a}async profile(a){this.state.profiling=!0;let u=this.state.numBytes,h=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await a(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-u,this.state.activeProfile.newTensors=this.state.numTensors-h;for(let d of 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h=Ui.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=oo(h,d).div(Ui.scalar(255)),x=Kh(g,u.dense0,!0);return x=Kh(x,u.dense1),x=Kh(x,u.dense2),x=Kh(x,u.dense3),x=Ui.avgPool(x,[7,7],[2,2],"valid"),x})}async forward(a){return this.forwardInput(await ze(a))}getDefaultModelName(){return"face_feature_extractor_model"}extractParamsFromWeigthMap(a){return $R(a)}extractParams(a){return SR(a)}},DR=te(ee()),Yl=te(ee());function Jh(i,a){return Yl.tidy(()=>Yl.add(Yl.matMul(i,a.weights),a.bias))}function IR(i,a,u){let h=[],{extractWeights:d,getRemainingWeights:g}=nr(i),x=Lb(d,h),w=x(a,u,"fc");if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{paramMappings:h,params:{fc:w}}}function ER(i){let a=[],u=$r(i,a);function h(g){let x=u(`${g}/weights`,2),w=u(`${g}/bias`,1);return{weights:x,bias:w}}let d={fc:h("fc")};return er(i,a),{params:d,paramMappings:a}}function Vb(i){let a={},u={};return Object.keys(i).forEach(h=>{let d=h.startsWith("fc")?u:a;d[h]=i[h]}),{featureExtractorMap:a,classifierMap:u}}var Zh=class extends Fn{constructor(a,u){super(a);this._faceFeatureExtractor=u}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:u}=this;if(!u)throw new Error(`${this._name} - load model before inference`);return DR.tidy(()=>{let h=a instanceof as?this.faceFeatureExtractor.forwardInput(a):a;return Jh(h.as2D(h.shape[0],-1),u.fc)})}dispose(a=!0){this.faceFeatureExtractor.dispose(a),super.dispose(a)}loadClassifierParams(a){let{params:u,paramMappings:h}=this.extractClassifierParams(a);this._params=u,this._paramMappings=h}extractClassifierParams(a){return IR(a,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeigthMap(a){let{featureExtractorMap:u,classifierMap:h}=Vb(a);return this.faceFeatureExtractor.loadFromWeightMap(u),ER(h)}extractParams(a){let u=this.getClassifierChannelsIn(),h=this.getClassifierChannelsOut(),d=h*u+h,g=a.slice(0,a.length-d),x=a.slice(a.length-d);return this.faceFeatureExtractor.extractWeights(g),this.extractClassifierParams(x)}},Gb=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ks=class{constructor(a){if(a.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${a.length}`);Gb.forEach((u,h)=>{this[u]=a[h]})}asSortedArray(){return Gb.map(a=>({expression:a,probability:this[a]})).sort((a,u)=>u.probability-a.probability)}},Qh=class extends Zh{constructor(a=new Yh){super("FaceExpressionNet",a)}forwardInput(a){return Jl.tidy(()=>Jl.softmax(this.runNet(a)))}async forward(a){return this.forwardInput(await ze(a))}async predictExpressions(a){let u=await ze(a),h=await this.forwardInput(u),d=await Promise.all(Jl.unstack(h).map(async x=>{let w=await x.data();return x.dispose(),w}));h.dispose();let g=d.map(x=>new Ks(x));return u.isBatchInput?g:g[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 7}};function Ub(i){return i.expressions instanceof Ks}function tf(i,a){let u={expressions:a};return Object.assign({},i,u)}function Hrt(i,a,u=.1,h){let d=Array.isArray(a)?a:[a];d.forEach(g=>{let x=g instanceof Ks?g:Ub(g)?g.expressions:void 0;if(!x)throw new Error("drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof");let w=x.asSortedArray(),k=w.filter(F=>F.probability>u),C=Eo(g)?g.detection.box.bottomLeft:h||new Yt(0,0),$=new Ri(k.map(F=>`${F.expression} (${Ia(F.probability)})`),C);$.draw(i)})}function qi(i){return Eo(i)&&i.landmarks instanceof Cr&&i.unshiftedLandmarks instanceof Cr&&i.alignedRect instanceof Ve}function sc(i,a){let{box:u}=i.detection,h=a.shiftBy(u.x,u.y),d=h.align(),{imageDims:g}=i.detection,x=new 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k=d(`${w}/separable_conv0`),C=d(`${w}/separable_conv1`),$=d(`${w}/separable_conv2`);return{separable_conv0:k,separable_conv1:C,separable_conv2:$}}return{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}}function FR(i,a){let u=[],{extractConvParams:h,extractSeparableConvParams:d,extractReductionBlockParams:g,extractMainBlockParams:x}=Xrt(i,u),w=h("entry_flow/conv_in"),k=g("entry_flow/reduction_block_0"),C=g("entry_flow/reduction_block_1"),$={conv_in:w,reduction_block_0:k,reduction_block_1:C},F={};Jo(a,0,1).forEach(tt=>{F[`main_block_${tt}`]=x(`middle_flow/main_block_${tt}`)});let _=g("exit_flow/reduction_block"),W=d("exit_flow/separable_conv"),et={reduction_block:_,separable_conv:W};return er(i,u),{params:{entry_flow:$,middle_flow:F,exit_flow:et},paramMappings:u}}function RR(i,a,u){return pn.add(pn.conv2d(i,a.filters,u,"same"),a.bias)}function Bk(i,a,u=!0){let h=u?pn.relu(i):i;return h=yr(h,a.separable_conv0,[1,1]),h=yr(pn.relu(h),a.separable_conv1,[1,1]),h=pn.maxPool(h,[3,3],[2,2],"same"),h=pn.add(h,RR(i,a.expansion_conv,[2,2])),h}function Yrt(i,a){let u=yr(pn.relu(i),a.separable_conv0,[1,1]);return u=yr(pn.relu(u),a.separable_conv1,[1,1]),u=yr(pn.relu(u),a.separable_conv2,[1,1]),u=pn.add(u,i),u}var zk=class extends Fn{constructor(a){super("TinyXception");this._numMainBlocks=a}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyXception - load model before inference");return pn.tidy(()=>{let h=pn.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=oo(h,d).div(pn.scalar(256)),x=pn.relu(RR(g,u.entry_flow.conv_in,[2,2]));return x=Bk(x,u.entry_flow.reduction_block_0,!1),x=Bk(x,u.entry_flow.reduction_block_1),Jo(this._numMainBlocks,0,1).forEach(w=>{x=Yrt(x,u.middle_flow[`main_block_${w}`])}),x=Bk(x,u.exit_flow.reduction_block),x=pn.relu(yr(x,u.exit_flow.separable_conv,[1,1])),x})}async forward(a){return this.forwardInput(await ze(a))}getDefaultModelName(){return"tiny_xception_model"}extractParamsFromWeigthMap(a){return FR(a,this._numMainBlocks)}extractParams(a){return AR(a,this._numMainBlocks)}};function PR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=nr(i),d=Lb(u,a),g=d(512,1,"fc/age"),x=d(512,2,"fc/gender");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{fc:{age:g,gender:x}}}}function OR(i){let a=[],u=$r(i,a);function h(g){let x=u(`${g}/weights`,2),w=u(`${g}/bias`,1);return{weights:x,bias:w}}let d={fc:{age:h("fc/age"),gender:h("fc/gender")}};return er(i,a),{params:d,paramMappings:a}}var cs;(function(i){i.FEMALE="female",i.MALE="male"})(cs||(cs={}));var ef=class extends Fn{constructor(a=new zk(2)){super("AgeGenderNet");this._faceFeatureExtractor=a}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(a){let{params:u}=this;if(!u)throw new Error(`${this._name} - load model before inference`);return ls.tidy(()=>{let h=a instanceof as?this.faceFeatureExtractor.forwardInput(a):a,d=ls.avgPool(h,[7,7],[2,2],"valid").as2D(h.shape[0],-1),g=Jh(d,u.fc.age).as1D(),x=Jh(d,u.fc.gender);return{age:g,gender:x}})}forwardInput(a){return ls.tidy(()=>{let{age:u,gender:h}=this.runNet(a);return{age:u,gender:ls.softmax(h)}})}async forward(a){return this.forwardInput(await ze(a))}async predictAgeAndGender(a){let u=await ze(a),h=await this.forwardInput(u),d=ls.unstack(h.age),g=ls.unstack(h.gender),x=d.map((k,C)=>({ageTensor:k,genderTensor:g[C]})),w=await Promise.all(x.map(async({ageTensor:k,genderTensor:C})=>{let $=(await k.data())[0],F=(await C.data())[0],_=F>.5,W=_?cs.MALE:cs.FEMALE,et=_?F:1-F;return k.dispose(),C.dispose(),{age:$,gender:W,genderProbability:et}}));return h.age.dispose(),h.gender.dispose(),u.isBatchInput?w:w[0]}getDefaultModelName(){return"age_gender_model"}dispose(a=!0){this.faceFeatureExtractor.dispose(a),super.dispose(a)}loadClassifierParams(a){let{params:u,paramMappings:h}=this.extractClassifierParams(a);this._params=u,this._paramMappings=h}extractClassifierParams(a){return PR(a)}extractParamsFromWeigthMap(a){let{featureExtractorMap:u,classifierMap:h}=Vb(a);return this.faceFeatureExtractor.loadFromWeightMap(u),OR(h)}extractParams(a){let u=512*1+1+(512*2+2),h=a.slice(0,a.length-u),d=a.slice(a.length-u);return this.faceFeatureExtractor.extractWeights(h),this.extractClassifierParams(d)}};var Ir=te(ee()),nf=class extends Zh{postProcess(a,u,h){let d=h.map(({width:x,height:w})=>{let k=u/Math.max(w,x);return{width:x*k,height:w*k}}),g=d.length;return Ir.tidy(()=>{let x=(F,_)=>Ir.stack([Ir.fill([68],F,"float32"),Ir.fill([68],_,"float32")],1).as2D(1,136).as1D(),w=(F,_)=>{let{width:W,height:et}=d[F];return _(W,et)?Math.abs(W-et)/2:0},k=F=>w(F,(_,W)=>_<W),C=F=>w(F,(_,W)=>W<_),$=a.mul(Ir.fill([g,136],u,"float32")).sub(Ir.stack(Array.from(Array(g),(F,_)=>x(k(_),C(_))))).div(Ir.stack(Array.from(Array(g),(F,_)=>x(d[_].width,d[_].height))));return $})}forwardInput(a){return Ir.tidy(()=>{let u=this.runNet(a);return this.postProcess(u,a.inputSize,a.inputDimensions.map(([h,d])=>({height:h,width:d})))})}async forward(a){return this.forwardInput(await ze(a))}async detectLandmarks(a){let u=await ze(a),h=Ir.tidy(()=>Ir.unstack(this.forwardInput(u))),d=await Promise.all(h.map(async(g,x)=>{let w=Array.from(await g.data()),k=w.filter(($,F)=>fg(F)),C=w.filter(($,F)=>!fg(F));return new Fa(Array(68).fill(0).map(($,F)=>new Yt(k[F],C[F])),{height:u.getInputHeight(x),width:u.getInputWidth(x)})}));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getClassifierChannelsOut(){return 136}},ic=class extends nf{constructor(a=new Yh){super("FaceLandmark68Net",a)}getDefaultModelName(){return"face_landmark_68_model"}getClassifierChannelsIn(){return 256}};var Hi=te(ee());function LR(i){let a=[],{extractDenseBlock3Params:u}=Wb(i,a),h={dense0:u("dense0",!0),dense1:u("dense1"),dense2:u("dense2")};return er(i,a),{params:h,paramMappings:a}}function MR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=nr(i),{extractDenseBlock3Params:d}=Bb(u,a),g=d(3,32,"dense0",!0),x=d(32,64,"dense1"),w=d(64,128,"dense2");if(h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{paramMappings:a,params:{dense0:g,dense1:x,dense2:w}}}var Wk=class extends Fn{constructor(){super("TinyFaceFeatureExtractor")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("TinyFaceFeatureExtractor - load model before inference");return Hi.tidy(()=>{let h=Hi.cast(a.toBatchTensor(112,!0),"float32"),d=[122.782,117.001,104.298],g=oo(h,d).div(Hi.scalar(255)),x=Rb(g,u.dense0,!0);return x=Rb(x,u.dense1),x=Rb(x,u.dense2),x=Hi.avgPool(x,[14,14],[2,2],"valid"),x})}async forward(a){return this.forwardInput(await ze(a))}getDefaultModelName(){return"face_feature_extractor_tiny_model"}extractParamsFromWeigthMap(a){return LR(a)}extractParams(a){return MR(a)}},rf=class extends nf{constructor(a=new Wk){super("FaceLandmark68TinyNet",a)}getDefaultModelName(){return"face_landmark_68_tiny_model"}getClassifierChannelsIn(){return 128}},Vk=class extends ic{};var jr=te(ee()),Zl=te(ee()),Hb=te(ee());function BR(i,a){return Hb.add(Hb.mul(i,a.weights),a.biases)}function Gk(i,a,u,h,d="same"){let{filters:g,bias:x}=a.conv,w=Zl.conv2d(i,g,u,d);return w=Zl.add(w,x),w=BR(w,a.scale),h?Zl.relu(w):w}function zR(i,a){return Gk(i,a,[1,1],!0)}function Uk(i,a){return Gk(i,a,[1,1],!1)}function jb(i,a){return Gk(i,a,[2,2],!0,"valid")}var Er=te(ee());function Jrt(i,a){function u(w,k,C){let $=i(w),F=$.length/(k*C*C);if(q1(F))throw new Error(`depth has to be an integer: ${F}, weights.length: ${$.length}, 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se={conv32_down:x,conv32_1:w,conv32_2:k,conv32_3:C,conv64_down:$,conv64_1:F,conv64_2:_,conv64_3:W,conv128_down:et,conv128_1:tt,conv128_2:G,conv256_down:mt,conv256_1:lt,conv256_2:gt,conv256_down_out:_t,fc:Gt};return{params:se,paramMappings:h}}function Zrt(i,a){let u=$r(i,a);function h(x){let w=u(`${x}/scale/weights`,1),k=u(`${x}/scale/biases`,1);return{weights:w,biases:k}}function d(x){let w=u(`${x}/conv/filters`,4),k=u(`${x}/conv/bias`,1),C=h(x);return{conv:{filters:w,bias:k},scale:C}}function g(x){return{conv1:d(`${x}/conv1`),conv2:d(`${x}/conv2`)}}return{extractConvLayerParams:d,extractResidualLayerParams:g}}function VR(i){let 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w=x.mean([1,2]),k=jr.matMul(w,u.fc);return k})}async forward(a){return this.forwardInput(await ze(a))}async computeFaceDescriptor(a){let u=await ze(a),h=jr.tidy(()=>jr.unstack(this.forwardInput(u))),d=await Promise.all(h.map(g=>g.data()));return h.forEach(g=>g.dispose()),u.isBatchInput?d:d[0]}getDefaultModelName(){return"face_recognition_model"}extractParamsFromWeigthMap(a){return VR(a)}extractParams(a){return WR(a)}};function GR(i){let a=new ac;return a.extractWeights(i),a}function sf(i,a){let u={descriptor:a};return Object.assign({},i,u)}function UR(i){return typeof i.age=="number"}function af(i,a){let u={age:a};return Object.assign({},i,u)}function qR(i){return(i.gender===cs.MALE||i.gender===cs.FEMALE)&&Cl(i.genderProbability)}function cf(i,a,u){let h={gender:a,genderProbability:u};return Object.assign({},i,h)}var Oo=te(ee()),Po=te(ee());function Qrt(i,a){function u(k,C){let $=Po.tensor4d(i(3*3*k),[3,3,k,1]),F=Po.tensor1d(i(k)),_=Po.tensor1d(i(k)),W=Po.tensor1d(i(k)),et=Po.tensor1d(i(k));return a.push({paramPath:`${C}/filters`},{paramPath:`${C}/batch_norm_scale`},{paramPath:`${C}/batch_norm_offset`},{paramPath:`${C}/batch_norm_mean`},{paramPath:`${C}/batch_norm_variance`}),{filters:$,batch_norm_scale:F,batch_norm_offset:_,batch_norm_mean:W,batch_norm_variance:et}}function h(k,C,$,F,_){let W=Po.tensor4d(i(k*C*$*$),[$,$,k,C]),et=Po.tensor1d(i(C));return a.push({paramPath:`${F}/filters`},{paramPath:`${F}/${_?"batch_norm_offset":"bias"}`}),{filters:W,bias:et}}function d(k,C,$,F){let{filters:_,bias:W}=h(k,C,$,F,!0);return{filters:_,batch_norm_offset:W}}function g(k,C,$){let F=u(k,`${$}/depthwise_conv`),_=d(k,C,1,`${$}/pointwise_conv`);return{depthwise_conv:F,pointwise_conv:_}}function x(){let k=d(3,32,3,"mobilenetv1/conv_0"),C=g(32,64,"mobilenetv1/conv_1"),$=g(64,128,"mobilenetv1/conv_2"),F=g(128,128,"mobilenetv1/conv_3"),_=g(128,256,"mobilenetv1/conv_4"),W=g(256,256,"mobilenetv1/conv_5"),et=g(256,512,"mobilenetv1/conv_6"),tt=g(512,512,"mobilenetv1/conv_7"),G=g(512,512,"mobilenetv1/conv_8"),mt=g(512,512,"mobilenetv1/conv_9"),lt=g(512,512,"mobilenetv1/conv_10"),gt=g(512,512,"mobilenetv1/conv_11"),_t=g(512,1024,"mobilenetv1/conv_12"),Gt=g(1024,1024,"mobilenetv1/conv_13");return{conv_0:k,conv_1:C,conv_2:$,conv_3:F,conv_4:_,conv_5:W,conv_6:et,conv_7:tt,conv_8:G,conv_9:mt,conv_10:lt,conv_11:gt,conv_12:_t,conv_13:Gt}}function w(){let k=d(1024,256,1,"prediction_layer/conv_0"),C=d(256,512,3,"prediction_layer/conv_1"),$=d(512,128,1,"prediction_layer/conv_2"),F=d(128,256,3,"prediction_layer/conv_3"),_=d(256,128,1,"prediction_layer/conv_4"),W=d(128,256,3,"prediction_layer/conv_5"),et=d(256,64,1,"prediction_layer/conv_6"),tt=d(64,128,3,"prediction_layer/conv_7"),G=h(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),mt=h(512,9,1,"prediction_layer/box_predictor_0/class_predictor"),lt=h(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),gt=h(1024,18,1,"prediction_layer/box_predictor_1/class_predictor"),_t=h(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),Gt=h(512,18,1,"prediction_layer/box_predictor_2/class_predictor"),se=h(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),fe=h(256,18,1,"prediction_layer/box_predictor_3/class_predictor"),_e=h(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),Ue=h(256,18,1,"prediction_layer/box_predictor_4/class_predictor"),Vt=h(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),ln=h(128,18,1,"prediction_layer/box_predictor_5/class_predictor"),Ce={box_encoding_predictor:G,class_predictor:mt},or={box_encoding_predictor:lt,class_predictor:gt},ti={box_encoding_predictor:_t,class_predictor:Gt},ei={box_encoding_predictor:se,class_predictor:fe},gc={box_encoding_predictor:_e,class_predictor:Ue},gn={box_encoding_predictor:Vt,class_predictor:ln};return{conv_0:k,conv_1:C,conv_2:$,conv_3:F,conv_4:_,conv_5:W,conv_6:et,conv_7:tt,box_predictor_0:Ce,box_predictor_1:or,box_predictor_2:ti,box_predictor_3:ei,box_predictor_4:gc,box_predictor_5:gn}}return{extractMobilenetV1Params:x,extractPredictionLayerParams:w}}function HR(i){let a=[],{extractWeights:u,getRemainingWeights:h}=nr(i),{extractMobilenetV1Params:d,extractPredictionLayerParams:g}=Qrt(u,a),x=d(),w=g(),k=Po.tensor3d(u(5118*4),[1,5118,4]),C={extra_dim:k};if(a.push({paramPath:"output_layer/extra_dim"}),h().length!==0)throw new Error(`weights remaing after extract: ${h().length}`);return{params:{mobilenetv1:x,prediction_layer:w,output_layer:C},paramMappings:a}}function tot(i,a){let u=$r(i,a);function h(C,$,F){let _=u(`${C}/Conv2d_${$}_pointwise/weights`,4,`${F}/filters`),W=u(`${C}/Conv2d_${$}_pointwise/convolution_bn_offset`,1,`${F}/batch_norm_offset`);return{filters:_,batch_norm_offset:W}}function d(C){let $=`mobilenetv1/conv_${C}`,F=`MobilenetV1/Conv2d_${C}_depthwise`,_=`${$}/depthwise_conv`,W=`${$}/pointwise_conv`,et=u(`${F}/depthwise_weights`,4,`${_}/filters`),tt=u(`${F}/BatchNorm/gamma`,1,`${_}/batch_norm_scale`),G=u(`${F}/BatchNorm/beta`,1,`${_}/batch_norm_offset`),mt=u(`${F}/BatchNorm/moving_mean`,1,`${_}/batch_norm_mean`),lt=u(`${F}/BatchNorm/moving_variance`,1,`${_}/batch_norm_variance`);return{depthwise_conv:{filters:et,batch_norm_scale:tt,batch_norm_offset:G,batch_norm_mean:mt,batch_norm_variance:lt},pointwise_conv:h("MobilenetV1",C,W)}}function g(){return{conv_0:h("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:d(1),conv_2:d(2),conv_3:d(3),conv_4:d(4),conv_5:d(5),conv_6:d(6),conv_7:d(7),conv_8:d(8),conv_9:d(9),conv_10:d(10),conv_11:d(11),conv_12:d(12),conv_13:d(13)}}function x(C,$){let F=u(`${C}/weights`,4,`${$}/filters`),_=u(`${C}/biases`,1,`${$}/bias`);return{filters:F,bias:_}}function w(C){let $=x(`Prediction/BoxPredictor_${C}/BoxEncodingPredictor`,`prediction_layer/box_predictor_${C}/box_encoding_predictor`),F=x(`Prediction/BoxPredictor_${C}/ClassPredictor`,`prediction_layer/box_predictor_${C}/class_predictor`);return{box_encoding_predictor:$,class_predictor:F}}function k(){return{conv_0:h("Prediction",0,"prediction_layer/conv_0"),conv_1:h("Prediction",1,"prediction_layer/conv_1"),conv_2:h("Prediction",2,"prediction_layer/conv_2"),conv_3:h("Prediction",3,"prediction_layer/conv_3"),conv_4:h("Prediction",4,"prediction_layer/conv_4"),conv_5:h("Prediction",5,"prediction_layer/conv_5"),conv_6:h("Prediction",6,"prediction_layer/conv_6"),conv_7:h("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:w(0),box_predictor_1:w(1),box_predictor_2:w(2),box_predictor_3:w(3),box_predictor_4:w(4),box_predictor_5:w(5)}}return{extractMobilenetV1Params:g,extractPredictionLayerParams:k}}function jR(i){let a=[],{extractMobilenetV1Params:u,extractPredictionLayerParams:h}=tot(i,a),d=i["Output/extra_dim"];if(a.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Rs(d))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${d}`);let g={mobilenetv1:u(),prediction_layer:h(),output_layer:{extra_dim:d}};return er(i,a),{params:g,paramMappings:a}}var Xs=te(ee()),ji=te(ee());function lo(i,a,u){return ji.tidy(()=>{let h=ji.conv2d(i,a.filters,u,"same");return h=ji.add(h,a.batch_norm_offset),ji.clipByValue(h,0,6)})}var eot=.0010000000474974513;function not(i,a,u){return Xs.tidy(()=>{let h=Xs.depthwiseConv2d(i,a.filters,u,"same");return h=Xs.batchNorm(h,a.batch_norm_mean,a.batch_norm_variance,a.batch_norm_offset,a.batch_norm_scale,eot),Xs.clipByValue(h,0,6)})}function rot(i){return[2,4,6,12].some(a=>a===i)?[2,2]:[1,1]}function KR(i,a){return Xs.tidy(()=>{let u,h=lo(i,a.conv_0,[2,2]),d=[a.conv_1,a.conv_2,a.conv_3,a.conv_4,a.conv_5,a.conv_6,a.conv_7,a.conv_8,a.conv_9,a.conv_10,a.conv_11,a.conv_12,a.conv_13];if(d.forEach((g,x)=>{let w=x+1,k=rot(w);h=not(h,g.depthwise_conv,k),h=lo(h,g.pointwise_conv,[1,1]),w===11&&(u=h)}),u===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:h,conv11:u}})}function XR(i,a,u,h,d){let g=i.shape[0],x=Math.min(u,g),w=a.map(($,F)=>({score:$,boxIndex:F})).filter($=>$.score>d).sort(($,F)=>F.score-$.score),k=$=>$<=h?1:0,C=[];return w.forEach($=>{if(C.length>=x)return;let F=$.score;for(let _=C.length-1;_>=0;--_){let W=oot(i,$.boxIndex,C[_]);if(W===0)continue;if($.score*=k(W),$.score<=d)break}F===$.score&&C.push($.boxIndex)}),C}function oot(i,a,u){let h=i.arraySync(),d=Math.min(h[a][0],h[a][2]),g=Math.min(h[a][1],h[a][3]),x=Math.max(h[a][0],h[a][2]),w=Math.max(h[a][1],h[a][3]),k=Math.min(h[u][0],h[u][2]),C=Math.min(h[u][1],h[u][3]),$=Math.max(h[u][0],h[u][2]),F=Math.max(h[u][1],h[u][3]),_=(x-d)*(w-g),W=($-k)*(F-C);if(_<=0||W<=0)return 0;let et=Math.max(d,k),tt=Math.max(g,C),G=Math.min(x,$),mt=Math.min(w,F),lt=Math.max(G-et,0)*Math.max(mt-tt,0);return lt/(_+W-lt)}var Mt=te(ee());function sot(i){let a=Mt.unstack(Mt.transpose(i,[1,0])),u=[Mt.sub(a[2],a[0]),Mt.sub(a[3],a[1])],h=[Mt.add(a[0],Mt.div(u[0],Mt.scalar(2))),Mt.add(a[1],Mt.div(u[1],Mt.scalar(2)))];return{sizes:u,centers:h}}function iot(i,a){let{sizes:u,centers:h}=sot(i),d=Mt.unstack(Mt.transpose(a,[1,0])),g=Mt.div(Mt.mul(Mt.exp(Mt.div(d[2],Mt.scalar(5))),u[0]),Mt.scalar(2)),x=Mt.add(Mt.mul(Mt.div(d[0],Mt.scalar(10)),u[0]),h[0]),w=Mt.div(Mt.mul(Mt.exp(Mt.div(d[3],Mt.scalar(5))),u[1]),Mt.scalar(2)),k=Mt.add(Mt.mul(Mt.div(d[1],Mt.scalar(10)),u[1]),h[1]);return Mt.transpose(Mt.stack([Mt.sub(x,g),Mt.sub(k,w),Mt.add(x,g),Mt.add(k,w)]),[1,0])}function YR(i,a,u){return Mt.tidy(()=>{let h=i.shape[0],d=iot(Mt.reshape(Mt.tile(u.extra_dim,[h,1,1]),[-1,4]),Mt.reshape(i,[-1,4]));d=Mt.reshape(d,[h,d.shape[0]/h,4]);let g=Mt.sigmoid(Mt.slice(a,[0,0,1],[-1,-1,-1])),x=Mt.slice(g,[0,0,0],[-1,-1,1]);x=Mt.reshape(x,[h,x.shape[1]]);let w=Mt.unstack(d),k=Mt.unstack(x);return{boxes:w,scores:k}})}var uf=te(ee()),lf=te(ee());function cc(i,a){return lf.tidy(()=>{let u=i.shape[0],h=lf.reshape(oc(i,a.box_encoding_predictor),[u,-1,1,4]),d=lf.reshape(oc(i,a.class_predictor),[u,-1,3]);return{boxPredictionEncoding:h,classPrediction:d}})}function JR(i,a,u){return uf.tidy(()=>{let h=lo(i,u.conv_0,[1,1]),d=lo(h,u.conv_1,[2,2]),g=lo(d,u.conv_2,[1,1]),x=lo(g,u.conv_3,[2,2]),w=lo(x,u.conv_4,[1,1]),k=lo(w,u.conv_5,[2,2]),C=lo(k,u.conv_6,[1,1]),$=lo(C,u.conv_7,[2,2]),F=cc(a,u.box_predictor_0),_=cc(i,u.box_predictor_1),W=cc(d,u.box_predictor_2),et=cc(x,u.box_predictor_3),tt=cc(k,u.box_predictor_4),G=cc($,u.box_predictor_5),mt=uf.concat([F.boxPredictionEncoding,_.boxPredictionEncoding,W.boxPredictionEncoding,et.boxPredictionEncoding,tt.boxPredictionEncoding,G.boxPredictionEncoding],1),lt=uf.concat([F.classPrediction,_.classPrediction,W.classPrediction,et.classPrediction,tt.classPrediction,G.classPrediction],1);return{boxPredictions:mt,classPredictions:lt}})}var Kr=class{constructor({minConfidence:a,maxResults:u}={}){this._name="SsdMobilenetv1Options";if(this._minConfidence=a||.5,this._maxResults=u||100,typeof this._minConfidence!="number"||this._minConfidence<=0||this._minConfidence>=1)throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`);if(typeof this._maxResults!="number")throw new Error(`${this._name} - expected maxResults to be a number`)}get minConfidence(){return this._minConfidence}get maxResults(){return this._maxResults}},Ki=class extends Fn{constructor(){super("SsdMobilenetv1")}forwardInput(a){let{params:u}=this;if(!u)throw new Error("SsdMobilenetv1 - load model before inference");return Oo.tidy(()=>{let h=Oo.cast(a.toBatchTensor(512,!1),"float32"),d=Oo.sub(Oo.mul(h,Oo.scalar(.007843137718737125)),Oo.scalar(1)),g=KR(d,u.mobilenetv1),{boxPredictions:x,classPredictions:w}=JR(g.out,g.conv11,u.prediction_layer);return YR(x,w,u.output_layer)})}async forward(a){return this.forwardInput(await ze(a))}async locateFaces(a,u={}){let{maxResults:h,minConfidence:d}=new Kr(u),g=await ze(a),{boxes:x,scores:w}=this.forwardInput(g),k=x[0],C=w[0];for(let gt=1;gt<x.length;gt++)x[gt].dispose(),w[gt].dispose();let $=Array.from(await C.data()),F=.5,_=XR(k,$,h,F,d),W=g.getReshapedInputDimensions(0),et=g.inputSize,tt=et/W.width,G=et/W.height,mt=k.arraySync(),lt=_.map(gt=>{let[_t,Gt]=[Math.max(0,mt[gt][0]),Math.min(1,mt[gt][2])].map(_e=>_e*G),[se,fe]=[Math.max(0,mt[gt][1]),Math.min(1,mt[gt][3])].map(_e=>_e*tt);return new Ve($[gt],new Aa(se,_t,fe-se,Gt-_t),{height:g.getInputHeight(0),width:g.getInputWidth(0)})});return k.dispose(),C.dispose(),lt}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeigthMap(a){return jR(a)}extractParams(a){return HR(a)}};function qk(i){let a=new Ki;return a.extractWeights(i),a}function ZR(i){return qk(i)}var Hk=class extends Ki{},QR=.4,tP=[new Yt(.738768,.874946),new Yt(2.42204,2.65704),new Yt(4.30971,7.04493),new Yt(10.246,4.59428),new Yt(12.6868,11.8741)],eP=[new Yt(1.603231,2.094468),new Yt(6.041143,7.080126),new Yt(2.882459,3.518061),new Yt(4.266906,5.178857),new Yt(9.041765,10.66308)],nP=[117.001,114.697,97.404],rP="tiny_yolov2_model",oP="tiny_yolov2_separable_conv_model",He=te(ee()),Kb=i=>typeof i=="number";function Xb(i){if(!i)throw new Error(`invalid config: ${i}`);if(typeof i.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${i.withSeparableConvs}`);if(!Kb(i.iouThreshold)||i.iouThreshold<0||i.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${i.iouThreshold}`);if(!Array.isArray(i.classes)||!i.classes.length||!i.classes.every(a=>typeof a=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(i.classes)}`);if(!Array.isArray(i.anchors)||!i.anchors.length||!i.anchors.map(a=>a||{}).every(a=>Kb(a.x)&&Kb(a.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(i.anchors)}`);if(i.meanRgb&&(!Array.isArray(i.meanRgb)||i.meanRgb.length!==3||!i.meanRgb.every(Kb)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(i.meanRgb)}`)}var po=te(ee()),uo=te(ee());function Ql(i){return uo.tidy(()=>{let a=uo.mul(i,uo.scalar(.10000000149011612));return uo.add(uo.relu(uo.sub(i,a)),a)})}function Ys(i,a){return po.tidy(()=>{let u=po.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=po.conv2d(u,a.conv.filters,[1,1],"valid"),u=po.sub(u,a.bn.sub),u=po.mul(u,a.bn.truediv),u=po.add(u,a.conv.bias),Ql(u)})}var Xi=te(ee());function Js(i,a){return Xi.tidy(()=>{let u=Xi.pad(i,[[0,0],[1,1],[1,1],[0,0]]);return u=Xi.separableConv2d(u,a.depthwise_filter,a.pointwise_filter,[1,1],"valid"),u=Xi.add(u,a.bias),Ql(u)})}var jk=te(ee());function aot(i,a){let u=jl(i,a);function h(x,w){let k=jk.tensor1d(i(x)),C=jk.tensor1d(i(x));return 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0,Ce=w(gt||lt||mt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ue,conv6:Vt,conv7:ln,conv8:Ce}}else{let[F,_,W,et,tt,G,mt,lt,gt]=h,_t=k(F,_,"conv0"),Gt=k(_,W,"conv1"),se=k(W,et,"conv2"),fe=k(et,tt,"conv3"),_e=k(tt,G,"conv4"),Ue=k(G,mt,"conv5"),Vt=k(mt,lt,"conv6"),ln=k(lt,gt,"conv7"),Ce=w(gt,5*u,1,"conv8");$={conv0:_t,conv1:Gt,conv2:se,conv3:fe,conv4:_e,conv5:Ue,conv6:Vt,conv7:ln,conv8:Ce}}if(g().length!==0)throw new Error(`weights remaing after extract: ${g().length}`);return{params:$,paramMappings:x}}function cot(i,a){let u=$r(i,a);function h(w){let k=u(`${w}/sub`,1),C=u(`${w}/truediv`,1);return{sub:k,truediv:C}}function d(w){let k=u(`${w}/filters`,4),C=u(`${w}/bias`,1);return{filters:k,bias:C}}function g(w){let k=d(`${w}/conv`),C=h(`${w}/bn`);return{conv:k,bn:C}}let x=Xl(u);return{extractConvParams:d,extractConvWithBatchNormParams:g,extractSeparableConvParams:x}}function iP(i,a){let u=[],{extractConvParams:h,extractConvWithBatchNormParams:d,extractSeparableConvParams:g}=cot(i,u),x;if(a.withSeparableConvs){let w=a.filterSizes&&a.filterSizes.length||9;x={conv0:a.isFirstLayerConv2d?h("conv0"):g("conv0"),conv1:g("conv1"),conv2:g("conv2"),conv3:g("conv3"),conv4:g("conv4"),conv5:g("conv5"),conv6:w>7?g("conv6"):void 0,conv7:w>8?g("conv7"):void 0,conv8:h("conv8")}}else x={conv0:d("conv0"),conv1:d("conv1"),conv2:d("conv2"),conv3:d("conv3"),conv4:d("conv4"),conv5:d("conv5"),conv6:d("conv6"),conv7:d("conv7"),conv8:h("conv8")};return er(i,u),{params:x,paramMappings:u}}var Yb;(function(i){i[i.XS=224]="XS",i[i.SM=320]="SM",i[i.MD=416]="MD",i[i.LG=608]="LG"})(Yb||(Yb={}));var Lo=class{constructor({inputSize:a,scoreThreshold:u}={}){this._name="TinyYolov2Options";if(this._inputSize=a||416,this._scoreThreshold=u||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${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}},Kk=class extends Fn{constructor(a){super("TinyYolov2");Xb(a),this._config=a}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(a,u){let h=Ys(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Ys(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=Ys(h,u.conv6),h=Ys(h,u.conv7),oc(h,u.conv8,"valid",!1)}runMobilenet(a,u){let h=this.config.isFirstLayerConv2d?Ql(oc(a,u.conv0,"valid",!1)):Js(a,u.conv0);return h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv1),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv2),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv3),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv4),h=He.maxPool(h,[2,2],[2,2],"same"),h=Js(h,u.conv5),h=He.maxPool(h,[2,2],[1,1],"same"),h=u.conv6?Js(h,u.conv6):h,h=u.conv7?Js(h,u.conv7):h,oc(h,u.conv8,"valid",!1)}forwardInput(a,u){let{params:h}=this;if(!h)throw new Error("TinyYolov2 - load model before inference");return He.tidy(()=>{let d=He.cast(a.toBatchTensor(u,!1),"float32");return d=this.config.meanRgb?oo(d,this.config.meanRgb):d,d=d.div(He.scalar(256)),this.config.withSeparableConvs?this.runMobilenet(d,h):this.runTinyYolov2(d,h)})}async forward(a,u){return await this.forwardInput(await ze(a),u)}async detect(a,u={}){let{inputSize:h,scoreThreshold:d}=new Lo(u),g=await ze(a),x=await this.forwardInput(g,h),w=He.tidy(()=>He.unstack(x)[0].expandDims()),k={width:g.getInputWidth(0),height:g.getInputHeight(0)},C=await this.extractBoxes(w,g.getReshapedInputDimensions(0),d);x.dispose(),w.dispose();let $=C.map(G=>G.box),F=C.map(G=>G.score),_=C.map(G=>G.classScore),W=C.map(G=>this.config.classes[G.label]),et=yg($.map(G=>G.rescale(h)),F,this.config.iouThreshold,!0),tt=et.map(G=>new Ps(F[G],_[G],W[G],$[G],k));return tt}getDefaultModelName(){return""}extractParamsFromWeigthMap(a){return iP(a,this.config)}extractParams(a){let u=this.config.filterSizes||Kk.DEFAULT_FILTER_SIZES,h=u?u.length:void 0;if(h!==7&&h!==8&&h!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${h} filterSizes in config`);return sP(a,this.config,this.boxEncodingSize,u)}async extractBoxes(a,u,h){let{width:d,height:g}=u,x=Math.max(d,g),w=x/d,k=x/g,C=a.shape[1],$=this.config.anchors.length,[F,_,W]=He.tidy(()=>{let mt=a.reshape([C,C,$,this.boxEncodingSize]),lt=mt.slice([0,0,0,0],[C,C,$,4]),gt=mt.slice([0,0,0,4],[C,C,$,1]),_t=this.withClassScores?He.softmax(mt.slice([0,0,0,5],[C,C,$,this.config.classes.length]),3):He.scalar(0);return[lt,gt,_t]}),et=[],tt=await _.array(),G=await F.array();for(let mt=0;mt<C;mt++)for(let lt=0;lt<C;lt++)for(let gt=0;gt<$;gt++){let _t=Sl(tt[mt][lt][gt][0]);if(!h||_t>h){let Gt=(lt+Sl(G[mt][lt][gt][0]))/C*w,se=(mt+Sl(G[mt][lt][gt][1]))/C*k,fe=Math.exp(G[mt][lt][gt][2])*this.config.anchors[gt].x/C*w,_e=Math.exp(G[mt][lt][gt][3])*this.config.anchors[gt].y/C*k,Ue=Gt-fe/2,Vt=se-_e/2,ln={row:mt,col:lt,anchor:gt},{classScore:Ce,label:or}=this.withClassScores?await this.extractPredictedClass(W,ln):{classScore:1,label:0};et.push({box:new Da(Ue,Vt,Ue+fe,Vt+_e),score:_t,classScore:_t*Ce,label:or,...ln})}}return F.dispose(),_.dispose(),W.dispose(),et}async extractPredictedClass(a,u){let{row:h,col:d,anchor:g}=u,x=await a.array();return Array(this.config.classes.length).fill(0).map((w,k)=>x[h][d][g][k]).map((w,k)=>({classScore:w,label:k})).reduce((w,k)=>w.classScore>k.classScore?w:k)}},tu=Kk;tu.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var lc=class extends tu{constructor(a=!0){let u=Object.assign({},{withSeparableConvs:a,iouThreshold:QR,classes:["face"]},a?{anchors:eP,meanRgb:nP}:{anchors:tP,withClassScores:!0});super(u)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ve(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?oP:rP}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}};function aP(i,a=!0){let u=new lc(a);return u.extractWeights(i),u}var pf=class extends Lo{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}},Xr=class{async then(a){return a(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}},ff=te(ee()),Xk=te(ee());async function uc(i,a,u,h,d=({alignedRect:g})=>g){let g=i.map(k=>qi(k)?d(k):k.detection),x=h||(a instanceof Xk.Tensor?await rc(a,g):await nc(a,g)),w=await u(x);return x.forEach(k=>k instanceof Xk.Tensor&&k.dispose()),w}async function eu(i,a,u,h,d){return uc([i],a,async g=>u(g[0]),h,d)}var cP=.4,lP=[new Yt(1.603231,2.094468),new Yt(6.041143,7.080126),new Yt(2.882459,3.518061),new Yt(4.266906,5.178857),new Yt(9.041765,10.66308)],uP=[117.001,114.697,97.404],pc=class extends tu{constructor(){let a={withSeparableConvs:!0,iouThreshold:cP,classes:["face"],anchors:lP,meanRgb:uP,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(a)}get anchors(){return this.config.anchors}async locateFaces(a,u){let h=await this.detect(a,u);return h.map(d=>new Ve(d.score,d.relativeBox,{width:d.imageWidth,height:d.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeigthMap(a){return super.extractParamsFromWeigthMap(a)}},Te={ssdMobilenetv1:new Ki,tinyFaceDetector:new pc,tinyYolov2:new lc,faceLandmark68Net:new ic,faceLandmark68TinyNet:new rf,faceRecognitionNet:new ac,faceExpressionNet:new Qh,ageGenderNet:new ef},Yk=(i,a)=>Te.ssdMobilenetv1.locateFaces(i,a),pP=(i,a)=>Te.tinyFaceDetector.locateFaces(i,a),hP=(i,a)=>Te.tinyYolov2.locateFaces(i,a),Jk=i=>Te.faceLandmark68Net.detectLandmarks(i),fP=i=>Te.faceLandmark68TinyNet.detectLandmarks(i),dP=i=>Te.faceRecognitionNet.computeFaceDescriptor(i),mP=i=>Te.faceExpressionNet.predictExpressions(i),gP=i=>Te.ageGenderNet.predictAgeAndGender(i),Zk=i=>Te.ssdMobilenetv1.load(i),yP=i=>Te.tinyFaceDetector.load(i),bP=i=>Te.tinyYolov2.load(i),xP=i=>Te.faceLandmark68Net.load(i),wP=i=>Te.faceLandmark68TinyNet.load(i),vP=i=>Te.faceRecognitionNet.load(i),TP=i=>Te.faceExpressionNet.load(i),kP=i=>Te.ageGenderNet.load(i),NP=Zk,_P=Yk,CP=Jk,Qk=class extends Xr{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},ou=class extends Qk{async run(){let a=await this.parentTask,u=await uc(a,this.input,async h=>await Promise.all(h.map(d=>Te.faceExpressionNet.predictExpressions(d))),this.extractedFaces);return a.map((h,d)=>tf(h,u[d]))}withAgeAndGender(){return new nu(this,this.input)}},su=class extends Qk{async run(){let a=await this.parentTask;if(!a)return;let u=await eu(a,this.input,h=>Te.faceExpressionNet.predictExpressions(h),this.extractedFaces);return tf(a,u)}withAgeAndGender(){return new ru(this,this.input)}},dc=class extends ou{withAgeAndGender(){return new hc(this,this.input)}withFaceDescriptors(){return new Zs(this,this.input)}},mc=class extends su{withAgeAndGender(){return new fc(this,this.input)}withFaceDescriptor(){return new Qs(this,this.input)}},tN=class extends Xr{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.extractedFaces=h}},nu=class extends tN{async run(){let a=await this.parentTask,u=await uc(a,this.input,async h=>await Promise.all(h.map(d=>Te.ageGenderNet.predictAgeAndGender(d))),this.extractedFaces);return a.map((h,d)=>{let{age:g,gender:x,genderProbability:w}=u[d];return af(cf(h,x,w),g)})}withFaceExpressions(){return new ou(this,this.input)}},ru=class extends tN{async run(){let a=await this.parentTask;if(!a)return;let{age:u,gender:h,genderProbability:d}=await eu(a,this.input,g=>Te.ageGenderNet.predictAgeAndGender(g),this.extractedFaces);return af(cf(a,h,d),u)}withFaceExpressions(){return new su(this,this.input)}},hc=class extends nu{withFaceExpressions(){return new dc(this,this.input)}withFaceDescriptors(){return new Zs(this,this.input)}},fc=class extends ru{withFaceExpressions(){return new mc(this,this.input)}withFaceDescriptor(){return new Qs(this,this.input)}},hf=class extends Xr{constructor(a,u){super();this.parentTask=a;this.input=u}},Zs=class extends hf{async run(){let a=await this.parentTask,u=await uc(a,this.input,h=>Promise.all(h.map(d=>Te.faceRecognitionNet.computeFaceDescriptor(d))),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return u.map((h,d)=>sf(a[d],h))}withFaceExpressions(){return new dc(this,this.input)}withAgeAndGender(){return new hc(this,this.input)}},Qs=class extends hf{async run(){let a=await this.parentTask;if(!a)return;let u=await eu(a,this.input,h=>Te.faceRecognitionNet.computeFaceDescriptor(h),null,h=>h.landmarks.align(null,{useDlibAlignment:!0}));return sf(a,u)}withFaceExpressions(){return new mc(this,this.input)}withAgeAndGender(){return new fc(this,this.input)}},df=class extends Xr{constructor(a,u,h){super();this.parentTask=a;this.input=u;this.useTinyLandmarkNet=h}get landmarkNet(){return this.useTinyLandmarkNet?Te.faceLandmark68TinyNet:Te.faceLandmark68Net}},mf=class extends df{async run(){let a=await this.parentTask,u=a.map(g=>g.detection),h=this.input instanceof ff.Tensor?await rc(this.input,u):await nc(this.input,u),d=await Promise.all(h.map(g=>this.landmarkNet.detectLandmarks(g)));return h.forEach(g=>g instanceof ff.Tensor&&g.dispose()),a.map((g,x)=>sc(g,d[x]))}withFaceExpressions(){return new dc(this,this.input)}withAgeAndGender(){return new hc(this,this.input)}withFaceDescriptors(){return new Zs(this,this.input)}},gf=class extends df{async run(){let a=await this.parentTask;if(!a)return;let{detection:u}=a,h=this.input instanceof ff.Tensor?await rc(this.input,[u]):await nc(this.input,[u]),d=await this.landmarkNet.detectLandmarks(h[0]);return h.forEach(g=>g instanceof ff.Tensor&&g.dispose()),sc(a,d)}withFaceExpressions(){return new mc(this,this.input)}withAgeAndGender(){return new fc(this,this.input)}withFaceDescriptor(){return new Qs(this,this.input)}},yf=class extends Xr{constructor(a,u=new Kr){super();this.input=a;this.options=u}},iu=class extends yf{async run(){let{input:a,options:u}=this,h=u instanceof pf?d=>Te.tinyFaceDetector.locateFaces(d,u):u instanceof Kr?d=>Te.ssdMobilenetv1.locateFaces(d,u):u instanceof Lo?d=>Te.tinyYolov2.locateFaces(d,u):null;if(!h)throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return h(a)}runAndExtendWithFaceDetections(){return new Promise(async a=>{let u=await this.run();return a(u.map(h=>Ai({},h)))})}withFaceLandmarks(a=!1){return new mf(this.runAndExtendWithFaceDetections(),this.input,a)}withFaceExpressions(){return new ou(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new nu(this.runAndExtendWithFaceDetections(),this.input)}},bf=class extends yf{async run(){let a=await new iu(this.input,this.options),u=a[0];return a.forEach(h=>{h.score>u.score&&(u=h)}),u}runAndExtendWithFaceDetection(){return new Promise(async a=>{let u=await this.run();return a(u?Ai({},u):void 0)})}withFaceLandmarks(a=!1){return new gf(this.runAndExtendWithFaceDetection(),this.input,a)}withFaceExpressions(){return new su(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ru(this.runAndExtendWithFaceDetection(),this.input)}};function SP(i,a=new Kr){return new bf(i,a)}function xf(i,a=new Kr){return new iu(i,a)}async function eN(i,a){return console.warn("allFacesSsdMobilenetv1 is deprecated and will be removed soon, use the high level api instead"),await xf(i,new Kr(a?{minConfidence:a}:{})).withFaceLandmarks().withFaceDescriptors()}async function $P(i,a={}){return console.warn("allFacesTinyYolov2 is deprecated and will be removed soon, use the high level api instead"),await xf(i,new Lo(a)).withFaceLandmarks().withFaceDescriptors()}var IP=eN;function Jb(i,a){if(i.length!==a.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let u=Array.from(i),h=Array.from(a);return Math.sqrt(u.map((d,g)=>d-h[g]).reduce((d,g)=>d+Math.pow(g,2),0))}var wf=class{constructor(a,u=.6){this._distanceThreshold=u;let h=Array.isArray(a)?a:[a];if(!h.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let d=1,g=()=>`person ${d++}`;this._labeledDescriptors=h.map(x=>{if(x instanceof Qo)return x;if(x instanceof Float32Array)return new Qo(g(),[x]);if(x.descriptor&&x.descriptor instanceof Float32Array)return new Qo(g(),[x.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(a,u){return u.map(h=>Jb(h,a)).reduce((h,d)=>h+d,0)/(u.length||1)}matchDescriptor(a){return this.labeledDescriptors.map(({descriptors:u,label:h})=>new $l(h,this.computeMeanDistance(a,u))).reduce((u,h)=>u.distance<h.distance?u:h)}findBestMatch(a){let u=this.matchDescriptor(a);return u.distance<this.distanceThreshold?u:new $l("unknown",u.distance)}toJSON(){return{distanceThreshold:this.distanceThreshold,labeledDescriptors:this.labeledDescriptors.map(a=>a.toJSON())}}static fromJSON(a){let u=a.labeledDescriptors.map(h=>Qo.fromJSON(h));return new wf(u,a.distanceThreshold)}};function EP(i){let a=new pc;return a.extractWeights(i),a}function nN(i,a){let{width:u,height:h}=new jn(a.width,a.height);if(u<=0||h<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:u,height:h})}`);if(Array.isArray(i))return i.map(d=>nN(d,{width:u,height:h}));if(qi(i)){let d=i.detection.forSize(u,h),g=i.unshiftedLandmarks.forSize(d.box.width,d.box.height);return sc(Ai(i,d),g)}return Eo(i)?Ai(i,i.detection.forSize(u,h)):i instanceof Cr||i instanceof Ve?i.forSize(u,h):i}var DP="0.8.8",uot=typeof process!="undefined",pot=typeof navigator!="undefined"&&typeof navigator.userAgent!="undefined",FP={faceapi:DP,node:uot,browser:pot};return lot;})();
/**
 * @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 See the LICENSE file. */
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