human/dist/tfjs.esm.js

  /*
  Human library
  homepage: <https://github.com/vladmandic/human>
  author: <https://github.com/vladmandic>'
  */

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o=++this.pendingBackendInitId,s=n.then(a=>o<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:o,asyncInit:s}=this.initializeBackend(n);if(s||o)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),o=n.backend,s=this.readSync(t);o.disposeData(t),n.backend=e,e.move(t,s,n.shape,n.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to 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this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let o=this.backend.numDataIds(),s=0;n.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=o-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e,t,n,o,s,a,i){let l,u=[],c=this.isTapeOn();o==null&&(o=this.state.activeScope!=null?this.state.activeScope.name:"");let p=this.state.numBytes,m=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let f;this.backendName==null&&this.backend;let d=ym(o,this.backendName),h;if(d!=null)f=()=>{let y=this.backend.numDataIds();h=d.kernelFunc({inputs:t,attrs:s,backend:this.backend});let b=Array.isArray(h)?h:[h];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,y,b);let w=b.map(_=>{if(_.rank!=null)return _;let{dataId:k,shape:E,dtype:N}=_;return this.makeTensorFromDataId(k,E,N)});if(c){let _=this.getTensorsForGradient(o,t,w);if(_==null){i==null&&(i=[]);let k=w.filter((E,N)=>i[N]);_=(a||[]).slice().concat(k)}u=this.saveTensorsForBackwardMode(_)}return w};else{if(e==null)throw new Error(`Error running ${o}: Neither modular kernel nor forward func passed`);let y=b=>{!c||(u=b.map(w=>this.keep(this.clone(w))))};f=()=>{let b=this.backend.numDataIds();h=this.tidy(()=>e(this.backend,y));let w=Array.isArray(h)?h:[h];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(o,b,w),w}}let g;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?l=f():(g=this.profiler.profileKernel(o,t,()=>f()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(g),l=g.outputs)}),c&&this.addTapeNode(o,t,l,n,u,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:o,bytesAdded:this.state.numBytes-p,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-m,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(t).map(y=>t[y]!=null?t[y].shape:null),outputShapes:l.map(y=>y.shape),kernelTimeMs:g.timeMs,extraInfo:g.extraInfo}),Array.isArray(h)?l:l[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let o=Gh(e);if(o!=null){let s=o.inputsToSave||[],a=o.outputsToSave||[],i;o.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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n=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,n===0){this.state.numDataBuffers++;let o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Eb(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o,refCount:0}),this.state.numBytes+=o}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof sl||this.track(e)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;this.state.numTensors--,e.dtype==="string"&&this.state.numStringTensors--;let t=this.state.tensorInfo.get(e.dataId);t.refCount<=1?(e.dtype!=="complex64"&&(this.state.numBytes-=t.bytes),this.state.numDataBuffers--,t.backend.disposeData(e.dataId),this.state.tensorInfo.delete(e.dataId)):this.state.tensorInfo.get(e.dataId).refCount--}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of 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a=v(r,"x","batchNorm"),i=v(e,"mean","batchNorm"),l=v(t,"variance","batchNorm"),u;o!=null&&(u=v(o,"scale","batchNorm"));let c;return n!=null&&(c=v(n,"offset","batchNorm")),A(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),A(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),A(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&A(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&A(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Lo(a,i,l,c,u,s)}var _w=S({batchNorm4d_:Ej});function Aj(r,e,t){let n=v(r,"x","bincount"),o=v(e,"weights","bincount");A(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),A(t>=0,()=>`size must be non-negative, but got 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Fm=S({ceil_:$j});function Rj(r,e,t){let n=v(r,"x","clipByValue");A(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let o={x:n},s={clipValueMin:e,clipValueMax:t};return D.runKernel(Pn,o,s)}var ar=S({clipByValue_:Rj});function Fj(r){return Qe(r,0)}var kw=S({concat1d_:Fj});function Oj(r,e){return Qe(r,e)}var Cw=S({concat2d_:Oj});function Pj(r,e){return Qe(r,e)}var Iw=S({concat3d_:Pj});function Mj(r,e){return Qe(r,e)}var Sw=S({concat4d_:Mj});function Lj(r,e,t,n,o="NHWC",s=[1,1],a){let i=v(r,"x","conv2d"),l=v(e,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=z(i,[1,i.shape[0],i.shape[1],i.shape[2]])),A(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&A(it(n),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p=o==="NHWC"?u.shape[3]:u.shape[1];A(p===l.shape[2],()=>`Error in conv2d: depth 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p={x:u,filter:l},m={strides:t,pad:n,dataFormat:o,dilations:s,dimRoundingMode:a},f=D.runKernel(to,p,m);return c?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var zn=S({depthwiseConv2d_:Zj});function Jj(r){let t={x:v(r,"x","diag")};return D.runKernel(ru,t)}var Qj=S({diag_:Jj});function eG(r,e,t,n,o=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");A(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),A(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=z(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:n,dilations:o},m=D.runKernel(la,c,p);return u?z(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Mm=S({dilation2d_:eG});function tG(r,e){let t=r.length,n=[];for(let o=0;o<t;o++){let 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o=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),je(o.shape,s.shape,"Error in absoluteDifference: ");let i=At(pe(o,s));return Er(i,a,n)}var FN=S({absoluteDifference_:Pq});function Mq(r,e,t,n,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;n!=null&&(i=v(n,"weights","cosineDistance")),je(s.shape,a.shape,"Error in cosineDistance: ");let l=ce(1),u=pe(l,be(M(s,a),t,!0));return Er(u,i,o)}var ON=S({cosineDistance_:Mq});function Lq(r,e,t,n=Ut.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),je(o.shape,s.shape,"Error in hingeLoss: ");let i=ce(1);o=pe(M(ce(2),o),i);let l=Tr(pe(i,M(o,s)));return Er(l,a,n)}var PN=S({hingeLoss_:Lq});function zq(r,e,t,n=1,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","huberLoss"),a=v(e,"predictions","huberLoss"),i=null;t!=null&&(i=v(t,"weights","huberLoss")),je(s.shape,a.shape,"Error in huberLoss: ");let l=ce(n),u=At(pe(a,s)),c=Vn(u,l),p=pe(u,c),m=ee(M(ce(.5),Me(c)),M(l,p));return Er(m,i,o)}var MN=S({huberLoss_:zq});function Bq(r,e,t,n=1e-7,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","logLoss"),a=v(e,"predictions","logLoss"),i=null;t!=null&&(i=v(t,"weights","logLoss")),je(s.shape,a.shape,"Error in logLoss: ");let l=ce(1),u=ce(n),c=He(M(s,lr(ee(a,u)))),p=M(pe(l,s),lr(ee(pe(l,a),u))),m=pe(c,p);return Er(m,i,o)}var LN=S({logLoss_:Bq});function Vq(r,e,t,n=Ut.SUM_BY_NONZERO_WEIGHTS){let o=v(r,"labels","meanSquaredError"),s=v(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=v(t,"weights","meanSquaredError")),je(o.shape,s.shape,"Error in meanSquaredError: ");let i=Pa(o,s);return Er(i,a,n)}var zN=S({meanSquaredError_:Vq});function Wq(r,e){let t=v(r,"labels","sigmoidCrossEntropyWithLogits"),n=v(e,"logits","sigmoidCrossEntropyWithLogits");je(t.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Tr(n),s=M(n,t),a=Du(er(He(At(n))));return ee(pe(o,s),a)}function jq(r,e,t,n=0,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"multiClassLabels","sigmoidCrossEntropy"),a=v(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","sigmoidCrossEntropy")),je(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=ce(n),c=ce(1),p=ce(.5);s=ee(M(s,pe(c,u)),M(p,u))}let l=Wq(s,a);return Er(l,i,o)}var BN=S({sigmoidCrossEntropy_:jq});function Gq(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Zr((o,s,a)=>{let l=jm(s,[t],!0),u=pe(oe(s,"float32"),l);a([o,u]);let c=He(M(u,o));return{value:be(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,y=Bo(f.shape,[t]);return[M(z(f,y),pe(oe(h,"float32"),er(g))),M(z(f,y),pe(er(g),oe(h,"float32")))]}}})(r,e)}function Uq(r,e,t,n=0,o=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),je(s.shape,a.shape,"Error in softmaxCrossEntropy: "),n>0){let u=ce(n),c=ce(1),p=ce(s.shape[1]);s=ee(M(s,pe(c,u)),fe(u,p))}let l=Gq(s,a);return Er(l,i,o)}var VN=S({softmaxCrossEntropy_:Uq});var qq={fft:Fa,ifft:zi,rfft:Oa,irfft:Wu},Hq={hammingWindow:xN,hannWindow:xg,frame:yg,stft:yN},As={flipLeftRight:wN,resizeNearestNeighbor:wg,resizeBilinear:bg,rotateWithOffset:_N,cropAndResize:bN,nonMaxSuppression:vN,nonMaxSuppressionAsync:IN,nonMaxSuppressionWithScore:SN,nonMaxSuppressionWithScoreAsync:NN,nonMaxSuppressionPadded:TN,nonMaxSuppressionPaddedAsync:EN},__={bandPart:AN,gramSchmidt:DN,qr:RN},Kq={absoluteDifference:FN,computeWeightedLoss:Er,cosineDistance:ON,hingeLoss:PN,huberLoss:MN,logLoss:LN,meanSquaredError:zN,sigmoidCrossEntropy:BN,softmaxCrossEntropy:VN};var Lr=class extends sg{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return De(s),t?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return ug(e,t)}dispose(){this.iterations_!=null&&De(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:ce(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Lr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var np=class extends Lr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n],a=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:V(()=>Se(s).variable(a))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:V(()=>Se(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedGrads[o].variable,u=this.accumulatedUpdates[o].variable;V(()=>{let c=ee(M(l,this.rho),M(Me(i),1-this.rho)),p=M(fe(_t(ee(u,this.epsilon)),_t(ee(l,this.epsilon))),i),m=ee(M(u,this.rho),M(Me(p),1-this.rho));l.assign(c),u.assign(m);let f=ee(M(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(De(this.accumulatedGrads.map(e=>e.variable)),De(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};np.className="Adadelta";nn(np);var op=class extends Lr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:V(()=>Sa(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;V(()=>{let l=ee(i,Me(a));i.assign(l);let u=ee(M(fe(a,_t(ee(l,D.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&De(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};op.className="Adagrad";nn(op);var sp=class extends Lr{constructor(e,t,n,o=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ce(t).variable(),this.accBeta2=ce(n).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=pe(1,this.accBeta1),o=pe(1,this.accBeta2);t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Se(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Se(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=ee(M(c,this.beta1),M(u,1-this.beta1)),f=ee(M(p,this.beta2),M(Me(u),1-this.beta2)),d=fe(m,n),h=fe(f,o);c.assign(m),p.assign(f);let g=ee(M(fe(d,ee(_t(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(M(this.accBeta1,this.beta1)),this.accBeta2.assign(M(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&De(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};sp.className="Adam";nn(sp);var ip=class extends Lr{constructor(e,t,n,o=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=ce(0).variable(),this.accBeta1=ce(t).variable()}),o==null&&(this.epsilon=D.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=pe(1,this.accBeta1),o=fe(-this.learningRate,ee(M(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=D.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Se(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Se(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=ee(M(c,this.beta1),M(u,1-this.beta1)),f=M(p,this.beta2),d=At(u),h=Sr(f,d);c.assign(m),p.assign(h);let g=ee(M(fe(o,n),fe(m,ee(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(ee(this.iteration,1)),this.accBeta1.assign(M(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&De(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&De(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};ip.className="Adamax";nn(ip);var ml=class extends Lr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=Array.isArray(e)?e[o].tensor:e[n];if(s==null)return;let a=D.registeredVariables[n];V(()=>{let i=ee(M(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=$t(ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ml.className="SGD";nn(ml);var ap=class extends ml{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=ce(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n];if(this.accumulations[o]==null){let l=!1;this.accumulations[o]={originalName:`${n}/momentum`,variable:V(()=>Se(s).variable(l))}}let a=this.accumulations[o].variable,i=Array.isArray(e)?e[o].tensor:e[n];i!=null&&V(()=>{let l,u=ee(M(this.m,a),i);this.useNesterov?l=ee(M(this.c,ee(i,M(u,this.m))),s):l=ee(M(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&De(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ap.className="Momentum";nn(ap);var lp=class extends Lr{constructor(e,t=.9,n=0,o=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=D.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=D.registeredVariables[n],a=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:V(()=>Se(s).variable(a))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:V(()=>Se(s).variable(a))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:V(()=>Se(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedMeanSquares[o].variable,u=this.accumulatedMoments[o].variable;V(()=>{let c=ee(M(l,this.decay),M(Me(i),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=ee(M(p,this.decay),M(i,1-this.decay)),f=fe(M(i,this.learningRate),_t(pe(c,ee(Me(m),this.epsilon)))),d=ee(M(u,this.momentum),f);l.assign(c),p.assign(m),u.assign(d);let h=pe(s,d);s.assign(h)}else{let p=ee(M(l,this.decay),M(Me(i),1-this.decay)),m=ee(M(u,this.momentum),fe(M(i,this.learningRate),_t(ee(p,this.epsilon))));l.assign(p),u.assign(m);let f=pe(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&De(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&De(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&De(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};lp.className="RMSProp";nn(lp);var Ma=class{static sgd(e){return new ml(e)}static momentum(e,t,n=!1){return new ap(e,t,n)}static rmsprop(e,t=.9,n=0,o=null,s=!1){return new lp(e,t,n,o,s)}static adam(e=.001,t=.9,n=.999,o=null){return new sp(e,t,n,o)}static adadelta(e=.001,t=.95,n=null){return new np(e,t,n)}static adamax(e=.002,t=.9,n=.999,o=null,s=0){return new ip(e,t,n,o,s)}static 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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o=n.map(i=>t.data.get(i.dataId).values),s=Ie(n[0].shape,n[0].dtype),a=s.values;for(let i=0;i<n.length;i++){let l=o[i];for(let u=0;u<a.length;u++)a[u]+=l[u]}return t.makeTensorInfo(s.shape,s.dtype,s.values)}var OE={kernelName:Hn,backendName:"cpu",kernelFunc:sK};function iK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;re(o,"all");let i=x.parseAxisParam(s,o.shape),l=i,u=T.getAxesPermutation(l,o.shape.length),c=o;u!=null&&(c=or({inputs:{x:o},backend:t,attrs:{perm:u}}),l=T.getInnerMostAxes(l.length,o.shape.length)),T.assertAxesAreInnerMostDims("all",l,c.shape.length);let[p,m]=T.computeOutAndReduceShapes(c.shape,l),f=x.sizeFromShape(m),d=x.makeZerosTypedArray(x.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let y=0;y<d.length;++y){let b=y*f,w=h[b];for(let _=0;_<f;++_){let k=h[b+_];w=w&&k}d[y]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let y=T.expandShapeToKeepDim(p,i),b=tt({inputs:{x:g},backend:t,attrs:{shape:y}});return t.disposeIntermediateTensorInfo(g),b}return g}var PE={kernelName:Gl,backendName:"cpu",kernelFunc:iK};function aK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;re(o,"any");let i=x.parseAxisParam(s,o.shape),l=i,u=T.getAxesPermutation(l,o.shape.length),c=o;u!=null&&(c=or({inputs:{x:o},backend:t,attrs:{perm:u}}),l=T.getInnerMostAxes(l.length,o.shape.length)),T.assertAxesAreInnerMostDims("any",l,c.shape.length);let[p,m]=T.computeOutAndReduceShapes(c.shape,l),f=x.sizeFromShape(m),d=x.makeZerosTypedArray(x.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let y=0;y<d.length;++y){let b=y*f,w=h[b];for(let _=0;_<f;++_){let k=h[b+_];w=w||k}d[y]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let y=T.expandShapeToKeepDim(p,i),b=tt({inputs:{x:g},backend:t,attrs:{shape:y}});return t.disposeIntermediateTensorInfo(g),b}return g}var ME={kernelName:Ul,backendName:"cpu",kernelFunc:aK};function lK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n;re(o,"argMax");let a=x.parseAxisParam(s,o.shape),i=T.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=or({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=T.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],T.assertAxesAreInnerMostDims("argMax",a,l.shape.length);let[c,p]=T.computeOutAndReduceShapes(l.shape,a),m=x.sizeFromShape(c),f=x.makeZerosTypedArray(m,"int32"),d=x.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let y=g*d,b=h[y],w=0;for(let _=0;_<d;++_){let k=h[y+_];k>b&&(b=k,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var LE={kernelName:Kn,backendName:"cpu",kernelFunc:lK};function uK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n;re(o,"argMin");let a=x.parseAxisParam(s,o.shape),i=T.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=or({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=T.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],T.assertAxesAreInnerMostDims("argMin",a,l.shape.length);let[c,p]=T.computeOutAndReduceShapes(l.shape,a),m=x.sizeFromShape(c),f=x.makeZerosTypedArray(m,"int32"),d=x.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let y=g*d,b=h[y],w=0;for(let _=0;_<d;++_){let k=h[y+_];k<b&&(b=k,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var zE={kernelName:na,backendName:"cpu",kernelFunc:uK};var cK=$e(Ks,r=>Math.asin(r)),BE={kernelName:Ks,backendName:"cpu",kernelFunc:cK};var pK=$e(Xs,r=>Math.asinh(r)),VE={kernelName:Xs,backendName:"cpu",kernelFunc:pK};var mK=$e(Ys,r=>Math.atan(r)),WE={kernelName:Ys,backendName:"cpu",kernelFunc:mK};var fK=Ze((r,e)=>Math.atan2(r,e)),dK=nt(Js,fK),jE={kernelName:Js,backendName:"cpu",kernelFunc:dK};var hK=$e(Zs,r=>Math.atanh(r)),GE={kernelName:Zs,backendName:"cpu",kernelFunc:hK};function mp(r,e,t,n,o,s){let a=o.strideHeight,i=o.strideWidth,l=o.dilationHeight,u=o.dilationWidth,c=o.effectiveFilterHeight,p=o.effectiveFilterWidth,m=o.padInfo.top,f=o.padInfo.left,d=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=Ie(o.outShape,t),g=h.values,y=o.outShape[1]*o.outShape[2]*o.outShape[3],b=o.outShape[2]*o.outShape[3],w=o.outShape[3];for(let _=0;_<o.batchSize;++_){let k=_*y,E=_*n[0];for(let N=0;N<o.inChannels;++N)for(let F=0;F<o.outHeight;++F){let O=F*a-m,P=Math.max(0,O),W=Math.min(o.inHeight,c+O),G=k+F*b;for(let j=0;j<o.outWidth;++j){let X=j*i-f,K=Math.max(0,X),Y=Math.min(o.inWidth,p+X),ne=d,J=0,Q=0;for(let ae=P;ae<W;ae+=l){let ue=E+ae*n[1];for(let le=K;le<Y;le+=u){let ge=ue+le*n[2],_e=r[ge+N];s==="max"&&_e>ne?ne=_e:s==="avg"&&(J+=_e,Q++)}if(isNaN(ne))break}let ie=G+j*w+N;g[ie]=s==="avg"?J/Q:ne}}}return h}function Ng(r,e,t,n,o=!1,s=!1){let a=Ie(n.outShape,"int32"),i=n.strideHeight,l=n.strideWidth,u=n.dilationHeight,c=n.dilationWidth,p=n.effectiveFilterHeight,m=n.effectiveFilterWidth,f=n.padInfo.top,d=n.padInfo.left,h=Ie(e,t,r);for(let g=0;g<n.batchSize;++g)for(let y=0;y<n.inChannels;++y)for(let b=0;b<n.outHeight;++b){let w=b*i-f,_=w;for(;_<0;)_+=u;let k=Math.min(n.inHeight,p+w);for(let E=0;E<n.outWidth;++E){let N=E*l-d,F=N;for(;F<0;)F+=c;let O=Math.min(n.inWidth,m+N),P=Number.NEGATIVE_INFINITY,W=-1;for(let G=_;G<k;G+=u){let j=G-w;for(let X=F;X<O;X+=c){let K=X-N,Y=h.get(g,G,X,y);Y>P&&(P=Y,o?W=s?((g*n.inHeight+G)*n.inWidth+X)*n.inChannels+y:(G*n.inWidth+X)*n.inChannels+y:W=j*m+K)}}a.set(W,g,b,E,y)}}return a}function Tg(r,e,t,n,o,s){let a=o.strideDepth,i=o.strideHeight,l=o.strideWidth,u=o.dilationDepth,c=o.dilationHeight,p=o.dilationWidth,m=o.effectiveFilterDepth,f=o.effectiveFilterHeight,d=o.effectiveFilterWidth,h=o.padInfo.front,g=o.padInfo.top,y=o.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=Ie(o.outShape,t),_=w.values,k=o.outShape[1]*o.outShape[2]*o.outShape[3]*o.outShape[4],E=o.outShape[2]*o.outShape[3]*o.outShape[4],N=o.outShape[3]*o.outShape[4],F=o.outShape[4];for(let O=0;O<o.batchSize;++O){let P=O*k,W=O*n[0];for(let G=0;G<o.inChannels;++G)for(let j=0;j<o.outDepth;++j){let X=j*a-h,K=X;for(;K<0;)K+=u;let Y=Math.min(o.inDepth,m+X),ne=P+j*E;for(let J=0;J<o.outHeight;++J){let Q=J*i-g,ie=Q;for(;ie<0;)ie+=c;let ae=Math.min(o.inHeight,f+Q),ue=ne+J*N;for(let le=0;le<o.outWidth;++le){let ge=le*l-y,_e=ge;for(;_e<0;)_e+=p;let ye=Math.min(o.inWidth,d+ge),ke=ue+le*F,Ae=b,Re=0,Pe=0;for(let mt=K;mt<Y;mt+=u){let gt=W+mt*n[1];for(let It=ie;It<ae;It+=c){let xt=gt+It*n[2];for(let yt=_e;yt<ye;yt+=p){let St=xt+yt*n[3],ot=r[St+G];if(s==="max"&&ot>Ae?Ae=ot:s==="avg"&&(Re+=ot,Pe++),isNaN(Ae))break}if(isNaN(Ae))break}if(isNaN(Ae))break}let ze=ke+G;_[ze]=s==="avg"?Re/Pe:Ae}}}}return w}function UE(r,e){let t=Ie(e.outShape,"int32"),n=e.strideDepth,o=e.strideHeight,s=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,m=e.padInfo.front,f=e.padInfo.top,d=e.padInfo.left;for(let h=0;h<e.batchSize;++h)for(let g=0;g<e.inChannels;++g)for(let y=0;y<e.outDepth;++y){let b=y*n-m,w=b;for(;w<0;)w+=a;let _=Math.min(e.inDepth,u+b);for(let k=0;k<e.outHeight;++k){let E=k*o-f,N=E;for(;N<0;)N+=i;let F=Math.min(e.inHeight,c+E);for(let O=0;O<e.outWidth;++O){let P=O*s-d,W=P;for(;W<0;)W+=l;let G=Math.min(e.inWidth,p+P),j=Number.NEGATIVE_INFINITY,X=-1;for(let K=w;K<_;K+=a){let Y=K-b;for(let ne=N;ne<F;ne+=i){let J=ne-E;for(let Q=W;Q<G;Q+=l){let ie=Q-P,ae=r.get(h,K,ne,Q,g);ae>=j&&(j=ae,X=Y*c*p+J*c+ie)}}}t.set(X,h,y,k,O,g)}}}return t}function gK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;re(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;x.assert(T.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=T.computePool3DInfo(s.shape,a,i,u,l,c),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,y=p.filterWidth,b=p.dilationDepth,w=p.dilationHeight,_=p.dilationWidth,k=p.effectiveFilterDepth,E=p.effectiveFilterHeight,N=p.effectiveFilterWidth,F=k-1-p.padInfo.front,O=N-1-p.padInfo.left,P=E-1-p.padInfo.top,W=Ie(s.shape,"float32"),G=1/(h*g*y),j=t.bufferSync(o);for(let X=0;X<p.batchSize;++X)for(let K=0;K<p.inChannels;++K)for(let Y=0;Y<p.inDepth;++Y)for(let ne=0;ne<p.inHeight;++ne)for(let J=0;J<p.inWidth;++J){let Q=Y-F,ie=ne-P,ae=J-O,ue=0;for(let le=0;le<k;le+=b){let ge=(Q+le)/m;if(!(ge<0||ge>=p.outDepth||Math.floor(ge)!==ge))for(let _e=0;_e<E;_e+=w){let ye=(ie+_e)/f;if(!(ye<0||ye>=p.outHeight||Math.floor(ye)!==ye))for(let ke=0;ke<N;ke+=_){let Ae=(ae+ke)/d;if(Ae<0||Ae>=p.outWidth||Math.floor(Ae)!==Ae)continue;ue+=j.get(X,ge,ye,Ae,K)}}}W.set(ue*G,X,Y,ne,J,K)}return t.makeTensorInfo(W.shape,W.dtype,W.values)}var KE={kernelName:Hl,backendName:"cpu",kernelFunc:yK};function bK(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;re([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=T.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=y-1-c.padInfo.top,k=Ie(a.shape,"float32"),E=1/(f*d),N=t.data.get(o.dataId).values,F=Ie(o.shape,"float32",N);for(let O=0;O<c.batchSize;++O)for(let P=0;P<c.inChannels;++P)for(let W=0;W<c.inHeight;++W)for(let G=0;G<c.inWidth;++G){let j=W-_,X=G-w,K=0;for(let Y=0;Y<y;Y+=h){let ne=(j+Y)/p;if(!(ne<0||ne>=c.outHeight||Math.floor(ne)!==ne))for(let J=0;J<b;J+=g){let Q=(X+J)/m;if(Q<0||Q>=c.outWidth||Math.floor(Q)!==Q)continue;K+=F.get(O,ne,Q,P)}}k.set(K*E,O,W,G,P)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var XE={kernelName:ql,backendName:"cpu",kernelFunc:bK};function wK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,scale:s,offset:a,mean:i,variance:l}=e;x.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),re([o,i,l,s,a],"batchNorm");let{varianceEpsilon:u}=n;u==null&&(u=.001);let c=t.data.get(o.dataId).values,p=t.data.get(i.dataId).values,m=t.data.get(l.dataId).values,f=s?t.data.get(s.dataId).values:new Float32Array([1]),d=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,y=f.length,b=m.length,w=p.length,_=0,k=0,E=0,N=0;for(let F=0;F<c.length;++F)h[F]=d[_++]+(c[F]-p[k++])*f[E++]/Math.sqrt(m[N++]+u),_>=g&&(_=0),k>=w&&(k=0),E>=y&&(E=0),N>=b&&(N=0);return t.makeTensorInfo(o.shape,o.dtype,h)}var YE={kernelName:io,backendName:"cpu",kernelFunc:wK};function _K(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;re([o],"batchToSpaceND");let i=s.reduce((y,b)=>y*b),l=T.getReshaped(o.shape,s,i),u=T.getPermuted(l.length,s.length),c=T.getReshapedPermuted(o.shape,s,i),p=T.getSliceBeginCoords(a,s.length),m=T.getSliceSize(c,a,s.length),f=tt({inputs:{x:o},backend:t,attrs:{shape:l}}),d=or({inputs:{x:f},backend:t,attrs:{perm:u}}),h=tt({inputs:{x:d},backend:t,attrs:{shape:c}}),g=Ho({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var ZE={kernelName:sa,backendName:"cpu",kernelFunc:_K};function vK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,u=af(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var JE={kernelName:Kl,backendName:"cpu",kernelFunc:vK};var kK=$e(Pn,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),QE={kernelName:Pn,backendName:"cpu",kernelFunc:kK};var CK=r=>{let{x:e}=r.inputs,t=r.backend,n=new Float32Array(x.sizeFromShape(e.shape)),o=t.data.get(e.dataId),s=o.complexTensorInfos.real,a=o.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];n[u]=Math.hypot(c,p)}return t.makeOutput(n,e.shape,"float32")},eA={kernelName:ia,backendName:"cpu",kernelFunc:CK};function Vi(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.data.get(n.dataId).complexTensorInfos.imag,s=t.data.get(o.dataId).values;return t.makeTensorInfo(o.shape,o.dtype,s)}var tA={kernelName:iu,backendName:"cpu",kernelFunc:Vi};function dl(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,e[0].shape)[0],a=T.computeOutShape(e.map(h=>h.shape),s);if(x.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(h=>x.sizeFromShape(h.shape)>0);if(i.length===1)return Dr({inputs:{x:i[0]},backend:t});let l=i.map(h=>h.shape);if(T.assertParamsConsistent(l,s),i[0].dtype==="complex64"){let h=i.map(_=>jo({inputs:{input:_},backend:t})),g=i.map(_=>Vi({inputs:{input:_},backend:t})),y=dl({inputs:h,backend:t,attrs:{axis:s}}),b=dl({inputs:g,backend:t,attrs:{axis:s}}),w=mr({inputs:{real:y,imag:b},backend:t});return h.forEach(_=>t.disposeIntermediateTensorInfo(_)),g.forEach(_=>t.disposeIntermediateTensorInfo(_)),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(b),w}let u=i.map(h=>{let g=x.sizeFromShape(h.shape.slice(s));return tt({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=T.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=lf(c,a,e[0].dtype,p),f=T.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return 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sA={kernelName:Jn,backendName:"cpu",kernelFunc:SK};function NK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n;re([o,s],"conv3d");let u=T.computeConv3DInfo(o.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,y=g.front,b=g.left,w=g.top,_=new ct(u.outShape,o.dtype),k=t.data.get(o.dataId).values,E=t.data.get(s.dataId).values,N=_.values,F=x.computeStrides(o.shape),O=x.computeStrides(s.shape);for(let P=0;P<u.batchSize;++P){let W=P*F[0],G=P*_.strides[0];for(let j=0;j<u.outDepth;++j){let X=G+j*_.strides[1],K=j*u.strideDepth-y;for(let Y=0;Y<c;++Y){let ne=K+Y*f;if(ne<0||ne>=u.inDepth)continue;let J=Y*O[0],Q=W+ne*F[1];for(let ie=0;ie<u.outHeight;++ie){let ae=X+ie*_.strides[2],ue=ie*u.strideHeight-w;for(let le=0;le<p;++le){let ge=ue+le*d;if(ge<0||ge>=u.inHeight)continue;let _e=J+le*O[1],ye=Q+ge*F[2];for(let ke=0;ke<u.outWidth;++ke){let 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      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;
  }
`;var Sv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=hl.DENSE;let t=gl(e),n=Lt();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${$s(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

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

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${n.output} = result;
      }
    `}};var Nv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=hl.DENSE;let t=gl(e),n=Lt();this.outputShape=e,this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${$s(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

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

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${n.output} = result;
      }
    `}};var Tv=class{constructor(e){this.variableNames=["A"],this.outTexUsage=$r.DOWNLOAD;let t=Lt();this.outputShape=e,this.userCode=`
      ${zg}

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

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}};var Av=class{constructor(e,t,n=!1){this.variableNames=["A"];let o=Lt(),[s,a]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
      ${gp(e)}

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

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

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

        int r = flatIndex / ${a};
        int c = imod(flatIndex, ${a});
        vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.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];
        }

        ${o.output} = vec4(${i}, 0., 0., 0.);
      }
    `}};var Dv=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Lt(),[s,a]=t;this.outputShape=e;let i="",l="result";n&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
          localCoords = coords;
          if(localCoords[2] + ${c} < ${e[2]}) {
            localCoords[2] += ${c};
            if(localCoords[1] + ${u} < ${e[1]}) {
              localCoords[1] += ${u};

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

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

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

              if(offset == 0) {
                result[${p}] = values[0];
              } else if(offset == 1) {
                result[${p}] = values[1];
              } else if(offset == 2) {
                result[${p}] = values[2];
              } else {
                result[${p}] = values[3];
              }
            }
          }
        `}this.userCode=`
      ${gp(e)}

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

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

        ${i}

        ${o.output} = ${l};
      }
    `}};var xD={};Ye(xD,{bindVertexProgramAttributeStreams:()=>Bv,createBufferFromOutputTexture:()=>jv,createFloat16MatrixTexture:()=>Pv,createFloat16PackedMatrixTexture:()=>zv,createFloat32MatrixTexture:()=>Ov,createIndexBuffer:()=>Fv,createPackedMatrixTexture:()=>Lv,createUnsignedBytesMatrixTexture:()=>Mv,createVertexBuffer:()=>Rv,createVertexShader:()=>$v,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Uv,downloadFloat32MatrixFromBuffer:()=>Gv,downloadMatrixFromPackedOutputTexture:()=>Hv,downloadPackedMatrixFromBuffer:()=>qv,getInternalFormatForFloat16MatrixTexture:()=>Vg,getInternalFormatForFloat16PackedMatrixTexture:()=>Gg,getInternalFormatForFloat32MatrixTexture:()=>Bg,getInternalFormatForPackedMatrixTexture:()=>jg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Wg,uploadDenseMatrixToTexture:()=>Vv,uploadPixelDataToTexture:()=>Wv});function $v(r){let e=Lt(),t=`${e.version}
    precision highp float;
    ${e.attribute} vec3 clipSpacePos;
    ${e.attribute} vec2 uv;
    ${e.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return iv(r,t)}function Rv(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return cv(r,e)}function Fv(r){let e=new Uint16Array([0,1,2,2,1,3]);return pv(r,e)}function vf(r,e,t,n,o,s){fv(e,t);let a=mv(r),i=r.TEXTURE_2D;return Ne(r,()=>r.bindTexture(i,a)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ne(r,()=>r.texImage2D(i,0,n,e,t,0,o,s,null)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function Bg(r){return r.internalFormatFloat}function Ov(r,e,t,n){let[o,s]=Ju(e,t);return vf(r,o,s,Bg(n),n.textureFormatFloat,r.FLOAT)}function Vg(r){return r.internalFormatHalfFloat}function Pv(r,e,t,n){let[o,s]=Ju(e,t);return vf(r,o,s,Vg(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Wg(r){return r.downloadTextureFormat}function Mv(r,e,t,n){let[o,s]=Ju(e,t);return vf(r,o,s,Wg(n),r.RGBA,r.UNSIGNED_BYTE)}function jg(r){return r.internalFormatPackedFloat}function Lv(r,e,t,n){let[o,s]=Wi(e,t);return vf(r,o,s,jg(n),r.RGBA,r.FLOAT)}function Gg(r){return r.internalFormatPackedHalfFloat}function zv(r,e,t,n){let[o,s]=Wi(e,t);return vf(r,o,s,Gg(n),r.RGBA,n.textureTypeHalfFloat)}function Bv(r,e,t){let n=0,o=3*4,s=3*4+2*4;return Ne(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Rg(r,e,"clipSpacePos",t,3,s,n)&&Rg(r,e,"uv",t,2,s,o)}function Vv(r,e,t,n,o,s){Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;o instanceof Uint8Array?(a=new Uint8Array(t*n*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*n*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(o),Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,n,0,r.RGBA,i,a)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Wv(r,e,t){Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function jv(r,e,t,n){let o=r.createBuffer();Ne(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let i=4*4*e*t;return Ne(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ne(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ne(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function Gv(r,e,t){let n=r,o=new Float32Array(t);return n.bindBuffer(n.PIXEL_PACK_BUFFER,e),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function Uv(r,e,t,n){let[o,s]=Ju(e,t),a=4,i=new Uint8Array(cD(e*t,a));return Ne(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function qv(r,e,t,n,o,s,a,i){let l=r,u=new Float32Array(pD(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Hv(r,e,t){let n=new Float32Array(e*t*4);return Ne(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,n)),n}var Ug=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=U().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,ov(t,e)):this.gl=Wn(t);let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(U().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=dp(this.gl,s),Cn(this.gl,a))this.textureHalfFloatExtension=dp(this.gl,a);else if(U().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Cn(this.gl,o))this.colorBufferHalfFloatExtension=dp(this.gl,o);else if(U().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Cn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Cn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Rv(this.gl),this.indexBuffer=Fv(this.gl),this.framebuffer=dv(this.gl),this.textureConfig=yf(this.gl,this.textureHalfFloatExtension)}get debug(){return U().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  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this.throwIfDisposed(),zv(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Lv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Fg(this.gl,this.framebuffer),this.outputTexture=null),Ne(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Uv(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,o,s,a){return qv(this.gl,e,t,n,o,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return Gv(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let o=jv(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(U().getBool("WEBGL_FENCE_API_ENABLED")){let o=e,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=o.clientWaitSync(s,0,0);return a===o.ALREADY_SIGNALED||a===o.CONDITION_SATISFIED},t=s}else U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Hv(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=av(t,e),o=$v(t),s=lv(t);return Ne(t,()=>t.attachShader(s,o)),Ne(t,()=>t.attachShader(s,n)),uv(t,s),this.debug&&bf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=Bv(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ne(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&bf(this.gl,this.program),Ne(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?hv(this.gl,e,t):gv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ne(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),xv(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[o,s]=Wi(t,n);this.setOutputMatrixTextureDriver(e,o,s)}setOutputMatrixWriteRegion(e,t,n,o){this.setOutputMatrixWriteRegionDriver(n,e,o,t)}setOutputPackedMatrixWriteRegion(e,t,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&bf(this.gl,this.program),hp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ne(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=dp(this.gl,U().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(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await x.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let 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o=this.gl;wf(o,e,this.framebuffer),this.debug&&hp(o),this.outputTexture=e,Ne(o,()=>o.viewport(0,0,t,n)),Ne(o,()=>o.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,o){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.scissor(e,t,n,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 V5(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:yD}=T;function bD(r,e,t,n){let o=[];r.forEach(d=>{let h=x.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`))});let s=o.join(`
`),a=r.map(d=>W5(d,e,n)).join(`
`),i=e.texShape,l=Lt(),u=U5(l),c,p,m=K5(l);return e.isPacked?(c=j5(e.logicalShape,i),p=H5(l)):(c=G5(e.logicalShape,i),p=q5(l)),n&&(m+=X5),[m,u,p,s,c,a,t].join(`
`)}function xp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Y5(r);case 1:return Z5(r);case 2:return J5(r);case 3:return Q5(r);case 4:return e8(r);case 5:return t8(r);case 6:return r8(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function wD(r){switch(r.shapeInfo.logicalShape.length){case 0:return n8(r);case 1:return o8(r);case 2:return s8(r);case 3:return i8(r);default:return a8(r)}}function W5(r,e,t=!1){let n="";t?n+=wD(r):n+=xp(r);let o=r.shapeInfo.logicalShape,s=e.logicalShape;return o.length<=s.length&&(t?n+=l8(r,e):n+=u8(r,e)),n}function j5(r,e){switch(r.length){case 0:return _D();case 1:return c8(r,e);case 2:return f8(r,e);case 3:return p8(r,e);default:return m8(r,e)}}function G5(r,e){switch(r.length){case 0:return _D();case 1:return d8(r,e);case 2:return b8(r,e);case 3:return h8(r,e);case 4:return g8(r,e);case 5:return x8(r,e);case 6:return y8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function U5(r){return`
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    }
  `}function q5(r){return`
    void setOutput(float val) {
      ${r.output} = vec4(val, 0, 0, 0);
    }
  `}function H5(r){return`
    void setOutput(vec4 val) {
      ${r.output} = val;
    }
  `}function K5(r){return`${r.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${r.varyingFs} vec2 resultUV;
    ${r.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${r.defineSpecialNaN}
    ${r.defineSpecialInf}
    ${r.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //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);
    }

    ${w8}
    ${_8}
    ${v8}
  `}var w8=`
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);
}
`,_8=`
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);
}
`,v8=`
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);
}
`,X5=`
  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 _D(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function c8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${t[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
    }
  `}function d8(r,e){return e[0]===1?`
      int getOutputCoords() {
        return int(resultUV.x * ${e[1]}.0);
      }
    `:e[1]===1?`
      int getOutputCoords() {
        return int(resultUV.y * ${e[0]}.0);
      }
    `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      return resTexRC.x * ${e[1]} + resTexRC.y;
    }
  `}function p8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),o=n*Math.ceil(r[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `}function h8(r,e){let t=$s(["r","c","d"],r);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${t}
      return ivec3(r, c, d);
    }
  `}function m8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),o=n*Math.ceil(r[r.length-2]/2),s=o,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
      int b${l} = index / ${s};
      index -= b${l} * ${s};
    `+a,i=`b${l}, `+i;return`
    ivec${r.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${a}

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

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

      return ivec${r.length}(${i});
    }
  `}function g8(r,e){let t=$s(["r","c","d","d2"],r);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${t}
      return ivec4(r, c, d, d2);
    }
  `}function x8(r,e){let t=$s(["r","c","d","d2","d3"],r);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
                             ${e[1]}));

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

      ${t}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function y8(r,e){let t=$s(["r","c","d","d2","d3","d4"],r);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;

      ${t}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function f8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(x.arraysEqual(r,e))return`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `;let n=Math.ceil(r[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));

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

      return ivec2(r, c);
    }
  `}function b8(r,e){return x.arraysEqual(r,e)?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
      }
    `:r[1]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${e[0]}, ${e[1]}));
        int index = resTexRC.x * ${e[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:r[0]===1?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${e[0]}, ${e[1]}));
        int index = resTexRC.x * ${e[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      int r = index / ${r[1]};
      int c = index - r * ${r[1]};
      return ivec2(r, c);
    }
  `}function Qu(r){return`offset${r}`}function n8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=Lt();return`
    vec4 ${t}() {
      return ${n.texture2D}(${e}, halfCR);
    }
  `}function Y5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[n,o]=r.shapeInfo.texShape;if(n===1&&o===1)return`
      float ${t}() {
        return sampleTexture(${e}, halfCR);
      }
    `;let[s,a]=r.shapeInfo.texShape,i=Qu(e);return`
    float ${t}() {
      vec2 uv = uvFromFlat(${s}, ${a}, ${i});
      return sampleTexture(${e}, uv);
    }
  `}function o8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=r.shapeInfo.texShape,o=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],s=Lt();return`
    vec4 ${t}(int index) {
      vec2 uv = packedUVfrom1D(
        ${o[0]}, ${o[1]}, index);
      return ${s.texture2D}(${e}, uv);
    }
  `}function Z5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
      float ${t}(int index) {
        ${yp(r)}
      }
    `;let n=r.shapeInfo.texShape,o=n[0],s=n[1];if(s===1&&o===1)return`
      float ${t}(int index) {
        return sampleTexture(${e}, halfCR);
      }
    `;let a=Qu(e);return s===1?`
      float ${t}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${o}.0);
        return sampleTexture(${e}, uv);
      }
    `:o===1?`
      float ${t}(int index) {
        vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
        return sampleTexture(${e}, uv);
      }
    `:`
    float ${t}(int index) {
      vec2 uv = uvFromFlat(${o}, ${s}, index + ${a});
      return sampleTexture(${e}, uv);
    }
  `}function s8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=o[0],a=o[1],i=Lt();if(o!=null&&x.arraysEqual(e,o))return`
      vec4 ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);

        return ${i.texture2D}(${t}, uv);
      }
    `;let l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=Math.ceil(e[1]/2);return`
    vec4 ${n}(int row, int col) {
      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
      return ${i.texture2D}(${t}, uv);
    }
  `}function J5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape;if(o!=null&&x.arraysEqual(e,o)){let p=o[0],m=o[1];return`
    float ${n}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
      return sampleTexture(${t}, uv);
    }
  `}let{newShape:s,keptDims:a}=x.squeezeShape(e),i=s;if(i.length<e.length){let p=bp(r,i),m=["row","col"];return`
      ${xp(p)}
      float ${n}(int row, int col) {
        return ${n}(${wp(m,a)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
        ${yp(r)}
      }
    `;let l=o[0],u=o[1],c=Qu(t);return u===1?`
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${t}, uv);
    }
  `:l===1?`
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
      return sampleTexture(${t}, uv);
    }
  `:`
  float ${n}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${e[1]} + col + ${c};
    vec2 uv = uvFromFlat(${l}, ${u}, index);
    return sampleTexture(${t}, uv);
  }
`}function i8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=bp(r,p),d=["b","row","col"];return`
        ${wD(f)}
        vec4 ${n}(int b, int row, int col) {
          return ${n}(${wp(d,m)});
        }
      `}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Lt();return`
    vec4 ${n}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${a}, ${i}, ${u}, ${l}, b, row, col);
      return ${c.texture2D}(${t}, uv);
    }
  `}function Q5(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=x.squeezeShape(e),l=a;if(l.length<e.length){let d=bp(r,l),h=["row","col","depth"];return`
        ${xp(d)}
        float ${n}(int row, int col, int depth) {
          return ${n}(${wp(h,i)});
        }
      `}if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${o}, ${s}, 1)));
        ${yp(r)}
      }
    `;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===o&&m==null)return`
        float ${n}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${s}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${p}.0, ${c}.0);
          return sampleTexture(${t}, uv);
        }
      `;if(p===s&&m==null)return`
    float ${n}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
      return sampleTexture(${t}, uv);
    }
  `;let f=Qu(t);return`
      float ${n}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${o} + col * ${s} + depth + ${f};
        vec2 uv = uvFromFlat(${c}, ${p}, index);
        return sampleTexture(${t}, uv);
      }
  `}function a8(r){let e=r.shapeInfo.logicalShape,t=e.length,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Lt();return`
    vec4 ${o}(${p}) {
      int index = ${m};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
      return ${f.texture2D}(${n}, uv);
    }
  `}function e8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[3],s=e[2]*o,a=e[1]*s,{newShape:i,keptDims:l}=x.squeezeShape(e);if(i.length<e.length){let d=bp(r,i),h=["row","col","depth","depth2"];return`
      ${xp(d)}
      float ${n}(int row, int col, int depth, int depth2) {
        return ${n}(${wp(h,l)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${a}, ${s}, ${o}, 1)));
        ${yp(r)}
      }
    `;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${s}, ${o}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${m}.0, ${p}.0);
        return sampleTexture(${t}, uv);
      }
    `;if(m===o&&u==null)return`
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${e[1]*e[2]}, ${e[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${m}.0, ${p}.0);
        return sampleTexture(${t}, uv);
      }
    `;let f=Qu(t);return`
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${a} + col * ${s} +
          depth * ${o} + depth2;
      vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
      return sampleTexture(${t}, uv);
    }
  `}function t8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[4],s=e[3]*o,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=x.squeezeShape(e);if(l.length<e.length){let h=bp(r,l),g=["row","col","depth","depth2","depth3"];return`
      ${xp(h)}
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        return ${n}(${wp(g,u)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${a}, ${s}, ${o})) +
          depth3;
        ${yp(r)}
      }
    `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${a}, ${s}, ${o}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${t}, uv);
      }
    `;if(f===o&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${e[1]*e[2]*e[3]},
               ${e[2]*e[3]}, ${e[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${t}, uv);
      }
    `;let d=Qu(t);return`
    float ${n}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${a} + depth * ${s} +
          depth2 * ${o} + depth3 + ${d};
      vec2 uv = uvFromFlat(${m}, ${f}, index);
      return sampleTexture(${t}, uv);
    }
  `}function r8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:o,keptDims:s}=x.squeezeShape(e);if(o.length<e.length){let g=bp(r,o),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${xp(g)}
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${n}(${wp(y,s)});
      }
    `}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${u}, ${l}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${a}, 1)));
        ${yp(r)}
      }
    `;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${i}, ${a})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${f}.0);
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        }
      `;else{let n=qt("rc",t),o=Be(t),s=k8(t,e,n),a=C8(t,e[e.length-1],e[e.length-2],n),i=I8(e,n);this.userCode=`
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          if(${s}) {
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            ${a}

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          }
        }
      `}}};function S8(r,e){let t=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function k8(r,e,t){if(r===1)return`rc > ${e[0]}`;let n="";for(let o=r-2;o<r;o++)n+=`${t[o]} >= ${e[o]}`,o<r-1&&(n+="||");return n}function C8(r,e,t,n){if(r===1)return"";let o=n.slice(-2);return`
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    int cp1 = c + 1;

    bool cEdge = cp1 >= ${e};
    bool rEdge = rp1 >= ${t};
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          cEdge ? 0. : getA(${n[1]}),
          rEdge ? 0. : getA(${n[2]}),
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        ${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}] =
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      `}this.userCode=`
      ${N8(t)}
      ${gp(e)}

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        vec4 result = vec4(0.);

        ivec3 thisRC;
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        int cols = ${e[2]};

        ${n}

        setOutput(result);
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    `}};function N8(r){return`
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  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`,l$=`
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
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  result.a = isNaN.a ? x.a : result.a;

  return result;
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  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
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  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;
`,Rs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
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      }

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

        setOutput(y);
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    `}};var Jv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=qt("rc",t),o=Be(t),s=JD(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
      void main() {
        ${o} rc = getOutputCoords();
        vec4 packedInput = getA(${s});

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    `}};var D8=Ar.whereImpl,$8=1e-7,R8=1e-4,Kg={};function F8(r){return r in Kg||(Kg[r]={}),Kg[r]}var O8=128,P8=600;function M8(){return U().global.screen==null?1024:U().global.screen.height*U().global.screen.width*window.devicePixelRatio*P8/1024/1024}var Xg=class extends js{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!U().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wn(U().getNumber("WEBGL_VERSION"));this.binaryCache=F8(U().getNumber("WEBGL_VERSION")),this.gpgpu=new Ug(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Yv(this.gpgpu),this.numMBBeforeWarning=M8(),this.texData=new el(this,Cs())}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((U().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||U().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={};return this.texData.set(o,{shape:t,dtype:n,values:e,usage:$r.UPLOAD,refCount:1,complexParentRefCount:0}),o}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,o){if(U().getBool("DEBUG")&&this.checkNumericalProblems(t),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:o,values:t,usage:$r.UPLOAD,refCount:1,complexParentRefCount:0})}disposeIntermediateTensorInfo(e){let t=e.dataId;if(this.texData.has(t)){let n=this.texData.get(t);n.refCount--,n.refCount<1&&this.disposeData(t)}}readSync(e){let t=this.texData.get(e),{values:n,dtype:o,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Rs(i,Cf):m=new sn(i,Cf);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(e);if(o==="string")return n;let u=this.activeTimers!=null,c;u&&(c=x.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=T.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=x.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:n,shape:o,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Rs(o,Cf):d=new sn(o,Cf);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(e);if(!U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&U().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&U().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...gl(o))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=T.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=x.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>x.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ie(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!sv(n))throw U().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:o}=this.texData.get(e),s=x.sizeFromShape(t);if(U().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...gl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=U().getBool("WEBGL_PACK")&&o===!0,i=a?_f(t):t,l=a?new Ev(i):new Tv(i),u=this.runWebGLProgram(l,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}async time(e){let t=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=x.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=x.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=x.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:x.now(),endMs:null}}endTimer(e){return U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=x.now(),e)}async getQueryTime(e){if(U().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e){if(this.pendingDisposal.has(e))return;if(this.pendingRead.has(e)){this.pendingDisposal.add(e),this.pendingDeletes++;return}if(!this.texData.has(e))return;if(this.texData.get(e).complexParentRefCount>0){this.texData.get(e).refCount--;return}this.releaseGPUData(e);let{complexTensorInfos:t}=this.texData.get(e);t!=null&&(this.texData.get(t.real.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.real),this.texData.get(t.imag.dataId).complexParentRefCount--,this.disposeIntermediateTensorInfo(t.imag)),this.texData.delete(e)}releaseGPUData(e){let{texture:t,dtype:n,texShape:o,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(t,o,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return U().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Cs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=O8){let n=this.getCPUBackend();return!U().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(o=>this.texData.get(o.dataId).texture==null&&x.sizeFromShape(o.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return D8(e.shape,t)}packedUnaryOp(e,t,n){let o=new Rs(e.shape,t);return this.compileAndRun(o,[e],n)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=Hg(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(U().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Zv,e.dtype);let t=new sn(e.shape,Zv);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&x.isString(n[0])){let s=n.map(a=>x.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return this.texData.get(o).usage=null,{dataId:o,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:o}=this.makeTensorInfo(e,t,n);return Cs().makeTensorFromDataId(o,e,t,this)}unpackTensor(e){let t=new Jv(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xv(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ba(e.shape),...Va(e.shape)],o={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Ba(t),...Va(t)],a=new kf(s,n),i=!0,l=this.runWebGLProgram(a,[o],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:o,dtype:s}=t,a=_f(o),i;n?i=new Nv(a):i=new Sv(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:o,dataId:u.dataId}}runWebGLProgram(e,t,n,o,s=!1){let a=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===hl.DENSE){let h=gl(e.outputShape);i.texShape=h.map(g=>g*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),x.sizeFromShape(a.shape)===0)return i.values=x.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(h=>{if(h.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(h.dataId);if(g.texture==null){if(!e.packedInputs&&x.sizeFromShape(h.shape)<=U().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:h.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=h.shape)}else if(!!g.isPacked!=!!e.packedInputs)h=g.isPacked?this.unpackTensor(h):this.packTensor(h),l.push(h),g=this.texData.get(h.dataId);else if(g.isPacked&&!xl(g.shape,h.shape)){let y=h,b=h.shape;h.shape=g.shape,h=this.packedReshape(h,b),l.push(h),g=this.texData.get(h.dataId),y.shape=b}return this.uploadToGPU(h.dataId),{shape:h.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=ID(e,u,c),m=this.getAndSaveBinary(p,()=>vD(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;if(f&&(d=this.startTimer()),CD(this.gpgpu,m,u,c,o),l.forEach(h=>this.disposeIntermediateTensorInfo(h)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)})),!U().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let h=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),h}return a}compileAndRun(e,t,n,o,s=!1){n=n||t[0].dtype;let a=this.runWebGLProgram(e,t,n,o,s);return Cs().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(U().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!U().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=U().getBool("DEBUG");U().set("DEBUG",!1);let t=this.abs(ce(1e-8)).dataSync()[0];if(U().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?$8:R8}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:o,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=x.now());let p=t.texShape;if(p==null&&(p=yv(n,l),t.texShape=p),s!=null){let m=_f(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Wi(p[0],p[1]),f=new Dv(m,[h,d],g)):f=new Av(m,[h,d],g);let y=this.makeTensorInfo([h,d],o);g?this.texData.get(y.dataId).usage=$r.PIXELS:this.texData.get(y.dataId).usage=$r.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),d,h,s);let b=!0,w=this.runWebGLProgram(f,[y],o,null,b),_=this.texData.get(w.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(w.dataId),t.values=null,u&&(this.uploadWaitMs+=x.now()-c)}else{let m=this.acquireTexture(p,i,o,l);t.texture=m}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:o}=n;return this.releaseGPUData(e),t!=null&&(n.values=L8(t,o)),n.values}acquireTexture(e,t,n,o){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,o)}computeBytes(e,t){return e[0]*e[1]*x.bytesPerElement(t)}};function L8(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<t.length;++n)t[n]=Math.round(r[n]);return t}else throw new Error(`Unknown dtype ${e}`)}var Qv="2.8.3";function ek(){U().set("WEBGL_FORCE_F16_TEXTURES",!0)}Wc.isBrowser()&&_u("webgl",()=>new Xg,2);var z8={forceHalfFloat:ek};var Yg=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;var Ko=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}};var yl=`
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;var Fs=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length,a="";if(o)if(s===0||x.sizeFromShape(this.outputShape)===1)a=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(a=`
          ${Be(s)} coords = getOutputCoords();
        `,s===1)a+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let l=qt("coords",s);a+=`
            bool nextRowOutOfBounds =
              (${l[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${l[s-1]} + 1) >= ${this.outputShape[s-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

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

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

        setOutput(result);
      }
    `}};function Ht(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var c$={kernelName:cs,backendName:"webgl",kernelFunc:Ht};function an(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=Ht({inputs:{x:n},backend:t}),l=t.texData.get(i.dataId);l.complexParentRefCount++;let u=Ht({inputs:{x:o},backend:t}),c=t.texData.get(u.dataId);return c.complexParentRefCount++,a.complexTensorInfos={real:i,imag:u},s}var p$={kernelName:Xl,backendName:"webgl",kernelFunc:an};var tk="return (a < 0.) ? b * a : a;",rk=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function B8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",x.createScalarValue(s,"float32")),i=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fs(rk,o.shape,a.shape):new Ko(tk,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],o.dtype);return t.disposeIntermediateTensorInfo(a),l}var m$={kernelName:lo,backendName:"webgl",kernelFunc:B8};var nk="return (a < 0.) ? b * a : a;",ok=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function V8(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fs(ok,n.shape,o.shape):new Ko(nk,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)}var f$={kernelName:_o,backendName:"webgl",kernelFunc:V8};var Zg="if (isnan(x)) return x;",d$=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,h$=`
  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 Ce({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=U().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Rs(a.shape,e):c=new sn(a.shape,r),i.runWebGLProgram(c,[a],l)}}function at({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,k]=w,E={dataId:_.dataId,dtype:_.dtype,shape:l.shape},N={dataId:k.dataId,dtype:k.dtype,shape:u.shape},F=new Ko(r,l.shape,u.shape);return c.runWebGLProgram(F,[E,N],dr(_.dtype,k.dtype))}),b=an({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),b}let p=s||dr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&o!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=o(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(y,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Fs(e,l.shape,u.shape,t):f=new Ko(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function bl(r,e=!1){if(r==="linear")return e?i$:r$;if(r==="relu")return e?l$:o$;if(r==="elu")return e?a$:n$;if(r==="relu6")return e?u$:s$;if(r==="prelu")return e?ok:nk;if(r==="leakyrelu")return e?rk:tk;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var If=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",y="";i&&(l?g=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }`:u?g=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }`:g=`vec4 activation(vec4 x) {
          ${i}
        }`,y="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${g}

      const float sharedDimension = ${p}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${p}; i++) {
          int batchA = ${w};
          int batchB = ${_};
          vec4 a = getMatrixA(batchA, ${m});
          vec4 b = getMatrixB(batchB, ${f});

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

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

        ${b}

        ${y}

        setOutput(result);
      }
    `}};var sk={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Jg=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}};var g$="return a * b;";function ik(r){let{inputs:e,backend:t}=r,{a:n,b:o}=e,s=T.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),u=new Jg(sk.REAL,n.shape,o.shape),c=new Jg(sk.IMAG,n.shape,o.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:n.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:o.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:o.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=an({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([n,o])){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),[u,c]=VD(n.shape,o.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Fs(g$,n.shape,o.shape):a=new Ko(g$,n.shape,o.shape),t.runWebGLProgram(a,[n,o],s)}var x$={kernelName:xo,backendName:"webgl",kernelFunc:ik};function y$(r,e,t){let n=[Ba(r.shape),...Va(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Ba(e),...Va(e)],a=new kf(s,n),i=!0,l=t.runWebGLProgram(a,[o],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function me(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{shape:s}=n,a=t,i=x.sizeFromShape(o.shape),l=x.inferFromImplicitShape(s,i),u=x.sizeFromShape(l);x.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!xl(o.shape,l)&&!(c.texture!==null&&xl(c.shape,l))?y$(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var b$={kernelName:ds,backendName:"webgl",kernelFunc:me};var Qg=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${x.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return 0.0;
        }
      `),this.userCode=`
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

        float sumValue = 0.0;

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

          ${u}
        }

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

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

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

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

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

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

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

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

          ${m}
        }

        int inIdx = inOffset + ${c};
        if (${p===1}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

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

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

          ${m}
        }
        setOutput(${u});
      }
    `}};function W8(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=T.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function In(r,e,t,n){let o=W8(r.shape),s=r;for(let a=0;a<o.length;a++){let{inSize:i,windowSize:l,outSize:u}=o[a],c,p;t==="mean"?c=a===0?new Qg({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new Qg({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new ak({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=n.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var lk=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let o=Be(this.rank),s=j8(t);this.userCode=`
    void main() {
      ${o} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function j8(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(e);for(let o=0;o<r.length;o++)n[r[o]]=t[o];return n.join()}var uk=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Be(this.rank),s=Kv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
    void main() {
      ${o} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${u};
      if(${l}) {
        result[1] = ${u};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${u};
        if(${l}) {
          result[3] = ${u};
        }
      }
      setOutput(result);
    }
    `}};function wl(r,e,t){let n=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uk(r.shape,e):new lk(r.shape,e);return t.runWebGLProgram(n,[r],r.dtype)}function w$(r,e,t,n){let o=e,s=r.shape.length,a=x.parseAxisParam(o,r.shape),i=a,l=T.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=wl(r,l,n),i=T.getInnerMostAxes(i.length,s)),T.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=T.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=T.expandShapeToKeepDim(p,a));let d=x.sizeFromShape(m),g=x.sizeFromShape(r.shape)/d,y=me({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=yu(r.dtype),w=In(y,b,"sum",n),_=me({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(w),u&&n.disposeIntermediateTensorInfo(c),_}function Sf(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;return w$(o,s,a,t)}var _$={kernelName:Do,backendName:"webgl",kernelFunc:Sf};function Vt(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{perm:s}=n,a=t,i=o.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=o.shape[s[c]];let u;if(a.shouldExecuteOnCPU([o])){let p=a.texData.get(o.dataId).values,m=_p(p,o.shape,o.dtype,s,l);u=a.makeTensorInfo(l,o.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=wl(o,s,a);return u}var v$={kernelName:Po,backendName:"webgl",kernelFunc:Vt};var ck=1e3;function ec({a:r,b:e,transposeA:t,transposeB:n,backend:o,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=n?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=n?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),y=x.sizeFromShape(h),b=x.sizeFromShape(g),w=y===b||y===1||b===1;x.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let k=(y>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);x.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${n} must match.`);let E=t?[y,p,f]:[y,f,p],N=n?[b,d,m]:[b,m,d],F=me({inputs:{x:r},backend:o,attrs:{shape:E}}),O=me({inputs:{x:e},backend:o,attrs:{shape:N}}),P=[F,O],W=Math.max(y,b),G=t?F.shape[1]:F.shape[2],j=s!=null,X=a!=null,K=l==="leakyrelu",Y=l!=null?bl(l,!0):null,ne=j||X||K||Y!=null,J;if((f===1||d===1)&&G>ck&&ne===!1){let ie=F,ae=O;t&&(ie=Vt({inputs:{x:F},backend:o,attrs:{perm:[0,2,1]}}),P.push(ie)),n&&(ae=Vt({inputs:{x:O},backend:o,attrs:{perm:[0,2,1]}}),P.push(ae));let ue=d!==1,le=d===1,ge=ie;ue&&(ge=me({inputs:{x:ie},backend:o,attrs:{shape:[W,G,1]}}),P.push(ge));let _e=d===1?2:1,ye=ae;le&&(ye=me({inputs:{x:ae},backend:o,attrs:{shape:[W,1,G]}}),P.push(ye));let ke=ik({inputs:{a:ge,b:ye},backend:o});J=Sf({inputs:{x:ke},backend:o,attrs:{axis:_e,keepDims:!0}}),P.push(ke)}else{let ie=dr(r.dtype,e.dtype),ae=new If(E,N,[W,f,d],t,n,j,Y,X,K),ue=[F,O];if(s!=null&&ue.push(s),X&&ue.push(a),K){let le=o.makeTensorInfo([],"float32",x.createScalarValue(i,"float32"));ue.push(le),P.push(le)}J=o.runWebGLProgram(ae,ue,ie)}let Q=me({inputs:{x:J},backend:o,attrs:{shape:k}});P.push(J);for(let ie of P)o.disposeIntermediateTensorInfo(ie);return Q}function G8(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n;return ec({a:o,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var k$={kernelName:ws,backendName:"webgl",kernelFunc:G8};var C$="return abs(x);";function U8(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=t.texData.get(n.dataId),a=Hg(s.values);return t.makeTensorInfo(n.shape,n.dtype,a)}let o;return U().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Rs(n.shape,C$):o=new sn(n.shape,C$),t.runWebGLProgram(o,[n],n.dtype)}var I$={kernelName:is,backendName:"webgl",kernelFunc:U8};var q8=gr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,H8=Ce({opSnippet:q8}),S$={kernelName:qs,backendName:"webgl",kernelFunc:H8};var K8=gr+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,X8=Ce({opSnippet:K8}),N$={kernelName:Hs,backendName:"webgl",kernelFunc:X8};var T$="return a + b;",Y8=at({opSnippet:T$,packedOpSnippet:T$,supportsComplex:!0,cpuKernelImpl:SD}),E$={kernelName:bn,backendName:"webgl",kernelFunc:Y8};var pk=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        float result = ${o};
        setOutput(result);
      }
    `}};var mk=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${o};
        setOutput(result);
      }
    `}};function ex(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return Ht({inputs:{x:n[0]},backend:t});if(n.length>U().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=ex({inputs:n.slice(0,l),backend:t}),c=ex({inputs:n.slice(l),backend:t});return ex({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>dr(l,u)),s=n.map(l=>l.shape),i=U().getBool("WEBGL_PACK")?new mk(n[0].shape,s):new pk(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var A$={kernelName:Hn,backendName:"webgl",kernelFunc:ex};function Z8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=T.getAxesPermutation(u,i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("all",u,i);let[m,f]=T.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=In(h,h.dtype,"all",t),y;if(a){let b=T.expandShapeToKeepDim(m,l);y=me({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=me({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var D$={kernelName:Gl,backendName:"webgl",kernelFunc:Z8};function J8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=T.getAxesPermutation(u,i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("any",u,i);let[m,f]=T.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=In(h,h.dtype,"any",t),y;if(a){let b=T.expandShapeToKeepDim(m,l);y=me({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=me({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var $$={kernelName:Ul,backendName:"webgl",kernelFunc:J8};var fk=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${o};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${o}; i++) {
          int inIdx = ${l};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}};var dk=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,x.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Be(l),c=qt("coords",l),p,m;if(a===1){m=l+1;let F=Be(m);p=`
        ${F} sourceLocR = ${F}(${c.join()}, 0);
        ++${c[l-1]};
        ${F} sourceLocG = ${F}(${c.join()}, 0);
        ++${c[l-2]};
        ${F} sourceLocA = ${F}(${c.join()}, 0);
        --${c[l-1]};
        ${F} sourceLocB = ${F}(${c.join()}, 0);
        --${c[l-2]};`}else m=l,p=`
        ${u} sourceLocR = coords;
        ++${c[l-1]};
        ${u} sourceLocG = coords;
        ++${c[l-2]};
        ${u} sourceLocA = coords;
        --${c[l-1]};
        ${u} sourceLocB = coords;
        --${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(F=>"int "+F),g=qt("sourceLocR",m-1).concat("inIdx.r"),y=qt("sourceLocG",m-1).concat("inIdx.g"),b=qt("sourceLocB",m-1).concat("inIdx.b"),w=qt("sourceLocA",m-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",k=o?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${w.join()})));`,E=`vec4(
            getAChannel(${g.join()}),
            hasNextCol ? getAChannel(${y.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,N=o?"":`
      float getBestIndicesAChannel(${h.join()}) {
        return getChannel(getBestIndicesA(${f.join()}),
                                          vec2(${f.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${h.join()}) {
        return getChannel(getA(${f.join()}),
                               vec2(${f.slice(-2).join()}));
      }
      ${N}
      void main() {
        ${u} coords = getOutputCoords();
        bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
        bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
        ${p}
        ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
          sourceLocB${d}, sourceLocA${d}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${E};

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

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function R$(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=T.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new fk(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=R$(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function F$(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=T.computeOptimalWindowSize(s),i=new dk(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=F$(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function tx(r,e,t,n){let o=[t];if(T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!U().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=T.computeOutAndReduceShapes(e.shape,o),l=x.sizeFromShape(i),u=me({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=R$(r,u,n);s.push(c);let p=me({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return F$(r,e,n)}function Q8(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=x.parseAxisParam(s,o.shape),i=T.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Vt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=T.getInnerMostAxes(a.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=tx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var O$={kernelName:Kn,backendName:"webgl",kernelFunc:Q8};function eX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=x.parseAxisParam(s,o.shape),i=T.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Vt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=T.getInnerMostAxes(a.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=tx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var P$={kernelName:na,backendName:"webgl",kernelFunc:eX};var tX=gr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,rX=Ce({opSnippet:tX}),M$={kernelName:Ks,backendName:"webgl",kernelFunc:rX};var nX=gr+"return log(x + sqrt(x * x + 1.0));",oX=Ce({opSnippet:nX}),L$={kernelName:Xs,backendName:"webgl",kernelFunc:oX};var sX=gr+`
  return atan(x);
`,iX=Ce({opSnippet:sX}),z$={kernelName:Ys,backendName:"webgl",kernelFunc:iX};var aX=d$+`
  return atan(a, b);
`,lX=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+h$+`
  return result;
`,uX=at({opSnippet:aX,packedOpSnippet:lX}),B$={kernelName:Js,backendName:"webgl",kernelFunc:uX};var cX=gr+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,pX=Ce({opSnippet:cX}),V$={kernelName:Zs,backendName:"webgl",kernelFunc:pX};var ji=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let F=">=";this.userCode=`
        const ivec2 strides = ivec2(${i}, ${l});
        const ivec2 pads = ivec2(${f}, ${d});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${p};
              wR += ${u}) {
            int xR = xRCorner + wR;

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

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

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

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${F} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${o?s?g:y:`wR * ${m} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let k=Math.floor(a/4)*4,E=a%4,N=`
      if (${h}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${w}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${i}, ${l});
      const ivec2 pads = ivec2(${f}, ${d});
      const float initializationValue = ${b};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${b});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${p};
            wR += ${u}) {
          int xR = xRCorner + wR;

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

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

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

            ${N}
          }

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

            ${N}
          } else if (${E===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              initializationValue,
              initializationValue
            );

            ${N}
          } else if (${E===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              getValue(batch, xR, xC + 2 * ${c}, d),
              initializationValue
            );

            ${N}
          }
        }
        setOutput(${_});
      }
    `}},tc=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,y=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),n){let P=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${i}, ${l}, ${u});
        const ivec3 pads = ivec3(${g}, ${y}, ${b});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${f};
              wD += ${c}) {
            int xD = xDCorner + wD;

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

            for (int wR = 0; wR < ${d};
                wR += ${p}) {
              int xR = xRCorner + wR;

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

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

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

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${P} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${o?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
                      wR * ${h} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let k="max",E=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(E="avgValue / count");let N=Math.floor(a/4)*4,F=a%4,O=`
      if (${w}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${k}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${i}, ${l}, ${u});
      const ivec3 pads = ivec3(${g}, ${y}, ${b});
      const float initializationValue = ${_};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${_});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${f};
            wD += ${c}) {
          int xD = xDCorner + wD;

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

          for (int wR = 0; wR < ${d};
            wR += ${p}) {
            int xR = xRCorner + wR;

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

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

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

              ${O}
            }

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

              ${O}
            } else if (${F===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                initializationValue,
                initializationValue
              );

              ${O}
            } else if (${F===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                getValue(batch, xD, xR, xC + 2 * ${m}, ch),
                initializationValue
              );

              ${O}
            }
          }
          setOutput(${E});
        }
      }
    `}};function mX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Ds(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;x.assert(T.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=T.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:o},backend:t});let p=new ji(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var W$={kernelName:Xn,backendName:"webgl",kernelFunc:mX};function fX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=T.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new tc(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var j$={kernelName:oa,backendName:"webgl",kernelFunc:fX};var hk=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${c}, ${p});
      const float avgMultiplier = float(${m});

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

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${l};
            wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

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

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

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

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

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},gk=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,y=1/(t*n*o);this.userCode=`
      const ivec3 pads = ivec3(${d}, ${h}, ${g});
      const float avgMultiplier = float(${y});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

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

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

          for (int wR = 0; wR < ${m};
              wR += ${u}) {
            float dyR = float(dyRCorner + wR) / ${a}.0;

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

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

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

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

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function dX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=T.computePool3DInfo(a.shape,i,l,p,u,c),f=new gk(m);return t.runWebGLProgram(f,[o],a.dtype)}var G$={kernelName:Hl,backendName:"webgl",kernelFunc:dX};function hX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;Ds([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=T.computePool2DInfo(a.shape,i,l,1,u),p=new hk(c);return t.runWebGLProgram(p,[o],a.dtype)}var U$={kernelName:ql,backendName:"webgl",kernelFunc:hX};function gX(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return ec({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var q$={kernelName:Yn,backendName:"webgl",kernelFunc:gX};var xk=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(T.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${l};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}};var yk=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(T.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${l};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}};var xX=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;x.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=U().getBool("WEBGL_PACK_NORMALIZATION")?new yk(n.shape,o.shape,s.shape,c,p,l):new xk(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},H$={kernelName:io,backendName:"webgl",kernelFunc:xX};var bk=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Be(this.rank),n=`uniform int start[${this.rank}];`,o=yX(this.rank),s,a=e.map((i,l)=>`sourceLoc.${wk[l]} = start[${l}] + coords.${wk[l]};`);s=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${a.join(`
`)}
      `,this.userCode=`
      ${n}
      void main() {
        ${s}
        setOutput(getSource(${o}));
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},wk=["x","y","z","w","u","v"];function yX(r){if(r===1)return"sourceLoc";if(r<=6)return wk.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var _k=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Be(this.rank),n=qt("coords",this.rank),o=qt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=`
      result.x = ${a};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${o[this.rank-1]};
        result.y = ${a};
        --${o[this.rank-1]};
      }
    `,l=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${o[this.rank-2]};
        result.z = ${a};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${o[this.rank-1]};
          result.w = ${a};
        }
      }
    `,u=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
      uniform int start[${this.rank}];
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${u}
        vec4 result = vec4(0.);
        ${i}
        ${l}
        setOutput(result);
      }
    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function bX(r,e,t,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(t,r.dtype),a=n.texData.get(s.dataId);Object.assign(a,o),a.shape=t,a.dtype=r.dtype;let i=ir.computeFlatOffset(e,x.computeStrides(r.shape));o.slice&&(i+=o.slice.flatOffset),a.slice={flatOffset:i,origDataId:o.slice&&o.slice.origDataId||r.dataId};let l=n.dataRefCount.get(a.slice.origDataId)||1;return n.dataRefCount.set(a.slice.origDataId,l+1),s}function Wa(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,size:a}=n,[i,l]=ir.parseSliceParams(o,s,a);if(ir.assertParamsValid(o,i,l),x.sizeFromShape(l)===0)return t.makeTensorInfo(l,o.dtype,[]);if(t.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=t.texData.get(o.dataId),m=qD(p.values,i,l,o.shape,o.dtype);return t.makeTensorInfo(l,o.dtype,m)}let{isPacked:u}=t.texData.get(o.dataId),c=ir.isSliceContinous(o.shape,i,l);if(u||!c){let p=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _k(l):new bk(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[o],o.dtype,m)}return t.uploadToGPU(o.dataId),bX(o,i,l,t)}var K$={kernelName:gs,backendName:"webgl",kernelFunc:Wa};var wX=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;x.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),l=T.getReshaped(o.shape,s,i),u=T.getPermuted(l.length,s.length),c=T.getReshapedPermuted(o.shape,s,i),p=T.getSliceBeginCoords(a,s.length),m=T.getSliceSize(c,a,s.length),f=[],d=me({inputs:{x:o},backend:t,attrs:{shape:l}}),h=Vt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),y=Wa({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),y},X$={kernelName:sa,backendName:"webgl",kernelFunc:wX};function _X(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.readSync(o.dataId),l=t.readSync(s.dataId),u=qg(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var Y$={kernelName:Kl,backendName:"webgl",kernelFunc:_X};var vX="return float(a != b);",vk=at({opSnippet:vX,dtype:"bool"}),Z$={kernelName:yi,backendName:"webgl",kernelFunc:vk};function ja(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Ht({inputs:{x:o.complexTensorInfos.real},backend:t})}var J$={kernelName:fu,backendName:"webgl",kernelFunc:ja};var kX="return float(int(x));";function Q$(r,e){let t=new sn(r.shape,kX),n=e.runWebGLProgram(t,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function kk(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Ht({inputs:{x:o},backend:t});let a=pt(o.shape),i=kk({inputs:{x:o},backend:t,attrs:{dtype:"float32"}}),l=an({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(o.dtype==="complex64"){let a=ja({inputs:{input:o},backend:t}),i=kk({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!x.hasEncodingLoss(o.dtype,s)){let a=Ht({inputs:{x:o},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return Q$(o,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",x.getTypedArrayFromDType("bool",1)),l=vk({inputs:{a:o,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var eR={kernelName:On,backendName:"webgl",kernelFunc:kk};var tR="return ceil(x);",CX=Ce({opSnippet:tR,packedOpSnippet:tR,cpuKernelImpl:TD}),rR={kernelName:Qs,backendName:"webgl",kernelFunc:CX};var Ck=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};var Ik=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
      uniform float minVal;
      uniform float maxVal;

      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function IX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i;U().getBool("WEBGL_PACK_CLIP")?i=new Ik(o.shape):i=new Ck(o.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[o],o.dtype,l)}var nR={kernelName:Pn,backendName:"webgl",kernelFunc:IX};var Sk=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `}};function oR(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function SX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=t.texData.get(n.dataId),s=new Sk(n.shape),a=[oR(n,o.complexTensorInfos.real),oR(n,o.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var sR={kernelName:ia,backendName:"webgl",kernelFunc:SX};var Nk=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let o=t.length,s=t[t.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}};var Tk=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=T.computeOutShape(e,t);let n=this.outputShape,o=n.length,s=Be(o),a=qt("coords",o),i=["x","y","z","w","u","v"].slice(0,o);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
        return getChannel(
            getT0(${p}), vec2(${c.join()}));
        }`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
        if (${u} < ${l[h]}  && ${u} >= ${l[h-1]}) {
          return getChannel(
            getT${h}(${rx(i,u,g)}),
            vec2(${rx(c,u,g)}));
        }`}let f=l.length,d=l[l.length-1];m+=`
        return getChannel(
          getT${f}(${rx(i,u,d)}),
          vec2(${rx(c,u,d)}));`,this.userCode=`
      float getValue(${i.map(h=>"int "+h)}) {
        ${m}
      }

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

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

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

        ${a[o-1]} = ${a[o-1]} - 1;
        if (${a[o-2]} < ${n[o-2]} &&
            ${a[o-1]} < ${n[o-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function rx(r,e,t){let n=r.indexOf(e);return r.map((s,a)=>a===n?`${s} - ${t}`:s).join()}function rc(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return Ht({inputs:{x:o.complexTensorInfos.imag},backend:t})}var iR={kernelName:iu,backendName:"webgl",kernelFunc:rc};function nc(r,e,t){let n=r[0].dtype;if(n==="complex64"){let u=r.map(d=>ja({inputs:{input:d},backend:t})),c=r.map(d=>rc({inputs:{input:d},backend:t})),p=nc(u,e,t),m=nc(c,e,t),f=an({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(n==="string"){let{tensors2D:u,outShape:c}=aR(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=ED(p,c,n,m),d=T.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,n,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>U().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=nc(r.slice(0,u),e,t),p=nc(r.slice(u),e,t),m=nc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new Tk(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,n)}let{tensors2D:o,outShape:s}=aR(r,e,t),a=new Nk(o.map(u=>u.shape)),i=t.runWebGLProgram(a,o,n);o.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=me({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function aR(r,e,t){let n=T.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>me({inputs:{x:s},attrs:{shape:[-1,x.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:n}}function Ek(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,e[0].shape)[0],a=T.computeOutShape(e.map(u=>u.shape),s);if(x.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>x.sizeFromShape(u.shape)>0);if(i.length===1)return Ht({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,s),nc(i,s,t)}var lR={kernelName:as,backendName:"webgl",kernelFunc:Ek};var Nf=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",y=g?1:2,b=g?2:3,w=g?3:1,_="",k="";n&&(o?_=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?_=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:_=`
          float activation(float x) {
            ${n}
          }
        `,k="result = activation(result);");let E=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${_}

      const ivec2 strides = ivec2(${l}, ${u});
      const ivec2 pads = ivec2(${a}, ${i});

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

        ivec2 xRCCorner =
            ivec2(coords[${y}], coords[${b}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${m}; wR++) {
          int xR = xRCorner + wR * ${c};

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

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

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

            for (int d1 = 0; d1 < ${d}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${g}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${h===1}) {

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

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

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

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

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

            }
          }
        }

        float result = dotProd;
        ${E}
        ${k}
        setOutput(result);
      }
    `}},Ak=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${s}, ${a}, ${i});
      const ivec3 pads = ivec3(${t}, ${n}, ${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 < ${p}; wF++) {
          int xF = xFCorner + wF * ${l};

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

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

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

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

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

              for (int d1 = 0; d1 < ${d}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${h===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${d}) *
                  getW(wF, wR, wC, ${d}, d2);
              } else if (${h===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${d}),
                  getX(batch, xF, xR, xC, ${d} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${d}, d2),
                  getW(wF, wR, wC, ${d} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${h===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${d}),
                  getX(batch, xF, xR, xC, ${d} + 1),
                  getX(batch, xF, xR, xC, ${d} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${d}, d2),
                  getW(wF, wR, wC, ${d} + 1, d2),
                  getW(wF, wR, wC, ${d} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};var Dk=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:o,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=n,{left:f,top:d}=l,h=s*o,g=Lt(),y=m==="channelsLast",b=y?0:1,w=y?1:2,_="";for(let k=0;k<=1;k++)for(let E=0;E<=1;E++)_+=`
          blockIndex = rc.y + ${E};
          pos = rc.x + ${k};

          if(blockIndex < ${e[1]} && pos < ${e[0]}) {
            offsetY = int(blockIndex / (${u})) * ${i} - ${d};
            d0 = offsetY + ${p} * (pos / ${h});

            if(d0 < ${t[b]} && d0 >= 0) {

              offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
              d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));

              if(d1 < ${t[w]} && d1 >= 0) {

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

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

        ${_}

        ${g.output} = result;
      }
    `}};function nx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,y=[],b=(p===1||m===1)&&c>ck,w=l[2]%2!=0&&!!u.isPacked;if(b||!U().getBool("WEBGL_LAZILY_UNPACK")||!U().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=me({inputs:{x:r},backend:n,attrs:{shape:[1,_,t.inChannels]}}),E=me({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),N=ec({a:k,b:E,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=me({inputs:{x:N},backend:n,attrs:{shape:t.outShape}}),y.push(k),y.push(E),y.push(N)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},E=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,x.assert(xl(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let N=me({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});y.push(N);let F=ec({a:k,b:N,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),O=n.texData.get(F.dataId);x.assert(O.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=E,O.shape=t.outShape,g=Ht({inputs:{x:F},backend:n}),g.shape=t.outShape,y.push(F)}for(let _ of y)n.disposeIntermediateTensorInfo(_);return g}function ox({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,y=[h,g],b=!0,w=!1,_=[],k=me({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),E=me({inputs:{x:e},backend:n,attrs:{shape:[1,h,x.sizeFromShape(e.shape)/h]}});_.push(k),_.push(E);let N=new Dk(y,k.shape,t),F=n.runWebGLProgram(N,[k],"float32"),O=me({inputs:{x:F},backend:n,attrs:{shape:[1,y[0],y[1]]}});_.push(F),_.push(O);let P=o!=null,W=s!=null,G=i==="leakyrelu",j=i?bl(i,!0):null,X=new If(O.shape,E.shape,[1,g,t.outChannels],b,w,P,j,W,G),K=[O,E];if(o&&K.push(o),W&&K.push(s),G){let Q=n.makeTensorInfo([],"float32",x.createScalarValue(a,"float32"));K.push(Q),_.push(Q)}let Y=n.runWebGLProgram(X,K,"float32"),ne=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],J=me({inputs:{x:Y},backend:n,attrs:{shape:ne}});_.push(Y);for(let Q of _)n.disposeIntermediateTensorInfo(Q);return J}function NX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=T.convertConv2DDataFormat(l),m=T.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=nx({x:o,filter:s,convInfo:m,backend:t});else if(U().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=ox({x:o,filter:s,convInfo:m,backend:t});else{let h=new Nf(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=me({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var uR={kernelName:Zn,backendName:"webgl",kernelFunc:NX};var $k=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${o};

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

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

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

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

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

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

        ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

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

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

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

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

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

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

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

            }
          }
        }
        setOutput(dotProd);
      }
    `}},Fk=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yF = 0; yF < ${e.outDepth}; yF++) {
            int xF = wF + yF * ${t} - ${s};

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

            for (int yR = 0; yR < ${e.outHeight}; yR++) {
              int xR = wR + yR * ${n} - ${a};

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

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

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

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

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${t}; wF++) {
          float dyF = float(dyFCorner + wF) / ${s}.0;

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

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

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

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

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

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

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

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

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function TX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=T.convertConv2DDataFormat(l),m=T.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new $k(m);return t.runWebGLProgram(f,[o,s],"float32")}var cR={kernelName:Yl,backendName:"webgl",kernelFunc:TX};function EX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=T.convertConv2DDataFormat(u),m=T.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new Rk(m);return t.runWebGLProgram(f,[o,s],"float32")}var pR={kernelName:Jn,backendName:"webgl",kernelFunc:EX};function AX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=T.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new Ak(u);return t.runWebGLProgram(c,[o,s],"float32")}var mR={kernelName:aa,backendName:"webgl",kernelFunc:AX};function DX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=T.computeConv3DInfo(o.shape,l,a,1,i),c=new Fk(u);return t.runWebGLProgram(c,[o,s],"float32")}var fR={kernelName:Zl,backendName:"webgl",kernelFunc:DX};function $X(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=T.computeConv3DInfo(l,s.shape,i,1,a),c=new Ok(u);return t.runWebGLProgram(c,[o,s],"float32")}var dR={kernelName:Jl,backendName:"webgl",kernelFunc:$X};var RX=Zg+`
  return cos(x);
`,FX=Ce({opSnippet:RX}),hR={kernelName:Qn,backendName:"webgl",kernelFunc:FX};var OX=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,PX=Ce({opSnippet:OX}),gR={kernelName:ei,backendName:"webgl",kernelFunc:PX};var Pk=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,y,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,k]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
      const float height_ratio = float(${g});
      const float width_ratio = float(${w});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${a}) {
          return;
        }

        float height_scale = ${y};
        float width_scale = ${_};

        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${d} ) {
          setOutput(float(${s}));
          return;
        }
        float in_x = ${k};
        if( in_x < 0.0 || in_x > ${h} ) {
          setOutput(float(${s}));
          return;
        }

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

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

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}};var MX=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new Pk(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},xR={kernelName:ti,backendName:"webgl",kernelFunc:MX};var sx=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${yR(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${Be(o)} coords = getOutputCoords();
        int end = ${bR(o,"coords")};
        float val = ${s};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${l};
          ${bR(o,"coords")} = idx;
          val += getX(${yR(o,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function yR(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function bR(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function LX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=T.getAxesPermutation([s],l),c=o;u!=null&&(c=Vt({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=T.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=o.shape[p],f=Ht({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new sx(c.shape,!1,i),g=h.getCustomSetupFunc(d),y=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(y)}if(a){let d=new sx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=T.getUndoAxesPermutation(u),h=Vt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var wR={kernelName:eo,backendName:"webgl",kernelFunc:LX};function zX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=qg(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=ND(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var _R={kernelName:Ql,backendName:"webgl",kernelFunc:zX};var Mk=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${t};
      int offset_h = imod(h, ${t});
      int in_w = w / ${t};
      int offset_w = imod(w, ${t});
      int offset_d = (offset_h * ${t} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function BX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;x.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new Mk(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var vR={kernelName:ri,backendName:"webgl",kernelFunc:BX};var Tf=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,y="",b="";n&&(o?y=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?y=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:y=`
          float activation(float x) {
            ${n}
          }
        `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${y}

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

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

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${d}; wR++) {
          int xR = xRCorner + wR * ${m};

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

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

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

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

        float result = dotProd;
        ${w}
        ${b}
        setOutput(result);
      }
    `}};var Ef=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,y="int xR; int xC; int xCOffset;";for(let k=0;k<d;k++)for(let E=0;E<h;E++)y+=`
          vec4 xTexelR${k}C${E*2} = vec4(0.);
          vec4 wR${k}C${E} = vec4(0.);
          vec4 xR${k}C${E} = vec4(0.);`;for(let k=0;k<d;k++)for(let E=0;E<g;E++){let N=E*2;if(y+=`
          xR = xRCorner + ${k*m};
          xC = xCCorner + ${N*f};
        `,p===1){if(N<h&&(u%2==1?y+=`
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${k}C${N} = getX(batch, xR, xCOffset, d1);

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

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

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

                  xR${k}C${N} = vec4(previous.zw, xTexelR${k}C${N}.xy);
                } else {
                  xR${k}C${N} = vec4(0, 0, xTexelR${k}C${N}.xy);
                }
              `:y+=`
                if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
                  xTexelR${k}C${N} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${k}C${N} = vec4(0.);
                }

                xR${k}C${N} = xTexelR${k}C${N};
              `,N+1<h)){let F=u%2==0?x.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(y+=`
                  xCOffset = xC + ${u%2} + ${F};

                  if(xR >= 0 && xR < ${a} &&
                    xCOffset >= 0 && xCOffset < ${i}) {
                    xTexelR${k}C${N+2} = getX(batch, xR, xCOffset, d1);
                  }
                `,f>1&&(y+=`
                    xCOffset -= 2;
                    if(xR >= 0 && xR < ${a} &&
                      xCOffset >= 0 && xCOffset < ${i}) {
                      xTexelR${k}C${N} = getX(batch, xR, xCOffset, d1);
                    } else {
                      xTexelR${k}C${N} = vec4(0.);
                    }
                  `),y+=`
                  xR${k}C${N+1} = vec4(
                    xTexelR${k}C${N}.zw, xTexelR${k}C${N+2}.xy);
                `):y+=`
                  xCOffset = xC + ${F};

                  if(xR >= 0 && xR < ${a} &&
                    xCOffset >= 0 && xCOffset < ${i}) {
                    xTexelR${k}C${N+2} = getX(batch, xR, xCOffset, d1);
                  }

                  xR${k}C${N+1} = xTexelR${k}C${N+2};
                `}}else N<h&&(y+=`
              if(xR >= 0 && xR < ${a}) {
            `,u%2==1?(y+=`
                xCOffset = xC + 1 - ${p};
                if(xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${k}C${N} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${k}C${N} = vec4(0.);
                }

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

                xR${k}C${N} = vec4(
                  xTexelR${k}C${N}.zw, xTexelR${k}C${N+2}.zw);
              `,N+1<h&&(y+=`
                  vec4 final = vec4(0.);
                  xCOffset = xC + 1 + ${p};
                  if(xCOffset >= 0 && xCOffset < ${i}) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xR${k}C${N+1} = vec4(xTexelR${k}C${N+2}.xy, final.xy);
                `)):(y+=`
                if(xC >= 0 && xC < ${i}) {
                  xTexelR${k}C${N} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${k}C${N} = vec4(0.);
                }

                xCOffset = xC + ${p};
                if(xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${k}C${N+2} = getX(batch, xR, xCOffset, d1);
                } else {
                  xTexelR${k}C${N+2} = vec4(0.);
                }

                xR${k}C${N} = vec4(
                  xTexelR${k}C${N}.xy, xTexelR${k}C${N+2}.xy);
              `,N+1<h&&(y+=`
                  xR${k}C${N+1} = vec4(
                    xTexelR${k}C${N}.zw, xTexelR${k}C${N+2}.zw);
                `)),y+="}");N<h&&(y+=`
            vec4 wTexelR${k}C${N} = getW(${k}, ${N}, d1, q);
            wR${k}C${N} = vec4(wTexelR${k}C${N}.xz, wTexelR${k}C${N}.xz);
          `,N+1<h&&(y+=`
              vec4 wTexelR${k}C${N+1} = getW(${k}, ${N+1}, d1, q);
              wR${k}C${N+1} =
                vec4(wTexelR${k}C${N+1}.xz, wTexelR${k}C${N+1}.xz);`))}for(let k=0;k<d;k++)for(let E=0;E<h;E++)y+=`dotProd += xR${k}C${E} * wR${k}C${E};`;let b="",w="";n&&(o?b=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?b=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:b=`vec4 activation(vec4 x) {
          ${n}
        }`,w="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${b}

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

      void main() {

        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2;
        int q = 0;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        vec4 dotProd = vec4(0.);

        ${y}

        vec4 result = dotProd;
        ${_}
        ${w}
        setOutput(result);
      }
    `}};function VX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=n,c=l;c==null&&(c=[1,1]),x.assert(T.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=T.computeConv2DInfo(o.shape,s.shape,a,c,i,u,!0),m;return U().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Ef(p):m=new Tf(p),t.runWebGLProgram(m,[o,s],"float32")}var kR={kernelName:to,backendName:"webgl",kernelFunc:VX};var Lk=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${a} + dm;

        float dotProd = 0.0;

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

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

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

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

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

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

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

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

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

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

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${l}; dm++) {
              int d2 = d1 * ${l} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function WX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=T.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new Lk(p);return t.runWebGLProgram(m,[o,s],"float32")}var CR={kernelName:eu,backendName:"webgl",kernelFunc:WX};function jX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=T.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new zk(p);return t.runWebGLProgram(m,[o,s],"float32")}var IR={kernelName:tu,backendName:"webgl",kernelFunc:jX};var Bk=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function GX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=x.sizeFromShape(n.shape),a=me({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new Bk(s),l=t.runWebGLProgram(i,[a],a.dtype),u=me({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var SR={kernelName:ru,backendName:"webgl",kernelFunc:GX};var Vk=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=`
      const ivec2 strides = ivec2(${s}, ${a});
      const ivec2 pads = ivec2(${p}, ${m});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${i}; h++) {
          int hIn = hBeg + h * ${u};

          if (hIn >= 0 && hIn < ${t}) {
            for (int w = 0; w < ${l}; w++) {
              int wIn = wBeg + w * ${c};

              if (wIn >= 0 && wIn < ${n}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `}};function UX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=T.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new Vk(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=me({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var NR={kernelName:la,backendName:"webgl",kernelFunc:UX};var qX="return (x >= 0.0) ? x : (exp(x) - 1.0);",HX=`
  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;
`,KX=Ce({opSnippet:qX,packedOpSnippet:HX}),TR={kernelName:ni,backendName:"webgl",kernelFunc:KX};var XX="return (b >= 1.0) ? a : a * (b + 1.0);",YX=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,ZX=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=U().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Fs(YX,n.shape,o.shape):new Ko(XX,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},ER={kernelName:nu,backendName:"webgl",kernelFunc:ZX};var JX=`
  return vec4(equal(a, b));
`,QX="return float(a == b);",e7=at({opSnippet:QX,packedOpSnippet:JX,dtype:"bool"}),AR={kernelName:si,backendName:"webgl",kernelFunc:e7};var t7=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${T.ERF_P};
  float a1 = ${T.ERF_A1};
  float a2 = ${T.ERF_A2};
  float a3 = ${T.ERF_A3};
  float a4 = ${T.ERF_A4};
  float a5 = ${T.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,r7=Ce({opSnippet:t7}),DR={kernelName:oi,backendName:"webgl",kernelFunc:r7};var $R="return exp(x);",Wk=Ce({opSnippet:$R,packedOpSnippet:$R,cpuKernelImpl:AD}),RR={kernelName:no,backendName:"webgl",kernelFunc:Wk};function ix(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(x.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),me({inputs:{x:s},backend:n,attrs:{shape:i}})}var FR={kernelName:ls,backendName:"webgl",kernelFunc:ix};var OR="return exp(x) - 1.0;",n7=Ce({opSnippet:OR,packedOpSnippet:OR,cpuKernelImpl:DD}),PR={kernelName:ii,backendName:"webgl",kernelFunc:n7};var ax=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
      const float exponentMultiplier = ${s};

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

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${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) / ${a};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function lx(r,e,t){let n=t.texData.get(r.dataId),o=x.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=o/s,l=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}).shape,u=new ax("real",l,e),c=new ax("imag",l,e),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=an({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=me({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(h),h}function o7(r){let{inputs:e,backend:t}=r,{input:n}=e;return lx(n,!1,t)}var MR={kernelName:ou,backendName:"webgl",kernelFunc:o7};var jk=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      uniform float value;
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Af(r){let{backend:e,attrs:t}=r,{shape:n,value:o}=t,{dtype:s}=t;if(s=s||x.inferDtype(o),s==="string"){let a=x.getArrayFromDType(s,x.sizeFromShape(n));return a.fill(o),e.makeTensorInfo(n,s,a)}else{let a=new jk(n,o),i=a.getCustomSetupFunc(o);return e.runWebGLProgram(a,[],s,i)}}var LR={kernelName:ua,backendName:"webgl",kernelFunc:Af};var Gk=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t} - x;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var zR={kernelName:ai,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,n=e,o=new Gk(t.shape);return n.runWebGLProgram(o,[t],t.dtype)}};var BR="return floor(x);",s7=Ce({opSnippet:BR,packedOpSnippet:BR,cpuKernelImpl:$D}),VR={kernelName:oo,backendName:"webgl",kernelFunc:s7};var i7=`
  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;
  }
`,a7=`
  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);
`,l7=at({opSnippet:i7,packedOpSnippet:a7,dtype:"int32"}),WR={kernelName:so,backendName:"webgl",kernelFunc:l7};var Uk=class{constructor(e){this.variableNames=["A"];let t=Lt(),[n,o]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);

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

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}};var qk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Lt(),[n,o]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${o}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `}};var jR={kernelName:Mc,backendName:"webgl",kernelFunc:u7},vp;function u7(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&o instanceof ImageBitmap,[u,c]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],p=[c,u],m=[c,u,s];(i||a||l)&&(vp==null&&(vp=document.createElement("canvas").getContext("2d")),vp.canvas.width=u,vp.canvas.height=c,vp.drawImage(o,0,0,u,c),o=vp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=$r.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),o);let d=U().getBool("WEBGL_PACK")?new qk(m):new Uk(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function c7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=nx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(U().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)y=ox({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,k=i!=null,E=f==="leakyrelu",N=f?bl(f,!1):null,F=new Nf(g,_,N,k,E),O=[o,s];if(a&&O.push(a),i&&O.push(i),E){let P=t.makeTensorInfo([],"float32",x.createScalarValue(d,"float32"));O.push(P),b.push(P)}y=t.runWebGLProgram(F,O,"float32")}let w=me({inputs:{x:y},backend:t,attrs:{shape:g.outShape}});return b.push(y),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var GR={kernelName:_s,backendName:"webgl",kernelFunc:c7};function p7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),x.assert(T.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=T.computeConv2DInfo(o.shape,s.shape,l,h,u,p,!0),y=U().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?bl(m,y):null,w=[o,s],_=a!=null,k=i!=null,E=m==="leakyrelu";if(_&&w.push(a),k&&w.push(i),E){let O=t.makeTensorInfo([],"float32",x.createScalarValue(f,"float32"));w.push(O),d.push(O)}let N;y?N=new Ef(g,_,b,k,E):N=new Tf(g,_,b,k,E);let F=t.runWebGLProgram(N,w,"float32");return d.forEach(O=>t.disposeIntermediateTensorInfo(O)),F}var UR={kernelName:vs,backendName:"webgl",kernelFunc:p7};var Hk=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let o=Be(t.length),s=Be(n.length),a=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 * ${a};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function m7(r){let{inputs:e,backend:t}=r,{params:n,indices:o}=e,s=o.shape,a=s[s.length-1],[i,l,u,c]=T.prepareAndValidate(n,o),p=me({inputs:{x:o},backend:t,attrs:{shape:[l,a]}}),m=me({inputs:{x:n},backend:t,attrs:{shape:[x.sizeFromShape(n.shape)/u,u]}}),f=new Hk(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=me({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var qR={kernelName:li,backendName:"webgl",kernelFunc:m7};var Kk=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=Be(this.rank),o=f7(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${o}));
      }
    `}};function f7(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("int(getIndices(resRC.x, resRC.z))"):n.push(`${t[o]}`);return n.join()}function d7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,indices:s}=e,{axis:a,batchDims:i}=n,l=x.parseAxisParam(a,o.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(o,s,l,i),c=x.sizeFromShape(s.shape),p=[],m=me({inputs:{x:o},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=me({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=t.bufferSync(f),w=t.bufferSync(m),_=RD(w,b,d);return p.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new Kk(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let y=me({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),y}var HR={kernelName:us,backendName:"webgl",kernelFunc:d7};var h7="return float(a > b);",g7=`
  return vec4(greaterThan(a, b));
`,x7=at({opSnippet:h7,packedOpSnippet:g7,cpuKernelImpl:FD,dtype:"bool"}),KR={kernelName:ui,backendName:"webgl",kernelFunc:x7};var y7="return float(a >= b);",b7=`
  return vec4(greaterThanEqual(a, b));
`,w7=at({opSnippet:y7,packedOpSnippet:b7,dtype:"bool"}),XR={kernelName:ao,backendName:"webgl",kernelFunc:w7};function _7(r){let{inputs:e,backend:t}=r,{input:n}=e;return lx(n,!0,t)}var YR={kernelName:su,backendName:"webgl",kernelFunc:_7};var v7="return float(!isnan(x) && !isinf(x));",k7=Ce({opSnippet:v7,dtype:"bool"}),ZR={kernelName:ci,backendName:"webgl",kernelFunc:k7};var C7="return float(isinf(x));",I7=Ce({opSnippet:C7,dtype:"bool"}),JR={kernelName:pi,backendName:"webgl",kernelFunc:I7};var S7="return float(isnan(x));",N7=Ce({opSnippet:S7,dtype:"bool"}),QR={kernelName:mi,backendName:"webgl",kernelFunc:N7};var T7="return float(a < b);",E7=`
  return vec4(lessThan(a, b));
`,A7=at({opSnippet:T7,packedOpSnippet:E7,cpuKernelImpl:OD,dtype:"bool"}),eF={kernelName:fi,backendName:"webgl",kernelFunc:A7};var D7="return float(a <= b);",$7=`
  return vec4(lessThanEqual(a, b));
`,R7=at({opSnippet:D7,packedOpSnippet:$7,dtype:"bool"}),tF={kernelName:di,backendName:"webgl",kernelFunc:R7};function F7(r){let{backend:e,attrs:t}=r,{start:n,stop:o,num:s}=t,a=PD(n,o,s);return e.makeTensorInfo([a.length],"float32",a)}var rF={kernelName:au,backendName:"webgl",kernelFunc:F7};var O7=`if (x < 0.0) return NAN;
  return log(x);`,P7=`
  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;
`,M7=Ce({opSnippet:O7,packedOpSnippet:P7,cpuKernelImpl:MD}),nF={kernelName:uo,backendName:"webgl",kernelFunc:M7};var L7="return log(1.0 + x);",z7=Ce({opSnippet:L7}),oF={kernelName:hi,backendName:"webgl",kernelFunc:z7};var B7="return float(a >= 1.0 && b >= 1.0);",V7=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,W7=at({opSnippet:B7,packedOpSnippet:V7,dtype:"bool"}),sF={kernelName:gi,backendName:"webgl",kernelFunc:W7};var j7="return float(!(x >= 1.0));",G7=Ce({opSnippet:j7}),iF={kernelName:tl,backendName:"webgl",kernelFunc:G7};var U7="return float(a >= 1.0 || b >= 1.0);",q7=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,H7=at({opSnippet:U7,packedOpSnippet:q7,dtype:"bool"}),aF={kernelName:rl,backendName:"webgl",kernelFunc:H7};var Xk=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${i}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${l};
        setOutput(val);
      }
    `}};var Yk=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

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

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

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

        int firstChannel = d - ${a};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${a}; j <= ${a}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${l};
        setOutput(result);
      }
    `}};var K7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=U().getBool("WEBGL_PACK_NORMALIZATION")?new Yk(o.shape,s,a,i,l):new Xk(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},lF={kernelName:ca,backendName:"webgl",kernelFunc:K7};var Zk=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,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 - ${t})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${o}) * norm + float(${n});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${o})
                * float(${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);
      }
    `}};var X7=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new Zk(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},uF={kernelName:lu,backendName:"webgl",kernelFunc:X7};function cF(r,e,t,n){let o=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/o,i=me({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=In(i,r.dtype,"max",n),u=me({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function Jk(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let N=0;N<_.length;N++)_[N]=o.shape[c[N]];let k=_p(w,o.shape,o.dtype,c,_);f=t.makeTensorInfo(_,o.dtype);let E=t.texData.get(f.dataId);E.values=k}else f=wl(o,c,t);u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("max",u,i);let[d,h]=T.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=T.expandShapeToKeepDim(d,l));let y;if(m){let w=t.texData.get(f.dataId).values,_=LD(w,x.sizeFromShape(h),g,o.dtype);y=t.makeTensorInfo(g,o.dtype);let k=t.texData.get(y.dataId);k.values=_}else y=cF(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),y}var pF={kernelName:co,backendName:"webgl",kernelFunc:Jk};var Y7=Yg+`
  return max(a, b);
`,Z7=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+yl+`
  return result;
`,J7=at({opSnippet:Y7,packedOpSnippet:Z7,cpuKernelImpl:zD}),mF={kernelName:po,backendName:"webgl",kernelFunc:J7};function Q7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;Ds(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;x.assert(T.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=T.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:o},backend:t});let p=new ji(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var fF={kernelName:mo,backendName:"webgl",kernelFunc:Q7};function eY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=T.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new tc(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var dF={kernelName:pa,backendName:"webgl",kernelFunc:eY};var Qk=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${l});

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

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${s};
          wR += ${o}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

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

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

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

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

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

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},e0=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${m}, ${f});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${l};
           wD += ${s}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

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

          for (int wR = 0; wR < ${u};
              wR += ${a}) {
            float dyR = float(dyRCorner + wR) / ${n}.0;

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

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

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function tY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=T.computePool3DInfo(a.shape,i,l,p,u,c),f=new tc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new e0(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var hF={kernelName:cu,backendName:"webgl",kernelFunc:tY};function rY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;Ds([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=T.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new ji(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new Qk(m),y=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),y}var gF={kernelName:uu,backendName:"webgl",kernelFunc:rY};function xF(r,e,t,n){let o=new ji(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new ji(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var yF={kernelName:pu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;x.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];x.assert(T.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=T.computePool2DInfo(n.shape,o,s,u,a),[p,m]=xF(n,i,c,l);return[p,m]}};function bF(r,e,t,n){let o=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/o,i=me({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=In(i,"float32","mean",n),u=me({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var wF={kernelName:fo,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let _=a.texData.get(d.dataId).values,k=new Array(i);for(let F=0;F<k.length;F++)k[F]=n.shape[c[F]];let E=_p(_,n.shape,n.dtype,c,k);d=a.makeTensorInfo(k,n.dtype);let N=a.texData.get(d.dataId);N.values=E}else d=wl(n,c,a);f.push(d),u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=T.computeOutAndReduceShapes(d.shape,u),y=h;o&&(y=T.expandShapeToKeepDim(h,l));let b=bF(d,g,y,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return b}};function nY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=T.getAxesPermutation(u,i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,o.shape.length)),T.assertAxesAreInnerMostDims("min",u,i);let[m,f]=T.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=In(h,h.dtype,"min",t),y;if(a){let b=T.expandShapeToKeepDim(m,l);y=me({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=me({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var _F={kernelName:ho,backendName:"webgl",kernelFunc:nY};var oY=Yg+`
  return min(a, b);
`,sY=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+yl+`
  return result;
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        int end = ${i};

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

      void main() {
        ${s} outC = getOutputCoords();
        for (int i = 0; i < ${o}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${u};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
          }
        }
        ${s} coords = outC - start;
        setOutput(getX(${l}));
      }
    `}};var r0=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=Be(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=qt("rc",o),u=qt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
        ${s} source = rc;
        if (source < start) {
          source = start * 2 - source - ${m};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${m};
        }
        source -= start;
      `;f=`
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${u.join()}), ${p});
        ${l[o-1]} += 1;
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          ${d}
          result[1] = getChannel(getX(${u.join()}), ${p});
        }
      `}else{let d=`
        ${s} source = rc;
        ${s} lt = ${s}(lessThan(source, start));
        ${s} gte = ${s}(greaterThanEqual(source, end));
        ${s} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${m}) +
                gte * ((end - 1) * 2 - source + ${m});
        source -= start;
      `;f=`
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${u.join()}), ${p});
        ${l[o-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${u.join()}), ${p});
        }
        rc = outputLoc;
        ${l[o-2]} += 1;
        if(${l[o-2]} < ${this.outputShape[o-2]}) {
          ${d}
          result[2] = getChannel(getX(${u.join()}), ${p});
          ${l[o-1]} += 1;
          if(${c}) {
            ${d}
            result[3] = getChannel(getX(${u.join()}), ${p});
          }
        }
      `}this.userCode=`
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${i});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${f}
        setOutput(result);
      }
    `}};var aY=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new r0(n.shape,o,s):new t0(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},kF={kernelName:ma,backendName:"webgl",kernelFunc:aY};var lY=`if (b == 0.0) return NAN;
  return mod(a, b);`,uY=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+yl+`
  return result;
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      uniform float seed;

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${t-1}; i++) {
          cdf += getProbs(batch, i);

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

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var pY=`
if (a == b) {
  return 1.0;
};
return a / b;`,mY=`
  // 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;
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      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${o}), float(${n}),
                      float(index == coords.y)));
      }
    `}};var _Y=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=x.sizeFromShape(o.shape),u=new a0(l,s,a,i),c=me({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=me({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},OF={kernelName:yo,backendName:"webgl",kernelFunc:_Y};function Df(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=ja({inputs:{input:n},backend:t}),s=Df({inputs:{x:o},backend:t}),a=rc({inputs:{input:n},backend:t}),i=Df({inputs:{x:a},backend:t}),l=an({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Af({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var PF={kernelName:bs,backendName:"webgl",kernelFunc:Df};function MF(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=ja({inputs:{input:n},backend:t}),s=MF({inputs:{x:o},backend:t}),a=rc({inputs:{input:n},backend:t}),i=Df({inputs:{x:a},backend:t}),l=an({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Af({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var LF={kernelName:ms,backendName:"webgl",kernelFunc:MF};function vY(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return ix({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=ix({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=Ek({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var zF={kernelName:fs,backendName:"webgl",kernelFunc:vY};var l0=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=Be(o),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
        int start = ${a};
        int end = ${i};

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(float(${n}));
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${l}));
        }
      }
    `}};var u0=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=Be(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=qt("rc",o),u=qt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1;
       if(${c}) {
      `,o===1?"":`}
       rc = outputLoc;
       ${l[o-2]} += 1;
       if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${l[o-1]} += 1;
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        ${m[h]}
        if (${f}) {
          result[${h}] = float(${n});
        } else {
          ${s} source = rc - start;
          result[${h}] = getChannel(getX(${u.join()}), ${p});
        }
      `;d+=o===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${i});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}};var c0=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n,i=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new u0(o.shape,s,a):new l0(o.shape,s,a);return t.runWebGLProgram(i,[o],o.dtype)},BF={kernelName:bo,backendName:"webgl",kernelFunc:c0};var kY=`
  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);
`,CY=`
  // 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));
  `+yl+`
  return result;
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  return (x < 0.0) ? 0.0 : x;
`,AY=`
  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;
`,DY=Ce({opSnippet:EY,packedOpSnippet:AY}),UF={kernelName:vo,backendName:"webgl",kernelFunc:DY};var $Y=gr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,RY=`
  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;
`,FY=Ce({opSnippet:$Y,packedOpSnippet:RY}),qF={kernelName:Co,backendName:"webgl",kernelFunc:FY};var m0=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/p[0]},
          ${c[1]/p[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);

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

        // Fractional source index.
        vec2 sourceFracIndexRC = ${m};

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

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

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

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

        setOutput(newValue);
      }
    `}};var f0=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/p[0]},
          ${c[1]/p[1]},
          ${c[1]/p[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
                                     ${l}.0);

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

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

        // Fractional source index.
        vec3 sourceFracIndexRC = ${m};

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

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

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

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

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

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

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

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

        setOutput(newValue);
      }
    `}};function OY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=U().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new f0(o.shape,l,u,s,a):new m0(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var HF={kernelName:ko,backendName:"webgl",kernelFunc:OY};var d0=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${c});
        const float widthScale = float(${p});

        const float invHeightScale = float(${m});
        const float invWidthScale = float(${f});

        const int winHeight = int(${d});
        const int winWidth = int(${h});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${a}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${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);
      }
    `}};function PY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new d0(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var KF={kernelName:hu,backendName:"webgl",kernelFunc:PY};var h0=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/p[0]},
          ${c[1]/p[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);

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

        // Fractional source index.
        vec2 sourceFracIndexRC = ${f};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}};function MY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=new h0(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var XF={kernelName:da,backendName:"webgl",kernelFunc:MY};var g0=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${c});
        const float widthScale = float(${p});

        const float invHeightScale = float(${m});
        const float invWidthScale = float(${f});

        const int winHeight = int(${d});
        const int winWidth = int(${h});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${a}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

            float sourceFracRow =
              float(${l[0]}) *
                (float(dyR) / float(${u[0]}));

            float sourceFracCol =
                float(${l[1]}) *
                  (float(dyC) / float(${u[1]}));

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

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

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

        setOutput(accumulator);
      }
    `}};function LY(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new g0(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var YF={kernelName:du,backendName:"webgl",kernelFunc:LY};var x0=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let o=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>o(l)).join(","),a=Be(n);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}};var y0=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let o=qt("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,i=Be(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${l(o.slice())};
          if(${s}){
            result.g = ${u(o.slice())};
          }
          if(${a}) {
            result.b = ${c(o.slice())};
            if(${s}) {
              result.a = ${p(o.slice())};
            }
          }
          setOutput(result);
        }
    `;function l(d){return m(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=e.map((b,w)=>f(w,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function zY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=o.shape.length,i=x.parseAxisParam(s,o.shape);if(a===0)return Ht({inputs:{x:o},backend:t});let l=U().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new y0(o.shape,i):new x0(o.shape,i);return t.runWebGLProgram(l,[o],o.dtype)}var ZF={kernelName:Io,backendName:"webgl",kernelFunc:zY};var b0=class{constructor(e,t,n,o){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=T.getImageCenter(o,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof n=="number"?f=`float outputValue = ${n.toFixed(2)};`:f=`
        vec3 fill = vec3(${n.join(",")});
        float outputValue = fill[coords[3]];`,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - ${p}) * ${l} - (float(y) - ${m}) * ${i};
          float coordYFloat = (float(x) - ${p}) * ${i} + (float(y) - ${m}) * ${l};
          int coordX = int(round(coordXFloat + ${p}));
          int coordY = int(round(coordYFloat + ${m}));
          ${f}
          if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${s}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var JF={kernelName:Ri,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:n}=r,{radians:o,fillValue:s,center:a}=e,i=t,l=new b0(n.shape,o,s,a);return i.runWebGLProgram(l,[n],n.dtype)}};var BY=`
  // 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;
    }
  }
`,VY=Ce({opSnippet:BY}),QF={kernelName:So,backendName:"webgl",kernelFunc:VY};var WY="return inversesqrt(x);",jY=Ce({opSnippet:WY,cpuKernelImpl:UD}),eO={kernelName:No,backendName:"webgl",kernelFunc:jY};var $f=class{constructor(e,t,n,o,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Be(s.length),u=Be(a.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
        ${l} strides = ${l}(${s});

        void main() {
          ${u} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${p});
              flattenedIndex += index * ${d};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${f};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function GY(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o,updates:s}=e,{shape:a}=n,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(s,o,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,o.dtype);let f=me({inputs:{x:o},backend:t,attrs:{shape:[l,i]}}),d=me({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new $f(l,i,f.shape.length,d.shape.length,c,m),y=t.runWebGLProgram(g,[d,f,h],d.dtype),b=me({inputs:{x:y},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(h),b}var tO={kernelName:Ci,backendName:"webgl",kernelFunc:GY};var w0=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);o=l.join(),s=u.join()}let a=Be(n);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${o});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function UY(r){let{inputs:e,backend:t}=r,{condition:n,t:o,e:s}=e,a=new w0(n.shape.length,o.shape,o.shape.length);return t.runWebGLProgram(a,[n,o,s],dr(o.dtype,s.dtype))}var rO={kernelName:hs,backendName:"webgl",kernelFunc:UY};var qY=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${T.SELU_SCALEALPHA};
  float scale = ${T.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,HY=Ce({opSnippet:qY}),nO={kernelName:Ii,backendName:"webgl",kernelFunc:HY};var KY="return 1.0 / (1.0 + exp(-1.0 * x));",XY=Ce({opSnippet:KY}),oO={kernelName:Eo,backendName:"webgl",kernelFunc:XY};var YY=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,ZY=Ce({opSnippet:YY}),sO={kernelName:Ni,backendName:"webgl",kernelFunc:ZY};var JY=Zg+`
  return sin(x);
`,QY=Ce({opSnippet:JY}),iO={kernelName:To,backendName:"webgl",kernelFunc:QY};var e9=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,t9=Ce({opSnippet:e9}),aO={kernelName:Si,backendName:"webgl",kernelFunc:t9};var r9=`
  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;
`,n9=Ce({opSnippet:r9}),lO={kernelName:Ti,backendName:"webgl",kernelFunc:n9};var o9=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n;x.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...a);for(let y=1+s.length;y<o.shape.length;++y)l.push([0,0]);let u=[],c=c0({inputs:{x:o},backend:t,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(c.shape,s,i,!1),m=T.getPermuted(p.length,s.length,!1),f=T.getReshapedPermuted(c.shape,s,i,!1),d=me({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Vt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=me({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(y=>t.disposeIntermediateTensorInfo(y)),g},uO={kernelName:ha,backendName:"webgl",kernelFunc:o9};function s9(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=T.calculateShapes(s,o,i),m=!1,f=new $f(u,l,o.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,o,a],s.dtype),h=me({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var cO={kernelName:gu,backendName:"webgl",kernelFunc:s9};function i9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=x.parseAxisParam(a,o.shape)[0],l=T.prepareSplitSize(o,s,i),u=o.shape.length,c=new Array(u).fill(0),p=o.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Wa({inputs:{x:o},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var pO={kernelName:xs,backendName:"webgl",kernelFunc:i9};var a9="return sqrt(x);",l9=Ce({opSnippet:a9}),mO={kernelName:Ao,backendName:"webgl",kernelFunc:l9};var u9="return x * x;",c9=Ce({opSnippet:u9}),fO={kernelName:ga,backendName:"webgl",kernelFunc:c9};var dO="return (a - b) * (a - b);",p9=at({opSnippet:dO,packedOpSnippet:dO}),hO={kernelName:Ro,backendName:"webgl",kernelFunc:p9};function m9({inputs:r,attrs:e,backend:t}){let{x:n}=r,o=gr+`
    return x > 0.0 ? 1.0 : float(${e.alpha});
  `,s=new sn(n.shape,o);return t.runWebGLProgram(s,[n],n.dtype)}var gO={kernelName:$i,backendName:"webgl",kernelFunc:m9};var _0=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=Be(n.length),a=Be(n.length),i="";if(o===1)i="coords * strides + begin";else{let l=0;i=n.map((u,c)=>(l++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${e});
      ${s} strides = ${s}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `}};function f9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:y,outShape:b}=ir.sliceInfo(o.shape,s,a,i,l,u,c,p,m),w=me({inputs:{x:o},backend:t,attrs:{shape:y}}),_;if(f){let E=Wa({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=me({inputs:{x:E},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(E)}else if(b.some(E=>E===0))_=t.makeTensorInfo(b,o.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let F=t.texData.get(w.dataId).values,O=Ie(w.shape,w.dtype,F),P=HD(b,O,h,d);_=t.makeTensorInfo(b,w.dtype,P.values)}else{let N=new _0(d,h,b);_=t.runWebGLProgram(N,[w],w.dtype)}let k=me({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),k}var xO={kernelName:Ei,backendName:"webgl",kernelFunc:f9};var d9="return tan(x);",h9=Ce({opSnippet:d9}),yO={kernelName:Ai,backendName:"webgl",kernelFunc:h9};var g9=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,x9=Ce({opSnippet:g9}),bO={kernelName:Oo,backendName:"webgl",kernelFunc:x9};var v0=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let o=Be(this.rank),s=y9(e);this.userCode=`
      void main() {
        ${o} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function y9(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${t[o]}, ${r[o]})`);return n.join()}function k0(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;if(o.dtype==="string"){let u=t.readSync(o.dataId).map(m=>x.decodeString(m)),c=Ie(o.shape,o.dtype,u),p=XD(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new v0(o.shape,s);return t.runWebGLProgram(a,[o],o.dtype)}var wO={kernelName:wn,backendName:"webgl",kernelFunc:k0};function b9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:s,sorted:a}=n,i=t.readSync(o.dataId),[l,u]=YD(i,o.shape,o.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var _O={kernelName:Di,backendName:"webgl",kernelFunc:b9};function w9(r){let{inputs:e,attrs:t,backend:n}=r,{axis:o}=t,{x:s}=e;Ds(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=n.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=ZD(a,o,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,i),n.makeTensorInfo([u.length],"int32",u)]}var vO={kernelName:xu,backendName:"webgl",kernelFunc:w9};function _9(r){let{inputs:e,backend:t,attrs:n}=r,{value:o}=e,{axis:s}=n;s<0&&(s+=o.shape.length);let a=o,i=a.shape.length,l=o.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Wa({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),y=me({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=y,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var kO={kernelName:ys,backendName:"webgl",kernelFunc:_9};var C0=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,o=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/n);this.outputShape=[o,i];let l="0.0",u="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
        sumValue += dot(values, segFilter);
    `,f="";s%n>0&&(f=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);let d="";s%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${l};

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

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

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

        float sumValue = 0.0;

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

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${m}
        }

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

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

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

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

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

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${m}
        }
        setOutput(${u});
      }
    `}};function v9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,segmentIds:s}=e,{numSegments:a}=n,i=o.shape.length,l=[],u=0,c=T.getAxesPermutation([u],i),p=o;c!=null&&(p=Vt({inputs:{x:o},backend:t,attrs:{perm:c}}),l.push(p),u=T.getInnerMostAxes(1,i)[0]);let m=T.segment_util.computeOutShape(p.shape,u,a),f=x.sizeFromShape([p.shape[u]]),d=me({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=yu(o.dtype),g=(_,k,E,N,F)=>{let O=_.shape[0],P=_.shape[1],W=T.segment_util.segOpComputeOptimalWindowSize(P,F),G={windowSize:W,inSize:P,batchSize:O,numSegments:F},j=new C0(G,k),X=t.compileAndRun(j,[_,E],N);if(l.push(X),X.shape[1]===F)return X;let K=p0({backend:t,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),Y=k0({inputs:{x:K},backend:t,attrs:{reps:[P/W]}});return l.push(K),l.push(Y),g(X,k,Y,N,F)},y=g(d,"unsortedSegmentSum",s,h,a),b=me({inputs:{x:y},backend:t,attrs:{shape:m}}),w=b;if(c!=null){l.push(b);let _=T.getUndoAxesPermutation(c);w=Vt({inputs:{x:w},backend:t,attrs:{perm:_}})}return l.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var CO={kernelName:xa,backendName:"webgl",kernelFunc:v9};var k9=[lF,uF,k$,I$,S$,N$,E$,A$,D$,$$,O$,P$,M$,L$,B$,z$,V$,j$,W$,G$,U$,q$,H$,X$,Y$,eR,rR,nR,sR,p$,lR,cR,pR,uR,fR,dR,mR,hR,gR,xR,wR,_R,vR,CR,IR,kR,SR,NR,TR,ER,AR,DR,RR,FR,PR,MR,LR,zR,VR,WR,jR,GR,UR,qR,HR,KR,XR,c$,YR,iR,ZR,JR,QR,m$,eF,tF,rF,oF,nF,sF,iF,aF,pF,dF,fF,hF,gF,yF,mF,wF,_F,vF,kF,CF,EF,x$,DF,$F,RF,FF,Z$,OF,LF,zF,BF,VF,f$,WF,jF,J$,IF,GF,qF,UF,b$,HF,KF,XF,YF,ZF,JF,QF,eO,tO,rO,nO,oO,sO,iO,aO,K$,TF,lO,uO,cO,pO,mO,fO,hO,gO,xO,NF,_$,yO,bO,wO,_O,v$,vO,kO,CO,PF];for(let r of k9)nl(r);var Wt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(Wt||(Wt={}));var _l;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu"})(_l||(_l={}));var IO;function C9(r){IO=r.wasm.cwrap(ws,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function I9(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let F=t.dataIdMap.get(a.dataId);if(F.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${F.shape.length}.`);d=F.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=_l[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?o.shape[2]:o.shape[1],b=u?s.shape[1]:s.shape[2],w=o.shape[0],_=t.makeOutput([w,y,b],o.dtype),k=t.dataIdMap.get(_.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return IO(m,E,o.shape.length,f,N,s.shape.length,l,u,g,d,h,p||0,k),_}var SO={kernelName:ws,backendName:"wasm",setupFunc:C9,kernelFunc:I9};function Ft(r){let e;function t(o){e=o.wasm.cwrap(r,null,["number","number"])}function n(o){let{backend:s,inputs:{x:a}}=o,i=s.dataIdMap.get(a.dataId).id,l=s.makeOutput(a.shape,a.dtype),u=s.dataIdMap.get(l.dataId).id;return x.sizeFromShape(l.shape)===0||e(i,u),l}return{kernelName:r,backendName:"wasm",setupFunc:t,kernelFunc:n}}var NO=Ft(is);function vt(r,e,t){let n;function o(a){n=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:l}=a,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,f=t!=null?t:u.dtype,d=T.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(d,f);if(x.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id,w=()=>n(p,g,u.shape.length,m,y,c.shape.length,Wt[u.dtype],b);if(e&&u.dtype==="float32")return w(),h;let _=T.getBroadcastDims(u.shape,d),k=T.getBroadcastDims(c.shape,d),E=_.every((F,O)=>F===O),N=k.every((F,O)=>F===O);if(E&&N)return w(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var S9=!0,TO=vt(bn,S9);var EO;function N9(r){EO=r.wasm.cwrap(Hn,null,["array","number","number","number"])}function T9(r){let{inputs:e,backend:t}=r,n=t.makeOutput(e[0].shape,e[0].dtype);if(x.sizeFromShape(n.shape)===0)return n;let o=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),a=t.dataIdMap.get(n.dataId).id;return EO(s,o.length,Wt[n.dtype],a),n}var AO={kernelName:Hn,backendName:"wasm",setupFunc:N9,kernelFunc:T9};function oc(r){let{inputs:{x:e},backend:t}=r,n=t.makeOutput(e.shape,e.dtype),o=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(n).set(o),n}var DO={kernelName:cs,backendName:"wasm",kernelFunc:oc};var $O;function E9(r){$O=r.wasm.cwrap(Po,null,["number","array","number","number","number","array","number"])}function kp(r){let{inputs:e,backend:t,attrs:n}=r,[o,s]=D9(e.x.shape,n.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=A9(e.x.shape,n.perm),l={dataId:e.x.dataId,shape:o,dtype:e.x.dtype};if(a){let d=oc({inputs:e,backend:t});return d.shape=i,d}let u=t.makeOutput(i,l.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(l.shape).buffer);return $O(c,f,l.shape.length,Wt[l.dtype],p,m,s.length),u}function A9(r,e){let t=new Array(r.length);for(let n=0;n<t.length;n++)t[n]=r[e[n]];return t}function D9(r,e){let t=[],n=[];for(let o=0;o<r.length;++o)r[o]!==1&&t.push(r[o]),r[e[o]]!==1&&n.push(e[o]);for(let o=0;o<n.length;++o){let s=-1;for(let a=0;a<n.length;++a)n[a]>=o&&(s===-1||n[s]>n[a])&&(s=a);n[s]=o}return[t,n]}var RO={kernelName:Po,backendName:"wasm",kernelFunc:kp,setupFunc:E9};function Xo(r,e,t){let n=r.shape,o=r.shape.length,s=x.parseAxisParam(e,n),a=s,i=T.getAxesPermutation(a,o),l=null,u=!1;if(i!=null){let c=new Array(o);for(let f=0;f<c.length;f++)c[f]=n[i[f]];a=T.getInnerMostAxes(a.length,o),l=kp({inputs:{x:r},attrs:{perm:i},backend:t});let p=t.dataIdMap.get(r.dataId).id;t.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:a,inputWasTransposed:u}}var FO;function $9(r){FO=r.wasm.cwrap(Kn,null,["number","number","number","number","number"])}function R9(r){let{backend:e,inputs:t,attrs:n}=r,{axis:o}=n,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=a,l=s,{transposed:u,axes:c,inputWasTransposed:p}=Xo(s,o,e);if(p){let y=e.dataIdMap.get(u.dataId).id;y!==a&&(l=u,i=y)}let m=l.shape.slice(0,-1),f=e.makeOutput(m,"int32"),d=e.dataIdMap.get(f.dataId).id,h=x.sizeFromShape(f.shape),g=l.shape[c[0]];return FO(i,Wt[l.dtype],h,g,d),p&&e.disposeData(u.dataId),f}var OO={kernelName:Kn,backendName:"wasm",kernelFunc:R9,setupFunc:$9};var PO;function F9(r){PO=r.wasm.cwrap(Xn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function O9(r){let{inputs:e,attrs:t,backend:n}=r,o=e.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=T.computePool2DInfo(o.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,y=c.strideHeight,b=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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Object.keys(this.name2Id)}getValue(e){if(e instanceof jr){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof jr){if(this.id2Value[e.id]==null)throw new B(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new B(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&De(this.id2Mask)}},Z0={},RL={};function mc(r,e,t,n){let o=t==null?!1:t.training,s=Array.isArray(r),a=s?r:[r],i=a.map(d=>d.name),l=[],u=e.names();for(let d of i)u.indexOf(d)!==-1?l.push(e.getValue(d)):l.push(null);n!=null&&(n.maxNumTensors=-Infinity,n.minNumTensors=Infinity);let c=i.join(",")+"|"+e.names().join(","),p,m;if(Z0[c]==null){let d=FQ(a,e);p=d.sorted,m=d.recipientCounts,Z0[c]=p,RL[c]=m}p=Z0[c],m={},o||Object.assign(m,RL[c]);let f=new Ms(e);for(let d=0;d<p.length;++d){if(n!=null){let O=Hc().numTensors;O>n.maxNumTensors&&(n.maxNumTensors=O),O<n.minNumTensors&&(n.minNumTensors=O)}let h=p[d],g=h.sourceLayer;if(g instanceof qi)continue;let y=[],b=[],w=[],_=!1;for(let O of h.inputs){let P=f.getValue(O),W=f.getMask(O);y.push(P),b.push(W),W!=null&&(_=!0),o||(m[O.name]--,m[O.name]===0&&!e.hasKey(O)&&i.indexOf(O.name)===-1&&!P.isDisposed&&O.sourceLayer.stateful!==!0&&w.push(P))}_&&(t=t||{},t.mask=b[0]);let k=kt(g.apply(y,t)),E=null;g.supportsMasking&&(E=g.computeMask(y,b));let N=OQ(h),F=Array.isArray(N)?N:[N];for(let O=0;O<F.length;++O){f.hasKey(F[O])||f.add(F[O],k[O],Array.isArray(E)?E[0]:E);let P=i.indexOf(F[O].name);P!==-1&&(l[P]=k[O])}o||De(w)}return f.disposeMasks(),s?l:l[0]}function FQ(r,e){x.assert(r!=null&&r.length>0,()=>"Expected at least one fetch, got none");let t=[],n={};if(r.length===1){let o=FL(r[0],e);t=o.sorted,n=o.recipientMap}else{let o=new Set;for(let s of r){let{sorted:a,recipientMap:i}=FL(s,e);for(let l of a)o.has(l.name)||(t.push(l),o.add(l.name));for(let l in i)n[l]==null&&(n[l]=new Set),i[l].forEach(u=>n[l].add(u))}}return{sorted:t,recipientCounts:PQ(n)}}function PQ(r){let e={};for(let t in r)e[t]=r[t].size;return e}function FL(r,e){let t=new Set,n=[],o={};for(let i of e.names())t.add(i);let s=[],a=[];for(s.push(r);s.length>0;){let i=s[s.length-1];if(t.has(i.name)){s.pop();continue}let l=a[a.length-1]===s.length-1;if(i.inputs.length===0||l)s.pop(),n.push(i),t.add(i.name),l&&a.pop();else{a.push(s.length-1);for(let u of i.inputs)o[u.name]==null&&(o[u.name]=new Set),o[u.name].add(i.name),!t.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:o}}function OQ(r){let e;if(r.sourceLayer.inboundNodes.length===1)e=r.sourceLayer.output;else{let t=null;for(let n=0;n<r.sourceLayer.inboundNodes.length;++n)for(let o of r.sourceLayer.inboundNodes[n].outputTensors)if(o.id===r.id){t=n;break}e=r.sourceLayer.getOutputAt(t)}return e}var Gn=class extends Le{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=kl(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Qo(this.inputs).length!==this.inputs.length)throw new B(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);Qo(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;this.outputLayers.push(w),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(k)}for(let b of this.inputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;jn(_===0,"input layer has >1 nodes"),jn(k===0,"input layer has >1 tensors"),this.inputLayers.push(w),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let w=this.inputLayers[b];if(!(w instanceof qi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${w.getClassName()}.`);this.inputNames.push(w.name),this.feedInputShapes.push(w.batchInputShape),this.feedInputNames.push(w.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},o={},s={},a={},i=[],l=(b,w,_,k,E,N)=>{(k==null||E==null||N==null)&&(k=b.sourceLayer,E=b.nodeIndex,N=b.tensorIndex);let F=k.inboundNodes[E];if(_.indexOf(F)!==-1)throw new Br(`The tensor ${b.name} at layer "${k.name}" is part of a cycle.`);if(w.indexOf(F)!==-1)return;this.containerNodes.add(Gn.nodeKey(k,E)),k.id in a||(a[k.id]=Object.keys(a).length),_.indexOf(F)===-1&&_.push(F);let O=F.inboundLayers.length;for(let P=0;P<O;P++){let W=F.inputTensors[P],G=F.inboundLayers[P],j=F.nodeIndices[P],X=F.tensorIndices[P];l(W,w,_,G,j,X)}for(w.push(F);_.indexOf(F)>=0;)_.splice(_.indexOf(F),1);i.push(F)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){n[b.id]=b,b.id in t||(t[b.id]=0);let w=t[b.id],_=o[b.outboundLayer.id]==null?0:o[b.outboundLayer.id];w=Math.max(w,_),o[b.outboundLayer.id]=w,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=w;for(let k=0;k<b.inboundLayers.length;k++){let E=b.inboundLayers[k],N=b.nodeIndices[k],F=E.inboundNodes[N],O=t[F.id]==null?0:t[F.id];t[F.id]=Math.max(w+1,O),n[F.id]=F}}let m={};for(let b in t){let w=t[b];w in m||(m[w]=[]),m[w].push(n[b])}let f={};for(let b in o){let w=o[b];w in f||(f[w]=[]),f[w].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(Mf);this.layers=[];for(let b of d){let w=f[b];w.sort((_,k)=>{let E=a[_.id],N=a[k.id];return E<N?-1:E>N?1:0});for(let _ of w)_ instanceof Gn&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(Mf);let h=this.inputs.slice(),g=[];for(let b of d)for(let w of m[b]){let _=w.outboundLayer;if(_!=null){for(let k of w.inputTensors)if(h.indexOf(k)===-1)throw new Br(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${_.name}". The following previous layers were accessed without issue: ${g}`);for(let k of w.outputTensors)h.push(k);g.push(_.name)}}this.nodesByDepth=m;let y=this.layers.map(b=>b.name);for(let b of y){let w=y.filter(_=>_===b).length;if(w!==1)throw new Br(`The name "${b}" is used ${w} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(y))}this.outboundNodes=[],this.inboundNodes=[],new Cl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new B("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},o=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,o++}let s=[];for(let a in e){let i=a;if(n[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(n[i]!=null)s.push([n[i],e[a]]);else if(t)throw new B(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new B(`${a.length} of ${o} weights are not set: ${a}`)}zp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${jp}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Px(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return V(()=>{e=kt(e);let n=new Ms;for(let o=0;o<this.inputs.length;++o)n.add(this.inputs[o],e[o]);return mc(this.outputs,n,t)})}computeMask(e,t){return V(()=>{e=kt(e);let n;return t==null?n=Yo(null,e.length):n=kt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Mp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";n[c]=u}let o=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Mf);if(o.length>1)for(let i of o){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],y=u.nodeIndices[h],b=u.tensorIndices[h],w=`${g.name}_${y}_${b}`,_=n[w];p.push(_)}let m=c.computeOutputShape(xr(p)),f=Mp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;n[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];jn(l in n),s.push(n[l])}return xr(s)}runInternalGraph(e,t){t==null&&(t=Yo(null,e.length));let n={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];n[u.id]=[c,p]}let o=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(Mf);for(let l of o){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in n&&d.push(n[h.id]);if(d.length===m.length){let h={},g,y,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[_,k]=d[0];h.mask==null&&(h.mask=k),b=kt(p.call(_,h)),w=kt(p.computeMask(_,k)),g=[_],y=[k]}else g=d.map(_=>_[0]),y=d.map(_=>_[1]),h.mask==null&&(h.mask=y),b=kt(p.call(g,h)),w=kt(p.computeMask(g,y));if(p.activityRegularizer)throw new Te("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<f.length;++_){let k=f[_],E=b[_],N=w[_];n[k.id]=[E,N]}}}}let s=[],a=[],i=[];for(let l of this.outputs){jn(l.id in n,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=n[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},n;for(let o of this.layers){n=o instanceof Gn?1:0;for(let s=0;s<o.inboundNodes.length;s++){let a=Gn.nodeKey(o,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new B(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new B("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new B(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let o=Gn.nodeKey(t,n);this.containerNodes.has(o)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Gn.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let y=m.inboundLayers[g],b=m.nodeIndices[g],w=m.tensorIndices[g],_=Gn.nodeKey(y,b),k=t[_];k==null&&(k=0),h.push([y.name,k,w,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,n.push(c)}e.layers=n;let o=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=Gn.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];o.push([i.name,c,p])}e.inputLayers=o;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=Gn.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,n={},o=!1){let s={},a={};function i(g,y){g.name in a?a[g.name].push(y):a[g.name]=[y]}function l(g,y){let b=[],w;for(let _ of y){let k=_[0],E=_[1],N=_[2];if(w=_[3]==null?{}:_[3],!(k in s)){i(g,y);return}let F=s[k];if(F.inboundNodes.length<=E){i(g,y);return}let O=F.inboundNodes[E];b.push(O.outputTensors[N])}b.length>0&&g.apply(xr(b),w)}function u(g){let y=g.name,b=en(g,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(o),s[y]=b,g.inboundNodes.forEach(_=>{if(!(_ instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${_}`);i(b,_)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!JM(a);)for(let g of p){let y=s[g.name];if(y.name in a){let b=a[y.name];delete a[y.name];for(let w of b)l(y,w)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let y=g[0],b=g[1],w=g[2];jn(y in s);let k=s[y].inboundNodes[b].outputTensors;m.push(k[w])}let h=t.outputLayers;for(let g of h){let y=g[0],b=g[1],w=g[2];jn(y in s);let k=s[y].inboundNodes[b].outputTensors;f.push(k[w])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. 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Ct(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in QL?QL[r]:r,config:{}};return ez(t)}else return r instanceof hC?r:ez(r)}var ad=class extends Le{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Tr(e);return this.maxValue!=null&&(n=ar(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};ad.className="ReLU";te.registerClass(ad);var ld=class extends Le{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Na(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};ld.className="LeakyReLU";te.registerClass(ld);var ud=class extends Le{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ct(e.alphaRegularizer),this.alphaConstraint=Bt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new B(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=et(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)t[o-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o<e.length;++o)n[o]=e[o];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),$a(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Tt(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:zt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};ud.className="PReLU";te.registerClass(ud);var cd=class extends Le{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Te(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Is(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};cd.className="ELU";te.registerClass(cd);var pd=class extends Le{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return n.mul(Ua(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};pd.className="ThresholdedReLU";te.registerClass(pd);var md=class extends Le{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new id().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};md.className="Softmax";te.registerClass(md);function Tl(r,e,t){if(typeof r=="number")return Yo(r,e);if(r.length!==e)throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. 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instead`);if(s==="channelsFirst"&&(r=qe(r,[0,2,1])),o==="causal")throw new Te("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Su(r,e,n,o==="same"?"same":"valid","NWC",a);return t!=null&&(i=ln(i,t)),i})}function tz(r,e,t,n=[1,1],o="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Jr()),Ot(s),r.rank!==3&&r.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=dd(r,s);if(o==="causal")throw new Te("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vo.conv2d({x:l,filter:e,strides:n,pad:o==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=qe(l,[0,3,1,2])),l})}function ZQ(r,e,t,n=[1,1,1],o="valid",s,a){return V(()=>{if(s==null&&(s=Jr()),Ot(s),r.rank!==4&&r.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=gC(r,s);if(o==="causal")throw new Te("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Om(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ln(i,t)),s==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var Up=class extends Le{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Up.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Te(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Tl(t.kernelSize,e,"kernelSize"),this.strides=Tl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Qr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ot(this.dataFormat),this.activation=zs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=Ct(t.biasRegularizer),this.activityRegularizer=Ct(t.activityRegularizer),this.dilationRate=Tl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(jn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!dx(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ls(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:zt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},dc=class extends Up{constructor(e,t){super(e,t);this.kernel=null,dc.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=Ct(t.kernelRegularizer)}build(e){e=et(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],o=this.kernelSize.concat([n,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:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let n,o=this.bias==null?null:this.bias.read(),s=hx(this.activation.getClassName());if(s!=null&&this.rank===2)n=tz(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=YQ(e,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=tz(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=ZQ(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Te("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=et(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=mn(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let o=[e[0]];return this.dataFormat==="channelsLast"?(o=o.concat(t),o.push(this.filters)):(o.push(this.filters),o=o.concat(t)),o}getConfig(){let e={filters:this.filters,kernelInitializer:Tt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:zt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},El=class extends dc{constructor(e){super(2,e);El.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!dx(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};El.className="Conv2D";te.registerClass(El);var hc=class extends dc{constructor(e){super(3,e);hc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};hc.className="Conv3D";te.registerClass(hc);var hd=class extends El{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=et(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Oe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=o[a],u=o[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=fd(l,m,c,this.padding),h=fd(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let y=Nu(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=qe(y,[0,3,1,2])),this.bias!=null&&(y=ln(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=et(e);let t=e.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[o]=fd(t[o],l,a,this.padding),t[s]=fd(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};hd.className="Conv2DTranspose";te.registerClass(hd);var xC=class extends dc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ct(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ct(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=et(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let n;if(this.rank===1)throw new Te("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=Km(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ln(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=zt(this.depthwiseConstraint),e.pointwiseConstraint=zt(this.pointwiseConstraint),e}};xC.className="SeparableConv";var gd=class extends xC{constructor(e){super(2,e)}};gd.className="SeparableConv2D";te.registerClass(gd);var gc=class extends dc{constructor(e){super(1,e);gc.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!dx(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};gc.className="Conv1D";te.registerClass(gc);var xd=class extends Le{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=jf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return jf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=jf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return jf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};xd.className="Cropping2D";te.registerClass(xd);var yd=class extends Le{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,lL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Oe(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a]);return qe(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};yd.className="UpSampling2D";te.registerClass(yd);function JQ(r,e,t=[1,1],n="valid",o,s){return V(()=>{o==null&&(o=Jr()),Ot(o);let a=dd(r,o);if(r.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=zn(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=qe(a,[0,3,1,2])),a})}var bd=class extends Up{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=Ct(e.depthwiseRegularizer)}build(e){if(e=et(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Oe(e);let n=JQ(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ln(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=et(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=mn(t,this.kernelSize[0],this.padding,this.strides[0]),a=mn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=zt(this.depthwiseRegularizer),e}};bd.className="DepthwiseConv2D";te.registerClass(bd);function yC(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function bC(r,e,t,n=!1,o,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Vr(2,l));if(e=qe(e,u),s!=null)throw new Te("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===l-1&&(o=wr(o,-1)),o=qe(o,u)),n&&(e=Zt(e,0),o!=null&&(o=Zt(o,0)));let c=[],p,m=t,f=e.shape[0],d=pr(e),h;o!=null&&(h=pr(o));for(let y=0;y<f;++y){let b=d[y],w=V(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let _=V(()=>{let k=h[y],E=nr(k).sub(k),N=w[0].mul(k).add(m[0].mul(E)),F=m.map((O,P)=>w[1][P].mul(k).add(O.mul(E)));return{output:N,newStates:F}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=jt(c,1)),[p,g,m]})}var fn=class extends Le{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new qp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Vr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){kx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Te("Constants support is not implemented in RNN yet.");kx(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,o=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...o]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Te("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!x.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Sn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>pt([n,o])):this.states_=[pt([n,this.cell.stateSize])];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>pt([n,o])):this.states_[0]=pt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):De(this.states_);for(let o=0;o<this.states_.length;++o){let s=e[o],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,i=[n,a];if(!x.arraysEqual(s.shape,i))throw new B(`State ${o} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>$t(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=yC(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof jr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;e=Oe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new B(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:o},u=bC((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=pt(e.shape);return t=be(t,[1,2]),t=qa(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?yx(t,[1,n]):t):this.cell.stateSize>1?[yx(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===fn.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=en(o,n);return new e(Object.assign(t,{cell:s}))}};fn.className="RNN";te.registerClass(fn);var Al=class extends Le{},Hp=class extends Al{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=zs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=ac([1,Ps([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ac([1,Ps([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=et(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(e),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(n),rate:this.recurrentDropout,training:o}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=ts(M(e,a),this.kernel.read()):s=ts(e,this.kernel.read()),this.bias!=null&&(s=ln(s,this.bias.read())),i!=null&&(n=M(n,i));let l=ee(s,ts(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ls(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),recurrentConstraint:zt(this.recurrentConstraint),biasConstraint:zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Hp.className="SimpleRNNCell";te.registerClass(Hp);var wd=class extends fn{constructor(e){e.cell=new Hp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};wd.className="SimpleRNN";te.registerClass(wd);var Kp=class extends Al{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=zs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=zs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=ac([1,Ps([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ac([1,Ps([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=et(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(o),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=M(e,s[0]));let c=ts(e,this.kernel.read());this.useBias&&(c=ln(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=M(o,a[0]));let p=this.recurrentKernel.read(),[m,f]=cr(p,[2*this.units,this.units],p.rank-1),d=ts(o,m),[h,g,y]=cr(c,3,c.rank-1),[b,w]=cr(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,b)),l=this.recurrentActivation.apply(ee(g,w));let _=ts(M(l,o),f);u=this.activation.apply(ee(y,_));let k=ee(M(i,o),M(ee(1,He(i)),u));return[k,k]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ls(this.activation),recurrentActivation:Ls(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),recurrentConstraint:zt(this.recurrentConstraint),biasConstraint:zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Kp.className="GRUCell";te.registerClass(Kp);var _d=class extends fn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Kp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};_d.className="GRU";te.registerClass(_d);var Dl=class extends Al{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=zs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=zs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ct(e.kernelRegularizer),this.recurrentRegularizer=Ct(e.recurrentRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=ac([1,Ps([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ac([1,Ps([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=et(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends un{apply(l,u){let c=s.apply([a]),p=new uc().apply([a]),m=s.apply([a*2]);return j0(j0(c,p),m)}},t.className="CustomInit",t)}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(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(o),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=M(e,a[0]));let m=ts(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=M(o,i[0])),m=ee(m,ts(o,this.recurrentKernel.read())),this.useBias&&(m=ln(m,this.bias.read()));let[f,d,h,g]=cr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=ee(M(u,s),M(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=M(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ls(this.activation),recurrentActivation:Ls(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),recurrentConstraint:zt(this.recurrentConstraint),biasConstraint:zt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Dl.className="LSTMCell";te.registerClass(Dl);var vd=class extends fn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Dl(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};vd.className="LSTM";te.registerClass(vd);var qp=class extends Al{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];n=o[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=l.call(a,t),s.push(a.slice(1))}n=[];for(let i of s.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){kx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,o)=>{Os(`RNNCell_${o}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(en(s,n));return new e({cells:o})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Jf(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}zp(t)}};qp.className="StackedRNNCells";te.registerClass(qp);function Ka(r){let{ones:e,rate:t,training:n=!1,count:o=1}=r,s=()=>wx(e(),t),a=()=>vl(s,e,n);return!o||o<=1?$t(a().clone()):Array(o).fill(void 0).map(a).map(l=>$t(l.clone()))}var QQ=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)e.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(t[n[o]]=r[n[o]]);return t};var wC=class extends fn{constructor(e){if(e.unroll)throw new Te("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Te("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(De(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(De(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=pt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Sn("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>pt(s)):this.states_=[pt(s)];else if(e==null)De(this.states_),this.keptStates!=null&&(De(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>pt(s)):this.states_[0]=pt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):De(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!x.arraysEqual(l.shape,u))throw new B(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>$t(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=mn(u,o[0],s,a[0],i[0]),m=mn(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};wC.className="ConvRNN2D";var Xp=class extends Dl{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Tl(n,2,"kernelSize"),this.kernelSize.forEach(l=>Kt(l,"kernelSize")),this.strides=Tl(o||1,2,"strides"),this.strides.forEach(l=>Kt(l,"strides")),this.padding=s||"valid",Qr(this.padding),this.dataFormat=a||"channelsLast",Ot(this.dataFormat),this.dilationRate=Tl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Kt(l,"dilationRate"))}build(e){var t;e=et(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends un{apply(m,f){let d=u.apply([c]),h=Nr([c]),g=u.apply([c*2]);return Ep([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>nr(o),rate:this.dropout,training:n,count:i}));let l=this.dropoutMask,u=(ie,ae,ue)=>!ae||!ae[ue]?ie:M(ae[ue],ie),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>nr(s),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),y=u(s,d,2),b=u(s,d,3),w=3,[_,k,E,N]=cr(this.kernel.read(),i,w),[F,O,P,W]=this.useBias?cr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,F,this.padding),p=this.inputConv(p,k,O,this.padding),m=this.inputConv(m,E,P,this.padding),f=this.inputConv(f,N,W,this.padding);let[G,j,X,K]=cr(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,j),y=this.recurrentConv(y,X),b=this.recurrentConv(b,K);let Y=this.recurrentActivation.apply(ee(c,h)),ne=this.recurrentActivation.apply(ee(p,g)),J=ee(M(ne,a),M(Y,this.activation.apply(ee(m,y)))),Q=M(this.recurrentActivation.apply(ee(f,b)),this.activation.apply(J));return[Q,Q,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=QQ(e,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(e,t,n,o){let s=Xr(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ln(s,n,this.dataFormat):s}recurrentConv(e,t){return Xr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xp.className="ConvLSTM2DCell";te.registerClass(Xp);var kd=class extends wC{constructor(e){let t=new Xp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};kd.className="ConvLSTM2D";te.registerClass(kd);var Yp=class extends Le{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?t[o]:this.noiseShape[o]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let o=t.training==null?!1:t.training,s=this.getNoiseShape(n);return vl(()=>wx(n,this.rate,s,this.seed),()=>n,o)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Yp.className="Dropout";te.registerClass(Yp);var Cd=class extends Yp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Cd.className="SpatialDropout1D";te.registerClass(Cd);var Id=class extends Le{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=zs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=Ct(e.kernelRegularizer),this.biasRegularizer=Ct(e.biasRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=et(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=et(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e),o=hx(this.activation.getClassName()),s;return o!=null?s=ts(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=ts(n,this.kernel.read()),this.bias!=null&&(s=ln(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ls(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:zt(this.kernelConstraint),biasConstraint:zt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Id.className="Dense";te.registerClass(Id);var Sd=class extends Le{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=et(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],es(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=n.transpose(o)}return hL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Sd.className="Flatten";te.registerClass(Sd);var Nd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.activation=zs(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ls(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Nd.className="Activation";te.registerClass(Nd);var Td=class extends Le{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Oe(e),fL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Td.className="RepeatVector";te.registerClass(Td);var Ed=class extends Le{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",o=t.slice(),s=1,a=null;for(let l=0;l<o.length;++l){let u=o[l];if(this.isUnknown(u))if(a===null)a=l;else throw new B("Can only specifiy one unknown dimension.");else s*=u}let i=es(e);if(a!==null){if(s===0||i%s!=0)throw new B(n);o[a]=i/s}else if(i!==s)throw new B(n);return o}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return n.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Ed.className="Reshape";te.registerClass(Ed);var Ad=class extends Le{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Vr(1,e.dims.length+1);if(!x.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=et(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Ad.className="Permute";te.registerClass(Ad);var Dd=class extends Le{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),o=-1;return ll(vn(n,this.maskValue),o)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e),o=-1,s=!0,a=ll(vn(n,this.maskValue),o,s);return n.mul(a.asType(n.dtype))})}};Dd.className="Masking";te.registerClass(Dd);var $d=class extends Le{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(kt(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ct(e.embeddingsRegularizer),this.activityRegularizer=Ct(e.activityRegularizer),this.embeddingsConstraint=Bt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Oe(e),vn(e,Se(e))):null)}computeOutputShape(e){if(e=et(e),this.inputLength==null)return[...e,this.outputDim];let t=kt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let o=0;o<t.length;++o){let s=t[o],a=e[o+1];if(s!=null&&a!=null&&s!==a)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);return n.dtype!=="int32"&&(n=Ua(n,"int32")),bx(this.embeddings.read(),n.as1D()).reshape(et(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Tt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:zt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};$d.className="Embedding";te.registerClass($d);var $l=class extends Le{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Te}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let o=0;o<t.length;++o){let s=e[e.length-t.length+o],a=t[o];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[et(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ld.className="Concatenate";te.registerClass(Ld);function zd(r,e){for(;r<0;)r+=e;return r}function eee(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Te("batchDot is not implemented for tensors of 4D or higher rank yet");if(x.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),x.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Te("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return V(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;n>o?l=n+o-3:l=n-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var Bd=class extends $l{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){x.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Te("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new B(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Vd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);return vl(()=>Ap(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Vd.className="GaussianNoise";te.registerClass(Vd);var Wd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?vl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return n.mul(Ap(n.shape,1,s))},()=>n,t.training||!1):n})}};Wd.className="GaussianDropout";te.registerClass(Wd);var jd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return vl(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=Pr(Ts(n),this.rate);u=Ua(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Oe(e),t.training||!1)}return e})}};jd.className="AlphaDropout";te.registerClass(jd);function Gd(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=bw(r,e,t,n,o,s);else if(r.rank===3)a=ww(r,e,t,n,o,s);else if(r.rank===4)a=_w(r,e,t,n,o,s);else throw new Te(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function tee(r,e,t,n,o=.001){return V(()=>{let s=Zc(r,n),a=s.mean,i=s.variance;return[Gd(r,a,i,t,e,o),a,i]})}function ree(r,e,t,n,o=.001){return V(()=>{let s=Zc(r,n),a=s.mean,i=s.variance,l=[];for(let d of Vr(0,r.rank))n.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[Gd(r,u,c,m,p,o),a,i]})}function nee(r,e,t,n,o=.001){return x.arraysEqual(n.slice().sort(),Vr(0,r.rank-1))?tee(r,e,t,n,o):ree(r,e,t,n,o)}var Ud=class extends Le{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Bt(e.betaConstraint),this.gammaConstraint=Bt(e.gammaConstraint),this.betaRegularizer=Ct(e.betaRegularizer),this.gammaRegularizer=Ct(e.gammaRegularizer)}build(e){e=et(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let o=[n];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(e,t){return V(()=>{let n=t.training==null?!1:t.training,o=Oe(e),s=o.shape,a=s.length,i=Vr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Yo(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!x.arraysEqual(c,Vr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return Gd(o,b,w,_,k,this.epsilon)}else return Gd(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=nee(o,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let k=1-_,E=b.read(),N=E.sub(w).mul(k);b.write(E.sub(N))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),movingMeanInitializer:Tt(this.movingMeanInitializer),movingVarianceInitializer:Tt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:zt(this.betaConstraint),gammaConstraint:zt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ud.className="BatchNormalization";te.registerClass(Ud);var qd=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=Ct(e.betaRegularizer),this.gammaRegularizer=Ct(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=et(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Qo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),o=n.shape,s=o.length;return V(()=>{let a=!0,{mean:i,variance:l}=Zc(n,this.axis,a),u=Yo(1,s);for(let h of this.axis)u[h]=o[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),Gd(n,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};qd.className="LayerNormalization";te.registerClass(qd);function oee(r,e,t){return V(()=>{if(r.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Jr()),t!=="channelsLast"&&t!=="channelsFirst")throw new B(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return t==="channelsFirst"?n=[[0,0],[0,0],e[0],e[1]]:n=[[0,0],e[0],e[1],[0,0]],Mr(r,n)})}var Hd=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Jr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new B(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new B(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=et(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>oee(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Hd.className="ZeroPadding2D";te.registerClass(Hd);function jx(r,e,t,n,o,s){return V(()=>{Ot(o),V0(s),Qr(n),t==null&&(t=[1,1]),n==null&&(n="valid"),o==null&&(o=Jr()),s==null&&(s="max"),r=dd(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Aa(r,e,t,i):a=ka(r,e,t,i),o==="channelsFirst"&&(a=qe(a,[0,3,1,2])),a})}function rz(r,e,t,n,o,s){return V(()=>{Ot(o),V0(s),Qr(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=Jr()),s==null&&(s="max"),r=gC(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=Gm(r,e,t,i):a=Rm(r,e,t,i),o==="channelsFirst"&&(a=qe(a,[0,4,1,2,3])),a})}var _C=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Qr(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=et(e);let t=mn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=qa(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return kn(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Kd=class extends _C{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),Qr(o),jx(e,t,n,o,s,"max")}};Kd.className="MaxPooling1D";te.registerClass(Kd);var Xd=class extends _C{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),Qr(o),jx(e,t,n,o,s,"avg")}};Xd.className="AveragePooling1D";te.registerClass(Xd);var vC=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new B(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),Qr(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=et(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=mn(t,this.poolSize[0],this.padding,this.strides[0]),n=mn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Yd=class extends vC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),Qr(o),jx(e,t,n,o,s,"max")}};Yd.className="MaxPooling2D";te.registerClass(Yd);var Zd=class extends vC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),Qr(o),jx(e,t,n,o,s,"avg")}};Zd.className="AveragePooling2D";te.registerClass(Zd);var kC=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),Qr(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=et(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=mn(t,this.poolSize[0],this.padding,this.strides[0]),n=mn(n,this.poolSize[1],this.padding,this.strides[1]),o=mn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Jd=class extends kC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),Qr(o),rz(e,t,n,o,s,"max")}};Jd.className="MaxPooling3D";te.registerClass(Jd);var Qd=class extends kC{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ot(s),Qr(o),rz(e,t,n,o,s,"avg")}};Qd.className="AveragePooling3D";te.registerClass(Qd);var CC=class extends Le{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Te}},eh=class extends CC{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Oe(e);return wt(n,1)})}};eh.className="GlobalAveragePooling1D";te.registerClass(eh);var th=class extends CC{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Oe(e);return ur(n,1)})}};th.className="GlobalMaxPooling1D";te.registerClass(th);var IC=class extends Le{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ot(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Te}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},rh=class extends IC{call(e,t){return V(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?wt(n,[1,2]):wt(n,[2,3])})}};rh.className="GlobalAveragePooling2D";te.registerClass(rh);var nh=class extends IC{call(e,t){return V(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?ur(n,[1,2]):ur(n,[2,3])})}};nh.className="GlobalMaxPooling2D";te.registerClass(nh);var SC=class extends Le{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let o=t.layer,s=en(o,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},oh=class extends SC{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=et(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=et(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),o=e[1];return[n[0],o].concat(n.slice(1))}call(e,t){return V(()=>(e=Oe(e),bC((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};oh.className="TimeDistributed";te.registerClass(oh);function see(r){Ui(aL,"BidirectionalMergeMode",r)}var iee="concat",sh=class extends SC{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=en(n),t.goBackwards=t.goBackwards!==!0;let o={};if(o.className=e.layer.getClassName(),o.config=t,this.backwardLayer=en(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?iee:e.mergeMode,see(this.mergeMode),e.weights)throw new Te("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,o,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):xr(o)}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=yC(e,n,o,this.numConstants);if(e=s.inputs,n=s.initialState,o=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&o==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(p=>new Dt({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(o!=null)throw new Te("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof jr;for(let u of a)if(u instanceof jr!==l)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,o,s;if(n==null)o=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=n.slice(0,n.length/2),u=n.slice(n.length/2);o=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(o)&&(a=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=Zt(s,1));let i;return this.mergeMode==="concat"?i=Ep([o,s]):this.mergeMode==="sum"?i=ee(o,s):this.mergeMode==="ave"?i=M(.5,ee(o,s)):this.mergeMode==="mul"?i=M(o,s):this.mergeMode==null&&(i=[o,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Os(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Os(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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V(()=>xz(s,a,i));case"control":return vz(s,a,i);case"convolution":return V(()=>Cz(s,a,i));case"creation":return V(()=>Iz(s,a,i));case"dynamic":return Sz(s,a,i);case"evaluation":return V(()=>Nz(s,a,i));case"image":return V(()=>Az(s,a,i));case"graph":return V(()=>Tz(s,a,i));case"logical":return V(()=>Dz(s,a,i));case"matrices":return V(()=>$z(s,a,i));case"normalization":return V(()=>Rz(s,a,i));case"reduction":return V(()=>Fz(s,a,i));case"slice_join":return V(()=>Oz(s,a,i));case"spectral":return V(()=>Pz(s,a,i));case"transformation":return V(()=>Mz(s,a,i));case"hash_table":return Ez(s,a,i,n);case"custom":let l=Hx(s.op);if(l&&l.customExecutor)return l.customExecutor(new QC(s,a,i));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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Lz(r,e,t){let{usedNodes:n,inputs:o}=t,s=[],a=Object.keys(o).map(c=>tn(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{n.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var rre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],nre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],ore=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function oI(r){return rre.indexOf(r.op)>=0}function ere(r){return nre.indexOf(r.op)>=0}function tre(r){return ore.indexOf(r.op)>=0}var Zp=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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c}processChildNodes(e,t,n,o,s,a){e.children.forEach(i=>{let[l]=Bs(i.name,n);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!yr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!yr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[o]=tn(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((l,u)=>a[u]===-1||a[u]===l);x.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&x.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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Mismatched at element ${this.count}.`);case Xa.SHORTEST:return{value:null,done:!0};case Xa.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},yI=class extends Jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new ah(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},_3=class extends yI{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=a3.alea(n||x.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var Yi=class{constructor(){this.size=null}batch(e,t=!0){let n=this;x.assert(e>0,()=>`batchSize needs to be positive, but it is
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`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var py='"',ch=Symbol("out"),I3=Symbol("field"),my=Symbol("quote"),bI=Symbol("quoteafterquote"),S3=Symbol("quoteinquote"),ph=class extends Yi{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new uh(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(x.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&x.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(t).filter(o=>t[o]>1);if(x.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-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(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?o[a]=u:n[a]=u}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],o=0,s=e.length,a=ch;for(let i=0;i<s;i++)switch(a){case ch:switch(e.charAt(i)){case py:o=i+1,a=my;break;case this.delimiter:if(o=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=ch;break;default:a=I3,o=i;break}break;case I3:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i)),a=ch,o=i+1;break;default:}break;case my:switch(e.charAt(i)){case py:a=bI;break;default:}break;case bI:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i-1)),a=ch,o=i+1;break;case py:a=my;break;default:a=S3;break}break;case S3:switch(e.charAt(i)){case py:a=my;break;default:}break;default:}if(a===bI?n.push(e.substring(o,s-1)):n.push(e.substring(o)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var mh=class extends Jt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(U().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new mh(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(x.sizeFromShape(t));return n.set(e,n.length-e.length),Or(n,t)}};var fh=class extends Jt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Gt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=Bi([a,s,l,i],[1,4])}else this.cropBox=Bi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(U().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new fh(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&x.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=eg.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=e.toFloat().expandDims(0),n;n=As.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return n.reshape(o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var dh=class{};var fy=class extends Jt{split(e){return new N3(this,e)}},N3=class extends fy{constructor(e,t){super();this.upstream=e,this.impl=new T3(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},T3=class extends Qp{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}};var wI=class extends Jt{decodeUTF8(){return new A3(this)}},A3=class extends fy{constructor(e){super();this.upstream=e,this.impl=new D3(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},D3=class extends Qp{constructor(e){super();if(this.upstream=e,U().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=E3();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return U().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}};var hh=class extends wI{constructor(e,t={}){super();this.file=e,this.options=t,x.assert(e instanceof Uint8Array||(U().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>n(new Error("Aborted")),s.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,o);s.readAsArrayBuffer(a)}this.offset=o}),done:!1}}};async function $3(r,e={}){let t,n;typeof r=="string"?t=r:(t=r.url,n=xre(r));let o=await x.fetch(t,n);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new hh(s,e)}else throw new Error(o.statusText)}var xre=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function dy(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var gh=class extends dh{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(dy(this.input)&&U().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new hh(this.input,this.options)}};var xh=class extends dh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return dy(this.url)?new gh(this.url,this.fileOptions).iterator():$3(this.url,this.fileOptions)}};function R3(r,e={}){return new ph(new xh(r),e)}function F3(r){let e=lh(r);return dn(async()=>e)}function O3(r){return dn(async()=>{let e=await r();return lh(()=>e.next())})}async function P3(r,e){return fh.create(r,e)}async function M3(r){return mh.create(r)}var L3="2.8.3";var yre={tfjs:$I,"tfjs-core":RI,"tfjs-data":FI,"tfjs-layers":OI,"tfjs-converter":PI,"tfjs-backend-cpu":M_,"tfjs-backend-webgl":Qv,"tfjs-backend-wasm":F0};export{is as Abs,qs as Acos,Hs as Acosh,np as AdadeltaOptimizer,op as AdagradOptimizer,sp as AdamOptimizer,ip as AdamaxOptimizer,bn as Add,Hn as AddN,Gl as All,Ul as Any,Kn as ArgMax,na as ArgMin,Ks as Asin,Xs as Asinh,Ys as Atan,Js as Atan2,Zs as Atanh,Xn as AvgPool,oa as AvgPool3D,Hl as AvgPool3DGrad,ql as AvgPoolGrad,mx as BackendWasm,Yn as BatchMatMul,sa as BatchToSpaceND,Kl as Bincount,Ob as BroadcastTo,Gx as Callback,Sx as CallbackList,On as Cast,Qs as Ceil,Pn as ClipByValue,Xl as Complex,ia as ComplexAbs,as as Concat,Zn as Conv2D,Yl as Conv2DBackpropFilter,Jn as Conv2DBackpropInput,aa as Conv3D,Zl as Conv3DBackpropFilterV2,Jl as Conv3DBackpropInputV2,Qn as Cos,ei as Cosh,ti as CropAndResize,eo as Cumsum,Tx as CustomCallback,el as DataStorage,Ql as DenseBincount,ri as DepthToSpace,to as DepthwiseConv2dNative,eu as DepthwiseConv2dNativeBackpropFilter,tu as DepthwiseConv2dNativeBackpropInput,ru as Diag,la as Dilation2D,Pc as Dilation2DBackpropFilter,Oc as Dilation2DBackpropInput,Db as ENV,qx as EarlyStopping,ni as Elu,nu as EluGrad,jh as Environment,si as Equal,oi as Erf,no as Exp,ls as ExpandDims,ii as Expm1,ou as FFT,ua as Fill,ai as FlipLeftRight,oo as Floor,so as FloorDiv,Mc as FromPixels,io as FusedBatchNorm,_s as FusedConv2D,vs as FusedDepthwiseConv2D,Ug as GPGPUContext,li as GatherNd,us as GatherV2,ay as GraphModel,ui as Greater,ao as GreaterEqual,Nx as History,su as IFFT,cs as Identity,iu as Imag,Dt as InputSpec,ci as IsFinite,pi as IsInf,mi as IsNan,js as KernelBackend,ca as LRN,lu as LRNGrad,Zf as LayerVariable,Nn as LayersModel,lo as LeakyRelu,fi as Less,di as LessEqual,au as LinSpace,uo as Log,hi as Log1p,Pb as LogSoftmax,gi as LogicalAnd,tl as LogicalNot,rl as LogicalOr,Cg as MathBackendCPU,Xg as MathBackendWebGL,co as Max,mo as MaxPool,pa as MaxPool3D,cu as MaxPool3DGrad,uu as MaxPoolGrad,pu as MaxPoolWithArgmax,po as Maximum,fo as Mean,ho as Min,go as Minimum,ma as MirrorPad,xi as Mod,ap as MomentumOptimizer,mu as Multinomial,xo as Multiply,ps as Neg,bi as NonMaxSuppressionV3,wi as NonMaxSuppressionV4,_i as NonMaxSuppressionV5,yi as NotEqual,tS as OP_SCOPE_SUFFIX,yo as OneHot,ms as OnesLike,Lr as Optimizer,fs as Pack,bo as PadV2,IV as Pool,wo as Pow,_o as Prelu,vi as Prod,lp as RMSPropOptimizer,fn as RNN,fa as Range,Vb as Rank,fu as Real,ro as RealDiv,ki as Reciprocal,Ut as Reduction,vo as Relu,Co as Relu6,ds as Reshape,ko as ResizeBilinear,hu as ResizeBilinearGrad,da as ResizeNearestNeighbor,du as ResizeNearestNeighborGrad,Io as Reverse,Ri as RotateWithOffset,So as Round,No as Rsqrt,ml as SGDOptimizer,Ci as ScatterNd,hs as Select,Ii as Selu,Xi as Sequential,Eo as Sigmoid,Ni as Sign,To as Sin,Si as Sinh,gs as Slice,$o as Softmax,Ti as Softplus,ha as SpaceToBatchND,gu as SparseToDense,xs as SplitV,Ao as Sqrt,ga as Square,Ro as SquaredDifference,$i as Step,Ei as StridedSlice,Fo as Sub,Do as Sum,jr as SymbolicTensor,Ai as Tan,Oo as Tanh,R as Tensor,ct as TensorBuffer,wn as Tile,Di as TopK,Po as Transpose,xu as Unique,ys as Unpack,xa as UnsortedSegmentSum,sl as Variable,bs as ZerosLike,ws as _FusedMatMul,At as abs,Im as acos,Sm as acosh,ee as add,hw as addN,o_ as addStrict,ku as all,ll as any,ul as argMax,Nm as argMin,Tm as asin,Em as asinh,Am as atan,Dm as atan2,$m as atanh,ka as avgPool,Rm as avgPool3d,dw as backend,T as backend_util,Cj as basicLSTMCell,Lo as batchNorm,bw as batchNorm2d,ww as batchNorm3d,_w as batchNorm4d,Ca as batchToSpaceND,vw as bincount,$U as booleanMaskAsync,cl as broadcastTo,eg as browser,Ie as buffer,lz as callbacks,oe as cast,Fm as ceil,ar as clipByValue,Mn as clone,_n as complex,Qe as concat,kw as concat1d,Cw as concat2d,Iw as concat3d,Sw as concat4d,B0 as constraints,Su as conv1d,Xr as conv2d,Nu as conv2dTranspose,Om as conv3d,Uj as conv3dTranspose,TV as copyRegisteredKernels,Ia as cos,Tu as cosh,of as cosineWindow,Eu as cumsum,Zr as customGrad,hy as data,Nw as denseBincount,Pt as deprecationWarn,Pm as depthToSpace,zn as depthwiseConv2d,pz as deregisterOp,Wc as device_util,Qj as diag,Mm as dilation2d,MW as disableDeprecationWarnings,De as dispose,LW as disposeVariables,fe as div,Lm as divNoNan,s_ as divStrict,Tw as dot,h_ as dropout,Is as elu,PW as enableDebugMode,OW as enableProdMode,g_ as enclosingPowerOfTwo,Cs as engine,U as env,Yr as equal,Jw as equalStrict,zm as erf,er as exp,wr as expandDims,Bm as expm1,Yc as eye,Fa as fft,Sa as fill,GW as findBackend,UW as findBackendFactory,Ss as floor,vu as floorDiv,ek as forceHalfFloat,Vo as fused,zo as gather,d_ as gatherND,tg as gather_util,WW as getBackend,Gh as getGradient,ym as getKernel,bm as getKernelsForBackend,xD as gpgpu_util,TG as grad,EG as grads,Yt as greater,Pr as greaterEqual,Qw as greaterEqualStrict,e_ as greaterStrict,zi as ifft,Au as imag,As as image,rq as inTopKAsync,U0 as initializers,Wx as input,Ir as io,Wu as irfft,Ew as isFinite,Aw as isInf,Dw as isNaN,$t as keep,Ar as kernel_impls,NC as layers,Na as leakyRelu,Ta as less,on as lessEqual,t_ as lessEqualStrict,r_ as lessStrict,__ as linalg,$w as linspace,zz as loadGraphModel,XL as loadLayersModel,Vm as localResponseNormalization,lr as log,Du as log1p,Rw as logSigmoid,$u as logSoftmax,jm as logSumExp,hr as logicalAnd,Ea as logicalNot,Ru as logicalOr,Mw as logicalXor,Kq as losses,Ue as matMul,$S as math,ur as max,Aa as maxPool,Gm as maxPool3d,Lw as maxPoolWithArgmax,Sr as maximum,i_ as maximumStrict,wt as mean,Hc as memory,DC as metrics,Li as min,Vn as minimum,a_ as minimumStrict,Um as mirrorPad,Fu as mod,l_ as modStrict,HL as model,$C as models,Zc as moments,YU as movingAverage,M as mul,u_ as mulStrict,o4 as multiRNNCell,zw as multinomial,He as neg,sf as nextFrame,Gu as norm,vn as notEqual,n_ as notEqualStrict,ks as oneHot,Nr as ones,nr as onesLike,S as op,u4 as outerProduct,Mr as pad,m4 as pad1d,d4 as pad2d,g4 as pad3d,y4 as pad4d,Bw as pool,_r as pow,c_ as powStrict,$a as prelu,ow as print,Ou as prod,zW as profile,N4 as rand,O4 as randomGamma,fg as randomNormal,Ts as randomUniform,Qc as range,VW as ready,pl as real,qm as reciprocal,_u as registerBackend,YL as registerCallbackConstructor,Lb as registerGradient,nl as registerKernel,cz as registerOp,RC as regularizers,Tr as relu,Mu as relu6,jW as removeBackend,z as reshape,Zt as reverse,G4 as reverse1d,q4 as reverse2d,K4 as reverse3d,Y4 as reverse4d,Oa as rfft,Hm as round,Lu as rsqrt,ce as scalar,f_ as scatterND,rg as scatter_util,zu as selu,Km as separableConv2d,KL as sequential,te as serialization,KS as setBackend,qW as setPlatform,DJ as setWasmPath,$J as setWasmPaths,ov as setWebGLContext,Xw as setdiff1dAsync,Sg as shared,Kr as sigmoid,Xm as sign,Hq as signal,Bu as sin,Vu as sinh,Fe as slice,Ym as slice1d,dg as slice2d,Zm as slice3d,ep as slice4d,ir as slice_util,Ra as softmax,Ns as softplus,Da as spaceToBatchND,nf as sparseToDense,qq as spectral,cr as split,_t as sqrt,Me as square,Pa as squaredDifference,p_ as squaredDifferenceStrict,kn as squeeze,jt as stack,Es as step,Jm as stridedSlice,pe as sub,m_ as subStrict,be as sum,yu as sumOutType,Qm as tan,Mi as tanh,Or as tensor,Gt as tensor1d,Bi as tensor2d,lw as tensor3d,vU as tensor4d,kU as tensor5d,CU as tensor6d,Mo as tensor_util,US as test_util,V as tidy,Bn as tile,BW as time,ef as topk,fl as train,qe as transpose,ju as truncatedNormal,tp as unique,NV as unregisterGradient,SV as unregisterKernel,tf as unsortedSegmentSum,pr as unstack,dr as upcastType,x as util,AG as valueAndGrad,DG as valueAndGrads,Yw as variable,ug as variableGrads,yre as version,Bz as version_converter,FW as version_core,M_ as version_cpu,jp as version_layers,F0 as version_wasm,Qv as version_webgl,z8 as webgl,mD as webgl_util,Rt as where,rf as whereAsync,pt as zeros,Se as zerosLike};
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/** @license See the LICENSE file. */
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