face-api/dist/tfjs.esm.js

  /*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
  */

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`;for(let h=2;h<l;h++)d+=`
`;return m[m.length-1]=" "+m[m.length-1]+"]"+(s?"":d),m}function ym(r){let e=[];for(let t=0;t<r.length;t+=2)e.push([r[t],r[t+1]]);return e}var ct=class{constructor(e,t,o){if(this.dtype=t,this.shape=e.slice(),this.size=dt(e),o!=null){let n=o.length;E(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=o||lb(t,this.size),this.strides=Us(e)}set(e,...t){t.length===0&&(t=[0]),E(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let o=this.locToIndex(t);this.values[o]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let s=`Requested out of range element at ${e}.   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To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Ri().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>zc(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,o=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:o},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:o}=this.initializeBackend(e);if(!(o?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new vb(this.backendInstance),!0}setupRegisteredKernels(){hm(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){hm(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof Ws)&&typeof o.then=="function"){let n=++this.pendingBackendInitId,s=o.then(a=>n<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(n<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]=o,{success:!0,asyncInit:!1}}catch(o){return console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 o=e[t],{success:n,asyncInit:s}=this.initializeBackend(o);if(s||n)return{name:o,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let o=this.state.tensorInfo.get(t),n=o.backend,s=this.readSync(t);n.disposeData(t),o.backend=e,e.move(t,s,o.shape,o.dtype),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let o=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");o=e}let n;return this.scopedRun(()=>this.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return bu.nextTensorId++}nextVariableId(){return bu.nextVariableId++}clone(e){let t=this.makeTensorFromDataId(e.dataId,e.shape,e.dtype),o={x:e},n=a=>({x:()=>{let i="float32",l={x:a},u={dtype:i};return A.runKernelFunc(c=>c.cast(a,i),l,null,Fo,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o,n,s){let a=null,i=null;return this.runKernelFunc(a,t,i,e,o,n,s)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e,t,o,n,s,a,i){let l,u=[],c=this.isTapeOn();n==null&&(n=this.state.activeScope!=null?this.state.activeScope.name:"");let p=this.state.numBytes,m=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let f;this.backendName==null&&this.backend;let d=dm(n,this.backendName),h;if(d!=null)f=()=>{let y=this.backend.numDataIds();h=d.kernelFunc({inputs:t,attrs:s,backend:this.backend});let b=Array.isArray(h)?h:[h];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,y,b);let _=b.map(w=>{if(w.rank!=null)return w;let{dataId:k,shape:$,dtype:T}=w;return this.makeTensorFromDataId(k,$,T)});if(c){let w=this.getTensorsForGradient(n,t,_);if(w==null){i==null&&(i=[]);let k=_.filter(($,T)=>i[T]);w=(a||[]).slice().concat(k)}u=this.saveTensorsForBackwardMode(w)}return _};else{if(e==null)throw new Error(`Error running ${n}: Neither modular kernel nor forward func passed`);let y=b=>{!c||(u=b.map(_=>this.keep(this.clone(_))))};f=()=>{let b=this.backend.numDataIds();h=this.tidy(()=>e(this.backend,y));let _=Array.isArray(h)?h:[h];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(n,b,_),_}}let g;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?l=f():(g=this.profiler.profileKernel(n,t,()=>f()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(g),l=g.outputs)}),c&&this.addTapeNode(n,t,l,o,u,s),this.state.profiling&&this.state.activeProfile.kernels.push({name:n,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(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Bh(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(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 l=o.filter((u,c)=>a[c]);return i.concat(l)}return null}makeTensor(e,t,o,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");o=o||"float32",n=n||this.backend;let s=e;o==="string"&&ss(e[0])&&(s=e.map(l=>rl(l)));let a=n.write(s,t,o),i=new R(t,o,a,this.nextTensorId());if(this.incRef(i,n),o==="string"){let l=this.state.tensorInfo.get(a),u=fb(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||"float32";let s=new R(t,o,e,this.nextTensorId());return this.incRef(s,n),s}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new ol(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}incRef(e,t){let o=this.state.tensorInfo.has(e.dataId)?this.state.tensorInfo.get(e.dataId).refCount:0;if(this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++,o===0){this.state.numDataBuffers++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*mb(e.dtype)),this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.state.numBytes+=n}this.state.tensorInfo.get(e.dataId).refCount++,e instanceof ol||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,o=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of 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Am=N({ceil_:SW});function TW(r,e,t){let o=v(r,"x","clipByValue");E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let n={x:o},s={clipValueMin:e,clipValueMax:t};return A.runKernel(Oo,n,s)}var ir=N({clipByValue_:TW});function EW(r){return Je(r,0)}var a_=N({concat1d_:EW});function AW(r,e){return Je(r,e)}var l_=N({concat2d_:AW});function DW(r,e){return Je(r,e)}var u_=N({concat3d_:DW});function $W(r,e){return Je(r,e)}var c_=N({concat4d_:$W});function RW(r,e,t,o,n="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]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&E(it(o),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let p=n==="NHWC"?u.shape[3]:u.shape[1];E(p===l.shape[2],()=>`Error in conv2d: depth 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p={x:u,filter:l},m={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=A.runKernel(tn,p,m);return c?z(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Lo=N({depthwiseConv2d_:qW});function HW(r){let t={x:v(r,"x","diag")};return A.runKernel(ru,t)}var KW=N({diag_:HW});function XW(r,e,t,o,n=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(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:o,dilations:n},m=A.runKernel(la,c,p);return u?z(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Rm=N({dilation2d_:XW});function YW(r,e){let t=r.length,o=[];for(let n=0;n<t;n++){let 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n=v(r,"labels","absoluteDifference"),s=v(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=v(t,"weights","absoluteDifference")),Ge(n.shape,s.shape,"Error in absoluteDifference: ");let i=Et(pe(n,s));return Tr(i,a,o)}var TS=N({absoluteDifference_:Dj});function $j(r,e,t,o,n=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"labels","cosineDistance"),a=v(e,"predictions","cosineDistance"),i=null;o!=null&&(i=v(o,"weights","cosineDistance")),Ge(s.shape,a.shape,"Error in cosineDistance: ");let l=ce(1),u=pe(l,be(O(s,a),t,!0));return Tr(u,i,n)}var ES=N({cosineDistance_:$j});function Rj(r,e,t,o=Ut.SUM_BY_NONZERO_WEIGHTS){let n=v(r,"labels","hingeLoss"),s=v(e,"predictions","hingeLoss"),a=null;t!=null&&(a=v(t,"weights","hingeLoss")),Ge(n.shape,s.shape,"Error in hingeLoss: ");let i=ce(1);n=pe(O(ce(2),n),i);let l=Sr(pe(i,O(n,s)));return Tr(l,a,o)}var AS=N({hingeLoss_:Rj});function Fj(r,e,t,o=1,n=Ut.SUM_BY_NONZERO_WEIGHTS){let 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t=v(r,"labels","sigmoidCrossEntropyWithLogits"),o=v(e,"logits","sigmoidCrossEntropyWithLogits");Ge(t.shape,o.shape,"Error in sigmoidCrossEntropyWithLogits: ");let n=Sr(o),s=O(o,t),a=Du(Qt(He(Et(o))));return ee(pe(n,s),a)}function Lj(r,e,t,o=0,n=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")),Ge(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),o>0){let u=ce(o),c=ce(1),p=ce(.5);s=ee(O(s,pe(c,u)),O(p,u))}let l=Mj(s,a);return Tr(l,i,n)}var FS=N({sigmoidCrossEntropy_:Lj});function zj(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 ${t}`);return Zr((n,s,a)=>{let l=zm(s,[t],!0),u=pe(ne(s,"float32"),l);a([n,u]);let c=He(O(u,n));return{value:be(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,y=Bn(f.shape,[t]);return[O(z(f,y),pe(ne(h,"float32"),Qt(g))),O(z(f,y),pe(Qt(g),ne(h,"float32")))]}}})(r,e)}function Bj(r,e,t,o=0,n=Ut.SUM_BY_NONZERO_WEIGHTS){let s=v(r,"onehotLabels","softmaxCrossEntropy"),a=v(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=v(t,"weights","softmaxCrossEntropy")),Ge(s.shape,a.shape,"Error in softmaxCrossEntropy: "),o>0){let u=ce(o),c=ce(1),p=ce(s.shape[1]);s=ee(O(s,pe(c,u)),fe(u,p))}let l=zj(s,a);return Tr(l,i,n)}var OS=N({softmaxCrossEntropy_:Bj});var Vj={fft:Fa,ifft:Li,rfft:Oa,irfft:Wu},Wj={hammingWindow:pS,hannWindow:mg,frame:fg,stft:mS},As={flipLeftRight:dS,resizeNearestNeighbor:hg,resizeBilinear:dg,rotateWithOffset:hS,cropAndResize:fS,nonMaxSuppression:gS,nonMaxSuppressionAsync:bS,nonMaxSuppressionWithScore:_S,nonMaxSuppressionWithScoreAsync:wS,nonMaxSuppressionPadded:vS,nonMaxSuppressionPaddedAsync:kS},sw={bandPart:CS,gramSchmidt:IS,qr:SS},Gj={absoluteDifference:TS,computeWeightedLoss:Tr,cosineDistance:ES,hingeLoss:AS,huberLoss:DS,logLoss:$S,meanSquaredError:RS,sigmoidCrossEntropy:FS,softmaxCrossEntropy:OS};var Mr=class extends tg{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ae(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return sg(e,t)}dispose(){this.iterations_!=null&&Ae(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Mr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var rp=class extends Mr{constructor(e,t,o=null){super();this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=A.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=A.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:V(()=>Ne(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:V(()=>Ne(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;V(()=>{let c=ee(O(l,this.rho),O(Me(i),1-this.rho)),p=O(fe(_t(ee(u,this.epsilon)),_t(ee(l,this.epsilon))),i),m=ee(O(u,this.rho),O(Me(p),1-this.rho));l.assign(c),u.assign(m);let f=ee(O(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};rp.className="Adadelta";oo(rp);var op=class extends Mr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=A.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:V(()=>Na(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;V(()=>{let l=ee(i,Me(a));i.assign(l);let u=ee(O(fe(a,_t(ee(l,A.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ae(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.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";oo(op);var np=class extends Mr{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ce(t).variable(),this.accBeta2=ce(o).variable()}),n==null&&(this.epsilon=A.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=pe(1,this.accBeta1),n=pe(1,this.accBeta2);t.forEach((s,a)=>{let i=A.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Ne(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Ne(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(O(c,this.beta1),O(u,1-this.beta1)),f=ee(O(p,this.beta2),O(Me(u),1-this.beta2)),d=fe(m,o),h=fe(f,n);c.assign(m),p.assign(f);let g=ee(O(fe(d,ee(_t(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(O(this.accBeta1,this.beta1)),this.accBeta2.assign(O(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(_r(this.beta1,this.iterations_+1)),this.accBeta2.assign(_r(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}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)}};np.className="Adam";oo(np);var sp=class extends Mr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=ce(0).variable(),this.accBeta1=ce(t).variable()}),n==null&&(this.epsilon=A.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=pe(1,this.accBeta1),n=fe(-this.learningRate,ee(O(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=A.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ne(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ne(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(O(c,this.beta1),O(u,1-this.beta1)),f=O(p,this.beta2),d=Et(u),h=Ir(f,d);c.assign(m),p.assign(h);let g=ee(O(fe(n,o),fe(m,ee(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(ee(this.iteration,1)),this.accBeta1.assign(O(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};sp.className="Adamax";oo(sp);var cl=class extends Mr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=A.registeredVariables[o];V(()=>{let i=ee(O(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Dt(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)}};cl.className="SGD";oo(cl);var ip=class extends cl{constructor(e,t,o=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ce(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=A.registeredVariables[o];if(this.accumulations[n]==null){let l=!1;this.accumulations[n]={originalName:`${o}/momentum`,variable:V(()=>Ne(s).variable(l))}}let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&V(()=>{let l,u=ee(O(this.m,a),i);this.useNesterov?l=ee(O(this.c,ee(i,O(u,this.m))),s):l=ee(O(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ae(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};ip.className="Momentum";oo(ip);var ap=class extends Mr{constructor(e,t=.9,o=0,n=null,s=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=A.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=A.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:V(()=>Ne(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:V(()=>Ne(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:V(()=>Ne(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;V(()=>{let c=ee(O(l,this.decay),O(Me(i),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[n].variable,m=ee(O(p,this.decay),O(i,1-this.decay)),f=fe(O(i,this.learningRate),_t(pe(c,ee(Me(m),this.epsilon)))),d=ee(O(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(O(l,this.decay),O(Me(i),1-this.decay)),m=ee(O(u,this.momentum),fe(O(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&&Ae(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ae(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ae(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}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)}};ap.className="RMSProp";oo(ap);var Ma=class{static sgd(e){return new cl(e)}static momentum(e,t,o=!1){return new ip(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new ap(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new np(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new rp(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new sp(e,t,o,n,s)}static adagrad(e,t=.1){return new op(e,t)}};var pl={sgd:Ma.sgd,momentum:Ma.momentum,adadelta:Ma.adadelta,adagrad:Ma.adagrad,rmsprop:Ma.rmsprop,adamax:Ma.adamax,adam:Ma.adam};var Uj=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r())();function rf(){return new Promise(r=>Uj(()=>r()))}var 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Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Lr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return _p(this.weights)}build(e){this.built=!0}getWeights(e=!1){return _f(e?this.trainableWeights:this.weights)}setWeights(e){V(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let _=b.sourceLayer,w=b.nodeIndex,k=b.tensorIndex;this.outputLayers.push(_),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(k)}for(let b of this.inputs){let _=b.sourceLayer,w=b.nodeIndex,k=b.tensorIndex;Vo(w===0,"input layer has >1 nodes"),Vo(k===0,"input layer has >1 tensors"),this.inputLayers.push(_),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let _=this.inputLayers[b];if(!(_ instanceof Wi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${_.getClassName()}.`);this.inputNames.push(_.name),this.feedInputShapes.push(_.batchInputShape),this.feedInputNames.push(_.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,_,w,k,$,T)=>{(k==null||$==null||T==null)&&(k=b.sourceLayer,$=b.nodeIndex,T=b.tensorIndex);let F=k.inboundNodes[$];if(w.indexOf(F)!==-1)throw new Lr(`The tensor ${b.name} at layer "${k.name}" is part of a cycle.`);if(_.indexOf(F)!==-1)return;this.containerNodes.add(Wo.nodeKey(k,$)),k.id in a||(a[k.id]=Object.keys(a).length),w.indexOf(F)===-1&&w.push(F);let M=F.inboundLayers.length;for(let L=0;L<M;L++){let G=F.inputTensors[L],H=F.inboundLayers[L],U=F.nodeIndices[L],Z=F.tensorIndices[L];l(G,_,w,H,U,Z)}for(_.push(F);w.indexOf(F)>=0;)w.splice(w.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){o[b.id]=b,b.id in t||(t[b.id]=0);let _=t[b.id],w=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];_=Math.max(_,w),n[b.outboundLayer.id]=_,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=_;for(let k=0;k<b.inboundLayers.length;k++){let $=b.inboundLayers[k],T=b.nodeIndices[k],F=$.inboundNodes[T],M=t[F.id]==null?0:t[F.id];t[F.id]=Math.max(_+1,M),o[F.id]=F}}let m={};for(let b in t){let _=t[b];_ in m||(m[_]=[]),m[_].push(o[b])}let f={};for(let b in n){let _=n[b];_ in f||(f[_]=[]),f[_].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(nf);this.layers=[];for(let b of d){let _=f[b];_.sort((w,k)=>{let $=a[w.id],T=a[k.id];return $<T?-1:$>T?1:0});for(let w of _)w instanceof Wo&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(nf);let h=this.inputs.slice(),g=[];for(let b of d)for(let _ of m[b]){let w=_.outboundLayer;if(w!=null){for(let k of _.inputTensors)if(h.indexOf(k)===-1)throw new Lr(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${w.name}". The following previous layers were accessed without issue: ${g}`);for(let k of _.outputTensors)h.push(k);g.push(w.name)}}this.nodesByDepth=m;let y=this.layers.map(b=>b.name);for(let b of y){let _=y.filter(w=>w===b).length;if(_!==1)throw new Lr(`The name "${b}" is used ${_} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(y))}this.outboundNodes=[],this.inboundNodes=[],new dl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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(o=>o.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 o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new B(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new B(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new B(`${a.length} of ${n} weights are not set: ${a}`)}wp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${xl}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Vg(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return V(()=>{e=wt(e);let o=new Rs;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return ec(this.outputs,o,t)})}computeMask(e,t){return V(()=>{e=wt(e);let o;return t==null?o=Gn(null,e.length):o=wt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=bp(e);if(t.length!==this.inputLayers.length)throw new B(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(nf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],y=u.nodeIndices[h],b=u.tensorIndices[h],_=`${g.name}_${y}_${b}`,w=o[_];p.push(w)}let m=c.computeOutputShape(hr(p)),f=bp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Vo(l in o),s.push(o[l])}return hr(s)}runInternalGraph(e,t){t==null&&(t=Gn(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(nf);for(let l of n){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 o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,y,b,_;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[w,k]=d[0];h.mask==null&&(h.mask=k),b=wt(p.call(w,h)),_=wt(p.computeMask(w,k)),g=[w],y=[k]}else g=d.map(w=>w[0]),y=d.map(w=>w[1]),h.mask==null&&(h.mask=y),b=wt(p.call(g,h)),_=wt(p.computeMask(g,y));if(p.activityRegularizer)throw new Se("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w<f.length;++w){let k=f[w],$=b[w],T=_[w];o[k.id]=[$,T]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Vo(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Wo?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Wo.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=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 o of this.layers)if(o.name===e)return o;throw new B(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Wo.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];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=Wo.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: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let y=m.inboundLayers[g],b=m.nodeIndices[g],_=m.tensorIndices[g],w=Wo.nodeKey(y,b),k=t[w];k==null&&(k=0),h.push([y.name,k,_,d])}u.push(h)}}}let c={};c.name=a.name,c.className=i,c.config=l,c.inboundNodes=u,o.push(c)}e.layers=o;let n=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],l=this.inputLayersNodeIndices[a],u=Wo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.inputLayersTensorIndices[a];n.push([i.name,c,p])}e.inputLayers=n;let s=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],l=this.outputLayersNodeIndices[a],u=Wo.nodeKey(i,l);if(!this.containerNodes.has(u))continue;let c=t[u];c==null&&(c=0);let p=this.outputLayersTensorIndices[a];s.push([i.name,c,p])}return e.outputLayers=s,e}static fromConfig(e,t,o={},n=!1){let s={},a={};function i(g,y){g.name in a?a[g.name].push(y):a[g.name]=[y]}function l(g,y){let b=[],_;for(let w of y){let k=w[0],$=w[1],T=w[2];if(_=w[3]==null?{}:w[3],!(k in s)){i(g,y);return}let F=s[k];if(F.inboundNodes.length<=$){i(g,y);return}let M=F.inboundNodes[$];b.push(M.outputTensors[T])}b.length>0&&g.apply(hr(b),_)}function u(g){let y=g.name,b=eo(g,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(n),s[y]=b,g.inboundNodes.forEach(w=>{if(!(w instanceof Array))throw new B(`Corrupted configuration, expected array for nodeData: ${w}`);i(b,w)})}let c=t.name,p=t.layers;for(let g of p)u(g);for(;!$T(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 _ of b)l(y,_)}}let m=[],f=[],d=t.inputLayers;for(let g of d){let y=g[0],b=g[1],_=g[2];Vo(y in s);let k=s[y].inboundNodes[b].outputTensors;m.push(k[_])}let h=t.outputLayers;for(let g of h){let y=g[0],b=g[1],_=g[2];Vo(y in s);let k=s[y].inboundNodes[b].outputTensors;f.push(k[_])}return new e({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new B("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function OH(r,e,t){let o=e.length;if(r==null||Array.isArray(r)&&r.length===0)return e.map(n=>null);if(o===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&e[0]in r?[r[e[0]]]:[r];if(Array.isArray(r)){if(r.length!==o)throw new Error(`Provided ${t} is an array of ${r.length} element(s), but the model has ${o} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let n=[];return e.forEach(s=>{s in r?n.push(r[s]):n.push(null)}),n}else throw new Error(`The model has multiple (${o}) outputs, so ${t} must be either an array with ${o} elements or an object with ${e} keys. Provided ${t} not understood: ${JSON.stringify(r)}`)}function Wg(r,e){return OH(r,e,"classWeight")}async function Gg(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Ae(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);a.push(t[i])}),Gt(a,"float32")}else return null}function h1(r,e){return O(r,e)}var PH=32;function x1(r,e){let t,o,n=e;t=n.xs,o=n.ys,x.assert(t!=null&&o!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${e}`);let s=g1("input",r.inputNames,t),a=g1("output",r.outputNames,o),i=s[0].shape[0];x.assert(s.length===r.inputs.length,()=>`LayersModel has ${r.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(r.inputNames)})`),x.assert(a.length===r.outputs.length,()=>`LayersModel has ${r.outputs.length} outputs, but the dataset provides ${a.length} outputs.  (Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l<s.length;l++)x.assert(s[l].shape[0]===i,()=>`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected  ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l<a.length;l++)x.assert(a[l].shape[0]===i,()=>`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected  ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function g1(r,e,t){if(t instanceof R)return[t];if(Array.isArray(t))return x.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let o=[];for(let n of e){if(t[n]==null)throw new B(`The feature data generated by the dataset lacks the required ${r} key '${n}'.`);o.push(t[n])}return o}}function MH(r){if(r.length===3)throw new Se("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function b1(r,e,t){let o=t.batchesPerEpoch!=null;if(x.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),x.assert(t!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),x.assert(t.epochs!=null&&t.epochs>0&&Number.isInteger(t.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${t.epochs}`),x.assert(!o||t.batchesPerEpoch>0&&Number.isInteger(t.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${t.batchesPerEpoch}`),x.assert(t.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(y1(t.validationData))x.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=MH(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Fg(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=Og(c,p,t.epochs,null,null,LH(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let y=0,b=0;for(o||(h=await e.iterator());o?y<t.batchesPerEpoch:!0;){let _=await h.next();if(o&&_.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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t=[],o,n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:n[u]});let i=new Rs(a),l=ec(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let c=this.lossFunctions[u],p=bt(c(s[u],l[u]));u===0?o=p:o=ee(o,p),t.push(o)}for(let u=0;u<this.metricsTensors.length;++u){let c=this.metricsTensors[u][0],p=this.metricsTensors[u][1],m=bt(c(s[p],l[p]));t.push(m)}return t})}async fit(e,t,o={}){return w1(this,e,t,o)}async fitDataset(e,t){return b1(this,e,t)}async trainOnBatch(e,t){let o=await this.standardizeUserData(e,t),n=o[0],s=o[1],i=this.makeTrainFunction()(n.concat(s)),l=[];for(let u of i){let c=await u.data();l.push(c[0])}return Ae(i),hr(l)}getNamedWeights(e){let t=[],o=e!=null&&e.trainableOnly,n=o?this.trainableWeights:this.weights,s=this.getWeights(o);for(let a=0;a<n.length;++a)o&&!n[a].trainable||t.push({name:n[a].originalName,tensor:s[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=qc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-qc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Un(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Un(t))}else{let t=Object.keys(this.loss);e={};let o=this.loss;for(let n of t)if(typeof o[n]=="string")e[n]=Un(o[n]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Un(Sf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Un(Sf(e)));{let e={};for(let t in this.metrics)e[t]=Un(Sf(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Qu(e.optimizer_config),o=eo(t),n;if(typeof e.loss=="string")n=La(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(a=>La(a));else if(e.loss!=null){n={};for(let a in e.loss)n[a]=La(e.loss[a])}let s;if(Array.isArray(e.metrics))s=e.metrics.map(a=>La(a));else if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=La(e.metrics[a])}this.compile({loss:n,metrics:s,optimizer:o})}async save(e,t){if(typeof e=="string"){let u=Cr.getSaveHandlers(e);if(u.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new B(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let o=await Cr.encodeWeights(this.getNamedWeights(t)),n=!1,s=null,i={modelTopology:this.toJSON(s,n),format:jH,generatedBy:`TensorFlow.js tfjs-layers v${xl}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:c,specs:p}=await Cr.encodeWeights(await this.optimizer.getWeights(),u);o.specs.push(...p),o.data=Cr.concatenateArrayBuffers([o.data,c])}if(this.userDefinedMetadata!=null){let u=!0;Sw(this.userDefinedMetadata,this.name,u),i.userDefinedMetadata=this.userDefinedMetadata}return i.weightData=o.data,i.weightSpecs=o.specs,e.save(i)}setUserDefinedMetadata(e){Sw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Io.className="Model";te.registerClass(Io);var Dw=class extends Io{};Dw.className="Functional";te.registerClass(Dw);async function I1(r,e){"modelTopology"in r||(r={modelTopology:r}),r=r;let t=r.modelTopology;t.model_config!=null&&(t=t.model_config);let o=Qu(t),n=eo(o,e);if(r.weightsManifest!=null){let s=await Cr.loadWeights(r.weightsManifest,r.pathPrefix,n.weights.map(i=>i.originalName)),a={};for(let i of n.weights)a[i.originalName]=s[i.originalName];n.loadWeights(a),Ae(s)}return n}async function N1(r,e){if(e==null&&(e={}),typeof r=="string"){let t=Cr.getLoadHandlers(r,e);if(t.length===0)t.push(Cr.browserHTTPRequest(r,e));else if(t.length>1)throw new B(`Found more than one (${t.length}) load handlers for URL '${r}'`);r=t[0]}return qH(r,void 0,e)}async function qH(r,e,t){if(t==null&&(t={}),r.load==null)throw new B("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let o=await r.load(),n=o.modelTopology;n.model_config!=null&&(n=n.model_config);let s=t.strict==null?!0:t.strict,a=o.weightData!=null&&o.weightSpecs!=null&&s,i=eo(Qu(n),e,a),l=o.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),o.userDefinedMetadata!=null&&i.setUserDefinedMetadata(o.userDefinedMetadata),o.weightData!=null){if(o.weightSpecs==null)throw new B("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=HH(o.weightData,o.weightSpecs);i.loadWeights(u,s),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),Ae(u),Ae(c.map(p=>p.tensor))}return i}function HH(r,e){let t=Cr.decodeWeights(r,e),o={},n=[];return e.forEach(s=>{s.group==="optimizer"?n.push({name:s.name,tensor:t[s.name]}):o[s.name]=t[s.name]}),{modelWeights:o,optimizerWeights:n}}var ji=class extends Io{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:fl("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(o=>o<0))throw new B(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof ji||e instanceof Io,o;if(t){if(o=e,o.outputs.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(o.inputs.length!==1)throw new B("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new B("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=Eg({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=o.outputs,this.inputs=o.inputs;else{if(e.inboundNodes.length!==1)throw new B(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new B("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=kw(this.outputs[0])}this.inboundNodes=[],new dl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Gn(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{let n=e.apply(this.outputs[0]);if(Array.isArray(n))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[n],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(Qe(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Io({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,o=console.log){this.built||this.build(),super.summary(e,t,o)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,o={}){if(!this.built)throw new Lr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,o)}async evaluateDataset(e,t){if(!this.built)throw new Lr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,o={}){if(!this.built)throw new Lr("The model needs to be compiled before being used.");return this.model.fit(e,t,o)}async fitDataset(e,t){if(!this.built)throw new Lr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,o={},n=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new B("Legacy serialization format not supported yet.");s=t}else x.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof ji))throw new Se(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let l of s){let c=eo(l,void 0,n);n&&c.setFastWeightInitDuringBuild(!0),i.add(c)}return i}set stopTraining(e){if(this.model==null)throw new B("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new B("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let o={};o.className=t.getClassName(),o.config=t.getConfig(),e.push(o)}return{name:this.name,layers:e}}};ji.className="Sequential";te.registerClass(ji);function S1(r){return new Io(r)}function T1(r){return new ji(r)}function E1(r,e){return e==null&&(e={}),N1(r,e)}function Hg(r){return Eg(r)}function A1(r,e){ao.registerCallbackConstructor(r,e)}var lo=class extends te.Serializable{getConfig(){return{}}},$w=class extends lo{apply(e,t=1){return YT(e,t)}};$w.className="elu";te.registerClass($w);var Rw=class extends lo{apply(e){return zu(e)}};Rw.className="selu";te.registerClass(Rw);var Fw=class extends lo{apply(e){return Sr(e)}};Fw.className="relu";te.registerClass(Fw);var Ow=class extends lo{apply(e){return V(()=>Bo(6,Sr(e)))}};Ow.className="relu6";te.registerClass(Ow);var Pw=class extends lo{apply(e){return e}};Pw.className="linear";te.registerClass(Pw);var Mw=class extends lo{apply(e){return Kr(e)}};Mw.className="sigmoid";te.registerClass(Mw);var Lw=class extends lo{apply(e){return JT(e)}};Lw.className="hardSigmoid";te.registerClass(Lw);var zw=class extends lo{apply(e){return Ss(e)}};zw.className="softplus";te.registerClass(zw);var Bw=class extends lo{apply(e){return ZT(e)}};Bw.className="softsign";te.registerClass(Bw);var Vw=class extends lo{apply(e){return Pi(e)}};Vw.className="tanh";te.registerClass(Vw);var Tf=class extends lo{apply(e,t=-1){return Ra(e,t)}};Tf.className="softmax";te.registerClass(Tf);var Ww=class extends lo{apply(e,t=-1){return $u(e,t)}};Ww.className="logSoftmax";te.registerClass(Ww);var Gw=class extends lo{apply(e,t=1){return V(()=>Kr(e.mul(t)).mul(e))}};Gw.className="swish";te.registerClass(Gw);function Fs(r){return r.getClassName()}function Uw(r,e={}){return Bi(r,te.SerializationMap.getMap().classNameMap,e,"activation")}function Os(r){if(r==null){let e={};return e.className="linear",e.config={},Uw(e)}if(typeof r=="string"){let e={};return e.className=r,e.config={},Uw(e)}else return r instanceof lo?r:Uw(r)}function jw(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var qw=class extends te.Serializable{},tc=class extends qw{constructor(e){super();jw(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=pt([1]);return this.hasL1&&(t=ee(t,be(O(this.l1,Et(e))))),this.hasL2&&(t=ee(t,be(O(this.l2,Yu(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};tc.className="L1L2";te.registerClass(tc);function D1(r){return jw(r),new tc({l1:r!=null?r.l1:null,l2:0})}function $1(r){return jw(r),new tc({l2:r!=null?r.l2:null,l1:0})}var R1={l1l2:"L1L2"};function ut(r){return lp(r)}function F1(r,e={}){return Bi(r,te.SerializationMap.getMap().classNameMap,e,"regularizer")}function vt(r){if(r==null)return null;if(typeof r=="string"){let t={className:r in R1?R1[r]:r,config:{}};return F1(t)}else return r instanceof qw?r:F1(r)}var Ef=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 o=Sr(e);return this.maxValue!=null&&(o=ir(o,0,this.maxValue)),o}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Ef.className="ReLU";te.registerClass(Ef);var Af=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 o=Oe(e);return Sa(o,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Af.className="LeakyReLU";te.registerClass(Af);var Df=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=vt(e.alphaRegularizer),this.alphaConstraint=Lt(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 ${e.sharedAxes}`)}build(e){e=Qe(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let o={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)o[n]=e[n];this.inputSpec=[new At({ndim:e.length,axes:o})],this.built=!0}call(e,t){return e=Oe(e),$a(e,this.alpha.read())}getConfig(){let e={alphaInitializer:St(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Mt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Df.className="PReLU";te.registerClass(Df);var $f=class extends Le{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Se(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let o=Oe(e);return Is(o)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$f.className="ELU";te.registerClass($f);var Rf=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 o=Oe(e);return o.mul(za(o.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Rf.className="ThresholdedReLU";te.registerClass(Rf);var Ff=class extends Le{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Tf().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let o=Oe(e);return this.softmax(o,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ff.className="Softmax";te.registerClass(Ff);function bl(r,e,t){if(typeof r=="number")return Gn(r,e);if(r.length!==e)throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${r.length} elements.`);for(let o=0;o<e;++o){let n=r[o];if(!jT(n))throw new B(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${JSON.stringify(r)} including a non-integer number ${n}`)}return r}function uo(r,e,t,o,n=1){if(r==null)return r;let s=e+(e-1)*(n-1),a;return t==="same"?a=r:a=r-s+1,Math.floor((a+o-1)/o)}function Of(r,e,t,o){if(r==null)return null;if(o==="valid")r=r*e+$s([t-e,0]);else if(o==="same")r=r*e;else throw new B(`Unsupport padding mode: ${o}.`);return r}function Pf(r,e){return V(()=>(Rt(e),e==="channelsFirst"?qe(r,[0,2,3,1]):r))}function Hw(r,e){return V(()=>(Rt(e),e==="channelsFirst"?qe(r,[0,2,3,4,1]):r))}function KH(r,e,t,o=1,n="valid",s,a=1){return V(()=>{if(s==null&&(s=Jr()),Rt(s),r.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=qe(r,[0,2,1])),n==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Nu(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=so(i,t)),i})}function O1(r,e,t,o=[1,1],n="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Jr()),Rt(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=Pf(r,s);if(n==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Vn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=qe(l,[0,3,1,2])),l})}function XH(r,e,t,o=[1,1,1],n="valid",s,a){return V(()=>{if(s==null&&(s=Jr()),Rt(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 ${r.rank}.`);let i=Hw(r,s);if(n==="causal")throw new Se("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Dm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=so(i,t)),s==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var Np=class extends Le{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Np.verifyArgs(t),this.rank=e,jt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Se(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=bl(t.kernelSize,e,"kernelSize"),this.strides=bl(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,Rt(this.dataFormat),this.activation=Os(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Lt(t.biasConstraint),this.biasRegularizer=vt(t.biasRegularizer),this.activityRegularizer=vt(t.activityRegularizer),this.dilationRate=bl(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(Vo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!bg(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:Fs(this.activation),useBias:this.useBias,biasInitializer:St(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Mt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},rc=class extends Np{constructor(e,t){super(e,t);this.kernel=null,rc.verifyArgs(t),this.filters=t.filters,jt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Lt(t.kernelConstraint),this.kernelRegularizer=vt(t.kernelRegularizer)}build(e){e=Qe(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 o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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]:o}}],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o,n=this.bias==null?null:this.bias.read(),s=_g(this.activation.getClassName());if(s!=null&&this.rank===2)o=O1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=KH(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=O1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=XH(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Se("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Qe(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=uo(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:St(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Mt(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)}`)}},_l=class extends rc{constructor(e){super(2,e);_l.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!bg(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)}.`)}};_l.className="Conv2D";te.registerClass(_l);var oc=class extends rc{constructor(e){super(3,e);oc.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)}.`)}};oc.className="Conv3D";te.registerClass(oc);var Mf=class extends _l{constructor(e){super(e);if(this.inputSpec=[new At({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Qe(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new At({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{let o=Oe(e);if(o.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Of(l,m,c,this.padding),h=Of(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=qe(o,[0,2,3,1]));let y=Su(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=qe(y,[0,3,1,2])),this.bias!=null&&(y=so(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=Qe(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=Of(t[n],l,a,this.padding),t[s]=Of(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Mf.className="Conv2DTranspose";te.registerClass(Mf);var Kw=class extends rc{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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=vt(t.depthwiseRegularizer),this.depthwiseConstraint=Lt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=vt(t.pointwiseRegularizer),this.pointwiseConstraint=Lt(t.pointwiseConstraint)}build(e){if(e=Qe(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${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 o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"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 At({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{e=Oe(e);let o;if(this.rank===1)throw new Se("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),o=jm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=so(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=qe(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=St(this.depthwiseInitializer),e.pointwiseInitializer=St(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Mt(this.depthwiseConstraint),e.pointwiseConstraint=Mt(this.pointwiseConstraint),e}};Kw.className="SeparableConv";var Lf=class extends Kw{constructor(e){super(2,e)}};Lf.className="SeparableConv2D";te.registerClass(Lf);var nc=class extends rc{constructor(e){super(1,e);nc.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"&&!bg(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)}.`)}};nc.className="Conv1D";te.registerClass(nc);var zf=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 o=pf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return pf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=pf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return pf(o,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}};zf.className="Cropping2D";te.registerClass(zf);var Bf=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,Rt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,WT(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return V(()=>{let o=Oe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=qe(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return qe(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Bf.className="UpSampling2D";te.registerClass(Bf);function YH(r,e,t=[1,1],o="valid",n,s){return V(()=>{n==null&&(n=Jr()),Rt(n);let a=Pf(r,n);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=Lo(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=qe(a,[0,3,1,2])),a})}var Vf=class extends Np{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=Lt(e.depthwiseConstraint),this.depthwiseRegularizer=vt(e.depthwiseRegularizer)}build(e){if(e=Qe(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 o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*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 o=YH(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=so(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=uo(t,this.kernelSize[0],this.padding,this.strides[0]),a=uo(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=St(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Mt(this.depthwiseRegularizer),e}};Vf.className="DepthwiseConv2D";te.registerClass(Vf);function Xw(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function Yw(r,e,t,o=!1,n,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 ${l}D.`);let u=[1,0].concat(zr(2,l));if(e=qe(e,u),s!=null)throw new Se("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=br(n,-1)),n=qe(n,u)),o&&(e=Yt(e,0),n!=null&&(n=Yt(n,0)));let c=[],p,m=t,f=e.shape[0],d=cr(e),h;n!=null&&(h=cr(n));for(let y=0;y<f;++y){let b=d[y],_=V(()=>r(b,m));if(n==null)p=_[0],m=_[1];else{let w=V(()=>{let k=h[y],$=rr(k).sub(k),T=_[0].mul(k).add(m[0].mul($)),F=m.map((M,L)=>_[1][L].mul(k).add(M.mul($)));return{output:T,newStates:F}});p=w.output,m=w.newStates}i&&c.push(p)}let g;return i&&(g=Wt(c,1)),[p,g,m]})}var co=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 Sp({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 At({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 zr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Tg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)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 Se("Constants support is not implemented in RNN yet.");Tg(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new At({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Se("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 At({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Co("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==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(n=>pt([o,n])):this.states_=[pt([o,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>pt([o,n])):this.states_[0]=pt([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!x.arraysEqual(s.shape,i))throw new B(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>Dt(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Xw(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new At({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof Vr){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 o=t==null?null:t.mask,n=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:n},u=Yw((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);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=Ba(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?kg(t,[1,o]):t):this.cell.stateSize>1?[kg(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 o=this.cell.getConfig();return this.getClassName()===co.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=eo(n,o);return new e(Object.assign(t,{cell:s}))}};co.className="RNN";te.registerClass(co);var wl=class extends Le{},Tp=class extends wl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,jt(this.units,"units"),this.activation=Os(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=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Xu([1,$s([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xu([1,$s([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>rr(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>rr(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Kn(O(e,a),this.kernel.read()):s=Kn(e,this.kernel.read()),this.bias!=null&&(s=so(s,this.bias.read())),i!=null&&(o=O(o,i));let l=ee(s,Kn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Fs(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Tp.className="SimpleRNNCell";te.registerClass(Tp);var Wf=class extends co{constructor(e){e.cell=new Tp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};Wf.className="SimpleRNN";te.registerClass(Wf);var Ep=class extends wl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,jt(this.units,"units"),this.activation=Os(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Os(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=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Xu([1,$s([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xu([1,$s([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Qe(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 o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>rr(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>rr(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let c=Kn(e,this.kernel.read());this.useBias&&(c=so(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=ur(p,[2*this.units,this.units],p.rank-1),d=Kn(n,m),[h,g,y]=ur(c,3,c.rank-1),[b,_]=ur(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,b)),l=this.recurrentActivation.apply(ee(g,_));let w=Kn(O(l,n),f);u=this.activation.apply(ee(y,w));let k=ee(O(i,n),O(ee(1,He(i)),u));return[k,k]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Fs(this.activation),recurrentActivation:Fs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Ep.className="GRUCell";te.registerClass(Ep);var Gf=class extends co{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 Ep(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Gf.className="GRU";te.registerClass(Gf);var vl=class extends wl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,jt(this.units,"units"),this.activation=Os(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Os(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=vt(e.kernelRegularizer),this.recurrentRegularizer=vt(e.recurrentRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.kernelConstraint=Lt(e.kernelConstraint),this.recurrentConstraint=Lt(e.recurrentConstraint),this.biasConstraint=Lt(e.biasConstraint),this.dropout=Xu([1,$s([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xu([1,$s([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=Qe(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends io{apply(l,u){let c=s.apply([a]),p=new Zu().apply([a]),m=s.apply([a*2]);return _w(_w(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let o=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 n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>rr(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>rr(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let m=Kn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,i[0])),m=ee(m,Kn(n,this.recurrentKernel.read())),this.useBias&&(m=so(m,this.bias.read()));let[f,d,h,g]=ur(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=ee(O(u,s),O(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=O(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Fs(this.activation),recurrentActivation:Fs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),recurrentConstraint:Mt(this.recurrentConstraint),biasConstraint:Mt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};vl.className="LSTMCell";te.registerClass(vl);var Uf=class extends co{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 vl(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Uf.className="LSTM";te.registerClass(Uf);var Sp=class extends wl{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 o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Tg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{Ds(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(eo(s,o));return new e({cells:n})}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 o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return _f(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}wp(t)}};Sp.className="StackedRNNCells";te.registerClass(Sp);function Wa(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Ig(e(),t),a=()=>ml(s,e,o);return!n||n<=1?Dt(a().clone()):Array(n).fill(void 0).map(a).map(l=>Dt(l.clone()))}var ZH=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var Zw=class extends co{constructor(e){if(e.unroll)throw new Se("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Se("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new At({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,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,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.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 Co("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[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)Ae(this.states_),this.keptStates!=null&&(Ae(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()):Ae(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=>Dt(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=uo(u,n[0],s,a[0],i[0]),m=uo(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};Zw.className="ConvRNN2D";var Ap=class extends vl{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,jt(this.filters,"filters"),this.kernelSize=bl(o,2,"kernelSize"),this.kernelSize.forEach(l=>jt(l,"kernelSize")),this.strides=bl(n||1,2,"strides"),this.strides.forEach(l=>jt(l,"strides")),this.padding=s||"valid",Qr(this.padding),this.dataFormat=a||"channelsLast",Rt(this.dataFormat),this.dilationRate=bl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>jt(l,"dilationRate"))}build(e){var t;e=Qe(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,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 io{apply(m,f){let d=u.apply([c]),h=Nr([c]),g=u.apply([c*2]);return pp([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 ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Wa({ones:()=>rr(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(ie,ae,ue)=>!ae||!ae[ue]?ie:O(ae[ue],ie),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Wa({ones:()=>rr(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),y=u(s,d,2),b=u(s,d,3),_=3,[w,k,$,T]=ur(this.kernel.read(),i,_),[F,M,L,G]=this.useBias?ur(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,F,this.padding),p=this.inputConv(p,k,M,this.padding),m=this.inputConv(m,$,L,this.padding),f=this.inputConv(f,T,G,this.padding);let[H,U,Z,K]=ur(this.recurrentKernel.read(),i,_);h=this.recurrentConv(h,H),g=this.recurrentConv(g,U),y=this.recurrentConv(y,Z),b=this.recurrentConv(b,K);let X=this.recurrentActivation.apply(ee(c,h)),oe=this.recurrentActivation.apply(ee(p,g)),J=ee(O(oe,a),O(X,this.activation.apply(ee(m,y)))),Q=O(this.recurrentActivation.apply(ee(f,b)),this.activation.apply(J));return[Q,Q,J]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=ZH(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=Xr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?so(s,o,this.dataFormat):s}recurrentConv(e,t){return Xr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Ap.className="ConvLSTM2DCell";te.registerClass(Ap);var jf=class extends Zw{constructor(e){let t=new Ap(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};jf.className="ConvLSTM2D";te.registerClass(jf);var Dp=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,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return ml(()=>Ig(o,this.rate,s,this.seed),()=>o,n)}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()}};Dp.className="Dropout";te.registerClass(Dp);var qf=class extends Dp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};qf.className="SpatialDropout1D";te.registerClass(qf);var Hf=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,jt(this.units,"units"),this.activation=Os(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=Lt(e.kernelConstraint),this.biasConstraint=Lt(e.biasConstraint),this.kernelRegularizer=vt(e.kernelRegularizer),this.biasRegularizer=vt(e.biasRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Qe(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=Qe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=_g(this.activation.getClassName()),s;return n!=null?s=Kn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Kn(o,this.kernel.read()),this.bias!=null&&(s=so(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Fs(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Mt(this.kernelConstraint),biasConstraint:Mt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Hf.className="Dense";te.registerClass(Hf);var Kf=class extends Le{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Qe(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],Hn(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return XT(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Kf.className="Flatten";te.registerClass(Kf);var Xf=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.activation=Os(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Fs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Xf.className="Activation";te.registerClass(Xf);var Yf=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),HT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Yf.className="RepeatVector";te.registerClass(Yf);var Zf=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 o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[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=Hn(e);if(a!==null){if(s===0||i%s!=0)throw new B(o);n[a]=i/s}else if(i!==s)throw new B(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){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 o=Oe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Zf.className="Reshape";te.registerClass(Zf);var Jf=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=zr(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 At({ndim:this.dims.length+1})]}computeOutputShape(e){e=Qe(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),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}};Jf.className="Permute";te.registerClass(Jf);var Qf=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 o=Oe(e),n=-1;return il(vo(o,this.maskValue),n)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e),n=-1,s=!0,a=il(vo(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};Qf.className="Masking";te.registerClass(Qf);var ed=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(wt(e.inputLength))}this.inputDim=e.inputDim,jt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,jt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=vt(e.embeddingsRegularizer),this.activityRegularizer=vt(e.activityRegularizer),this.embeddingsConstraint=Lt(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),vo(e,Ne(e))):null)}computeOutputShape(e){if(e=Qe(e),this.inputLength==null)return[...e,this.outputDim];let t=wt(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+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[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Oe(e);return o.dtype!=="int32"&&(o=za(o,"int32")),Cg(this.embeddings.read(),o.as1D()).reshape(Qe(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:St(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Mt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};ed.className="Embedding";te.registerClass(ed);var kl=class extends Le{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Se}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 o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Qe(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=qn(t),t.length>1)throw new B(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&qn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=$s(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Ba(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(Hn(c.slice(1))));f=qe(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=zr(1,u).concat([0]);o.push(qe(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=qe(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(zr(0,i-1));a=qe(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=qn(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an Array");if(!Array.isArray(e))throw new B("`inputs` should be an Array");if(t.length!==e.length)throw new B(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:br(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=dr(o,t[n]);return o})}},td=class extends kl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return t})}};td.className="Add";te.registerClass(td);var rd=class extends kl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=O(t,e[o]);return t})}};rd.className="Multiply";te.registerClass(rd);var od=class extends kl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return O(1/e.length,t)})}};od.className="Average";te.registerClass(od);var nd=class extends kl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Ir(t,e[o]);return t})}};nd.className="Maximum";te.registerClass(nd);var sd=class extends kl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Bo(t,e[o]);return t})}};sd.className="Minimum";te.registerClass(sd);var id=class extends kl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new B("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(x.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new B("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>pp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new B("A `Concatenate` layer should be called on a list of inputs.");let t=e,o=t[0].slice(),n=this.axis<0?o.length+this.axis:this.axis;for(let s of t.slice(1)){if(o[n]==null||s[n]==null){o[n]=null;break}o[n]+=s[n]}return o}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new B("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new B("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new B(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let o=!0;if(t.forEach(a=>{if(a!=null){o=!1;return}}),o)return null;let n=[];for(let a=0;a<e.length;++a)t[a]==null?n.push(rr(e[a]).asType("bool")):t[a].rank<e[a].rank?n.push(br(t[a],-1)):n.push(t[a]);let s=Je(n,this.axis);return ku(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};id.className="Concatenate";te.registerClass(id);function ad(r,e){for(;r<0;)r+=e;return r}function JH(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Se("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 Se("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return V(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;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;o>n?l=o+n-3:l=o-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 ld=class extends kl{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],o=e[1];if(t.length>3||o.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new B(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>ad(s,e[a].shape.length)):n=[ad(this.axes,t.shape.length),ad(this.axes,o.shape.length)],this.normalize&&(t=wf(t,n[0]),o=wf(o,n[1])),JH(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[ad(this.axes,e.length),ad(this.axes,t.length)],o}computeOutputShape(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].slice(),o=e[1].slice();if(t.length>3||o.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};ld.className="Dot";te.registerClass(ld);var ud=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 o=Oe(e);return ml(()=>mp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};ud.className="GaussianNoise";te.registerClass(ud);var cd=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 o=Oe(e);return this.rate>0&&this.rate<1?ml(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(mp(o.shape,1,s))},()=>o,t.training||!1):o})}};cd.className="GaussianDropout";te.registerClass(cd);var pd=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 o=this._getNoiseShape(e);return ml(()=>{let s=Oe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=Or(Ts(o),this.rate);u=za(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})}};pd.className="AlphaDropout";te.registerClass(pd);function md(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=o_(r,e,t,o,n,s);else if(r.rank===3)a=n_(r,e,t,o,n,s);else if(r.rank===4)a=s_(r,e,t,o,n,s);else throw new Se(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function QH(r,e,t,o,n=.001){return V(()=>{let s=Yc(r,o),a=s.mean,i=s.variance;return[md(r,a,i,t,e,n),a,i]})}function eK(r,e,t,o,n=.001){return V(()=>{let s=Yc(r,o),a=s.mean,i=s.variance,l=[];for(let d of zr(0,r.rank))o.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[md(r,u,c,m,p,n),a,i]})}function tK(r,e,t,o,n=.001){return x.arraysEqual(o.slice().sort(),zr(0,r.rank-1))?QH(r,e,t,o,n):eK(r,e,t,o,n)}var fd=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=Lt(e.betaConstraint),this.gammaConstraint=Lt(e.gammaConstraint),this.betaRegularizer=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer)}build(e){e=Qe(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new At({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training,n=Oe(e),s=n.shape,a=s.length,i=zr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Gn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!x.arraysEqual(c,zr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),_=this.movingVariance.read().reshape(u),w=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return md(n,b,_,w,k,this.epsilon)}else return md(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=tK(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,_,w)=>{V(()=>{let k=1-w,$=b.read(),T=$.sub(_).mul(k);b.write($.sub(T))})};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:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),movingMeanInitializer:St(this.movingMeanInitializer),movingVarianceInitializer:St(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:Mt(this.betaConstraint),gammaConstraint:Mt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};fd.className="BatchNormalization";te.registerClass(fd);var dd=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=vt(e.betaRegularizer),this.gammaRegularizer=vt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Qe(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!==qn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Oe(e),n=o.shape,s=n.length;return V(()=>{let a=!0,{mean:i,variance:l}=Yc(o,this.axis,a),u=Gn(1,s);for(let h of this.axis)u[h]=n[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(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),md(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};dd.className="LayerNormalization";te.registerClass(dd);function rK(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: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let o;return t==="channelsFirst"?o=[[0,0],[0,0],e[0],e[1]]:o=[[0,0],e[0],e[1],[0,0]],Pr(r,o)})}var hd=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Jr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new B(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,o;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],o=[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.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){e=Qe(e);let t,o;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?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(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?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return V(()=>rK(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 Kg(r,e,t,o,n,s){return V(()=>{Rt(n),yw(s),Qr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Jr()),s==null&&(s="max"),r=Pf(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Aa(r,e,t,i):a=ka(r,e,t,i),n==="channelsFirst"&&(a=qe(a,[0,3,1,2])),a})}function P1(r,e,t,o,n,s){return V(()=>{Rt(n),yw(s),Qr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Jr()),s==null&&(s="max"),r=Hw(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Bm(r,e,t,i):a=Em(r,e,t,i),n==="channelsFirst"&&(a=qe(a,[0,4,1,2,3])),a})}var Jw=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(jt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Qr(this.padding),this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){e=Qe(e);let t=uo(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=Ba(Oe(e),2);let o=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ko(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},gd=class extends Jw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Qr(n),Kg(e,t,o,n,s,"max")}};gd.className="MaxPooling1D";te.registerClass(gd);var xd=class extends Jw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Qr(n),Kg(e,t,o,n,s,"avg")}};xd.className="AveragePooling1D";te.registerClass(xd);var Qw=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];jt(this.poolSize,"poolSize"),jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Qr(this.padding),this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=uo(t,this.poolSize[0],this.padding,this.strides[0]),o=uo(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,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 Qw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Qr(n),Kg(e,t,o,n,s,"max")}};yd.className="MaxPooling2D";te.registerClass(yd);var bd=class extends Qw{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Qr(n),Kg(e,t,o,n,s,"avg")}};bd.className="AveragePooling2D";te.registerClass(bd);var ev=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];jt(this.poolSize,"poolSize"),jt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),Qr(this.padding),this.inputSpec=[new At({ndim:5})]}computeOutputShape(e){e=Qe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=uo(t,this.poolSize[0],this.padding,this.strides[0]),o=uo(o,this.poolSize[1],this.padding,this.strides[1]),n=uo(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,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}},_d=class extends ev{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Qr(n),P1(e,t,o,n,s,"max")}};_d.className="MaxPooling3D";te.registerClass(_d);var wd=class extends ev{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return Rt(s),Qr(n),P1(e,t,o,n,s,"avg")}};wd.className="AveragePooling3D";te.registerClass(wd);var tv=class extends Le{constructor(e){super(e);this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Se}},vd=class extends tv{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Oe(e);return bt(o,1)})}};vd.className="GlobalAveragePooling1D";te.registerClass(vd);var kd=class extends tv{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Oe(e);return lr(o,1)})}};kd.className="GlobalMaxPooling1D";te.registerClass(kd);var rv=class extends Le{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Rt(this.dataFormat),this.inputSpec=[new At({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Se}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Cd=class extends rv{call(e,t){return V(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?bt(o,[1,2]):bt(o,[2,3])})}};Cd.className="GlobalAveragePooling2D";te.registerClass(Cd);var Id=class extends rv{call(e,t){return V(()=>{let o=Oe(e);return this.dataFormat==="channelsLast"?lr(o,[1,2]):lr(o,[2,3])})}};Id.className="GlobalMaxPooling2D";te.registerClass(Id);var ov=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,o={}){let n=t.layer,s=eo(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},Nd=class extends ov{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Qe(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=Qe(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return V(()=>(e=Oe(e),Yw((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Nd.className="TimeDistributed";te.registerClass(Nd);function oK(r){Vi(VT,"BidirectionalMergeMode",r)}var nK="concat",Sd=class extends ov{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=eo(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=eo(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?nK:e.mergeMode,oK(this.mergeMode),e.weights)throw new Se("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,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):hr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=Xw(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.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=o,a.push(...o);let c=o.map(p=>new At({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Se("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof Vr;for(let u of a)if(u instanceof Vr!==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 o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=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(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=Yt(s,1));let i;return this.mergeMode==="concat"?i=pp([n,s]):this.mergeMode==="sum"?i=ee(n,s):this.mergeMode==="ave"?i=O(.5,ee(n,s)):this.mergeMode==="mul"?i=O(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ds(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ds(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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pE=(r,e,t)=>{switch(r.op){case"Equal":return[Yr(C("a",r,e,t),C("b",r,e,t))];case"NotEqual":return[vo(C("a",r,e,t),C("b",r,e,t))];case"Greater":return[Xt(C("a",r,e,t),C("b",r,e,t))];case"GreaterEqual":return[Or(C("a",r,e,t),C("b",r,e,t))];case"Less":return[Ta(C("a",r,e,t),C("b",r,e,t))];case"LessEqual":return[no(C("a",r,e,t),C("b",r,e,t))];case"LogicalAnd":return[dr(C("a",r,e,t),C("b",r,e,t))];case"LogicalNot":return[Ea(C("a",r,e,t))];case"LogicalOr":return[Ru(C("a",r,e,t),C("b",r,e,t))];case"Select":case"SelectV2":return[$t(C("condition",r,e,t),C("a",r,e,t),C("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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s=s.slice(0,o),[Je(s,n)]}case"Gather":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[zn(o,ne(n,"int32"),0)]}case"GatherV2":{let o=C("axis",r,e,t),n=C("batchDims",r,e,t),s=C("x",r,e,t),a=C("indices",r,e,t);return[zn(s,ne(a,"int32"),o,n)]}case"Reverse":{let o=C("dims",r,e,t),n=[];for(let a=0;a<o.length;a++)o[a]&&n.push(a);let s=C("x",r,e,t);return[Yt(s,n)]}case"ReverseV2":{let o=C("axis",r,e,t),n=C("x",r,e,t);return[Yt(n,o)]}case"Slice":{let o=C("begin",r,e,t),n=C("size",r,e,t);return[Fe(C("x",r,e,t),o,n)]}case"StridedSlice":{let o=C("begin",r,e,t),n=C("end",r,e,t),s=C("strides",r,e,t),a=C("beginMask",r,e,t),i=C("endMask",r,e,t),l=C("ellipsisMask",r,e,t),u=C("newAxisMask",r,e,t),c=C("shrinkAxisMask",r,e,t),p=C("x",r,e,t);return[Xm(p,o,n,s,a,i,l,u,c)]}case"Pack":return V(()=>{let o=C("axis",r,e,t),n=C("tensors",r,e,t),s=n[0].shape,a=ko(n[0]).shape,i=n.map(l=>{let u=x.arraysEqual(l.shape,s);if(!u&&!x.arraysEqual(ko(l).shape,a))throw new Error("the input tensors shape does not match");return u?l:z(l,s)});return[Wt(i,o)]});case"Unpack":{let o=C("axis",r,e,t),n=C("tensor",r,e,t);return cr(n,o)}case"Tile":{let o=C("reps",r,e,t);return[zo(C("x",r,e,t),o)]}case"Split":case"SplitV":{let o=C("axis",r,e,t),n=C("numOrSizeSplits",r,e,t),s=C("x",r,e,t);return ur(s,n,o)}case"ScatterNd":{let o=C("indices",r,e,t),n=C("values",r,e,t),s=C("shape",r,e,t);return[Z_(o,n,s)]}case"GatherNd":{let o=C("x",r,e,t),n=C("indices",r,e,t);return[J_(o,n)]}case"SparseToDense":{let o=C("sparseIndices",r,e,t),n=C("outputShape",r,e,t),s=C("sparseValues",r,e,t),a=C("defaultValue",r,e,t);return[ef(o,s,n,s.dtype===a.dtype?a:ne(a,s.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var gE=(r,e,t)=>{switch(r.op){case"FFT":return[Fa(C("x",r,e,t))];case"IFFT":return[Li(C("x",r,e,t))];case"RFFT":return[Oa(C("x",r,e,t))];case"IRFFT":return[Wu(C("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var xE=(r,e,t)=>{switch(r.op){case"Cast":return[ne(C("x",r,e,t),C("dtype",r,e,t))];case"ExpandDims":{let o=C("axis",r,e,t);return[br(C("x",r,e,t),o)]}case"Squeeze":{let o=C("axis",r,e,t);return[ko(C("x",r,e,t),o)]}case"Reshape":return[z(C("x",r,e,t),C("shape",r,e,t))];case"MirrorPad":return[Vm(C("x",r,e,t),C("padding",r,e,t),C("mode",r,e,t))];case"PadV2":case"Pad":return[Pr(C("x",r,e,t),C("padding",r,e,t),C("constantValue",r,e,t))];case"SpaceToBatchND":{let o=C("blockShape",r,e,t),n=C("paddings",r,e,t);return[Da(C("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=C("blockShape",r,e,t),n=C("crops",r,e,t);return[Ca(C("x",r,e,t),o,n)]}case"DepthToSpace":{let o=C("blockSize",r,e,t),n=C("dataFormat",r,e,t).toUpperCase();return[$m(C("x",r,e,t),o,n)]}case"BroadcastTo":return[ll(C("x",r,e,t),C("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Fv(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return V(()=>Y1(s,a,i));case"basic_math":return V(()=>Z1(s,a,i));case"control":return rE(s,a,i);case"convolution":return V(()=>nE(s,a,i));case"creation":return V(()=>sE(s,a,i));case"dynamic":return iE(s,a,i);case"evaluation":return V(()=>aE(s,a,i));case"image":return V(()=>cE(s,a,i));case"graph":return V(()=>lE(s,a,i));case"logical":return V(()=>pE(s,a,i));case"matrices":return V(()=>mE(s,a,i));case"normalization":return V(()=>fE(s,a,i));case"reduction":return V(()=>dE(s,a,i));case"slice_join":return V(()=>hE(s,a,i));case"spectral":return V(()=>gE(s,a,i));case"transformation":return V(()=>xE(s,a,i));case"hash_table":return uE(s,a,i,o);case"custom":let l=Jg(s.op);if(l&&l.customExecutor)return l.customExecutor(new Av(s,a,i));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return x.isPromise(n)?n.then(s=>[].concat(s)):[].concat(n)}var px=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let o=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(o))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Pv(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>to(m)[0]),c=[];o!=null&&(c=o.map(m=>to(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((Ov(m)||J6(m)||Q6(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function yE(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>to(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.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)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var e5=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],t5=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],r5=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function Ov(r){return e5.indexOf(r.op)>=0}function J6(r){return t5.indexOf(r.op)>=0}function Q6(r){return r5.indexOf(r.op)>=0}var $p=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new $p(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=Pv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. 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You can use model.execute() instead.");let b=l.filter(_=>!Ov(_)&&!gr(_.name,d,t)).map(_=>_.name);if(b.length>0){let _="";throw p!=null&&(_=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. 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c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=Ps(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!gr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!gr(u,n,o))&&(s[l]=!0,t.push({contexts:o.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 o=e[t],[n]=to(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.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 [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&x.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[o]!=null){let n=this._signature.inputs[o];t[n.name]=e[o]}else t[o]=e[o];return t}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=to(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[o]=to(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var Mv=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}};var o5="?tfjs-format=file",n5="model.json",mx=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Mv}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Cr.browserHTTPRequest(e,this.loadOptions);else{let 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e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,o,n)=>(t[o]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=this.executor.execute(e,t);return o.length>1?o:o[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function bE(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var Fd=class extends Zt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Fd(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(o){throw new Error(`Error thrown while initializing video stream: ${o.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,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[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=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({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,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(x.sizeFromShape(t));return o.set(e,o.length-e.length),Fr(o,t)}};var Od=class extends Zt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Gt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=zi([a,s,l,i],[1,4])}else this.cropBox=zi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().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 o=new Od(e,t);return await o.start(),o}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=Yh.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=e.toFloat().expandDims(0),o;o=As.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return o.reshape(n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var Pd=class{};var yx=class extends Zt{split(e){return new jE(this,e)}},jE=class extends yx{constructor(e,t){super();this.upstream=e,this.impl=new qE(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qE=class extends Fp{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var Uv=class extends Zt{decodeUTF8(){return new KE(this)}},KE=class extends yx{constructor(e){super();this.upstream=e,this.impl=new XE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},XE=class extends Fp{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=HE();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let o;return W().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var Md=class extends Uv{constructor(e,t={}){super();this.file=e,this.options=t,x.assert(e instanceof Uint8Array||(W().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return o(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>o(new Error("Aborted")),s.onerror=i=>o(new Error(i.type));let a=this.file.slice(this.offset,n);s.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function YE(r,e={}){let t,o;typeof r=="string"?t=r:(t=r.url,o=u5(r));let n=await x.fetch(t,o);if(n.ok){let s=new Uint8Array(await n.arrayBuffer());return new Md(s,e)}else throw new Error(n.statusText)}var u5=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function bx(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var Ld=class extends Pd{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(bx(this.input)&&W().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Md(this.input,this.options)}};var zd=class extends Pd{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return bx(this.url)?new Ld(this.url,this.fileOptions).iterator():YE(this.url,this.fileOptions)}};function ZE(r,e={}){return new Rd(new zd(r),e)}function JE(r){let e=Ad(r);return po(async()=>e)}function QE(r){return po(async()=>{let e=await r();return Ad(()=>e.next())})}async function eA(r,e){return Od.create(r,e)}async function tA(r){return Fd.create(r)}var _x="2.8.3";function re(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&x.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var c5=Er.whereImpl,qv=class extends Ws{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ja(this,Cs())}write(e,t,o){this.firstUse&&(this.firstUse=!1,W().get("IS_NODE")&&S.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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O2={kernelName:eu,backendName:"cpu",kernelFunc:uX};function cX(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o;re([n,s],"depthwiseConv2DNativeBackpropInput");let p=x.computeStrides(n.shape),m=x.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),d=new ct(f.inShape,"float32"),h=d.values,[g,y,b]=d.strides,_=t.data.get(n.dataId).values,[w,k,$]=p,T=t.data.get(s.dataId).values,[F,M,L]=m,{batchSize:G,filterHeight:H,filterWidth:U,inChannels:Z,inHeight:K,inWidth:X,outChannels:oe,outHeight:J,outWidth:Q,strideHeight:ie,strideWidth:ae}=f,ue=H-1-f.padInfo.top,le=U-1-f.padInfo.left,ge=oe/Z;for(let we=0;we<G;++we)for(let ye=0;ye<Z;++ye)for(let ke=0;ke<K;++ke){let Ee=ke-ue,Re=Math.max(0,Math.ceil(Ee/ie)),Pe=Math.min(J,(H+Ee)/ie);for(let ze=0;ze<X;++ze){let mt=ze-le,gt=Math.max(0,Math.ceil(mt/ae)),Ct=Math.min(Q,(U+mt)/ae),xt=0;for(let yt=Re;yt<Pe;++yt){let It=yt*ie-Ee;for(let nt=gt;nt<Ct;++nt){let 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        float result;

        if(offset == 0) {
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          result = values[2];
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          result = values[3];
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        ${n.output} = vec4(${i}, 0., 0., 0.);
      }
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              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, ${s}.0);
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              if(offset == 0) {
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                result[${p}] = values[1];
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      ${Bp(e)}

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        ${i}

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      int x;
      int y;
      int z;
      int w;
      int u;
    };

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      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

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      }
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    #define HASHSCALE1 443.8975
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      vec2 p = resultUV * seed;
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vec2 uvFromFlat(int texNumR, int texNumC, int index) {
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}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
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  int texR = texelIndex / texNumC;
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}
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vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
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  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
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}
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  }
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  }
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    }
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      }
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    }
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      }
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      }
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      ivec2 resTexRC = ivec2(resultUV.yx *
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    }
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      int b = index / ${n};
      index -= b * ${n};

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    }
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      return ivec3(r, c, d);
    }
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      ${a}

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

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

      return ivec${r.length}(${i});
    }
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      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${t}
      return ivec4(r, c, d, d2);
    }
  `}function MY(r,e){let t=Ls(["r","c","d","d2","d3"],r);return`
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      int index = resTexRC.x * ${e[1]} + resTexRC.y;

      ${t}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function LY(r,e){let t=Ls(["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 RY(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]}, ${t[1]}));
      }
    `;let o=Math.ceil(r[1]/2);return`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));

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

      return ivec2(r, c);
    }
  `}function zY(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 * ${r[1]};
      return ivec2(r, c);
    }
  `}function cc(r){return`offset${r}`}function kY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=zt();return`
    vec4 ${t}() {
      return ${o.texture2D}(${e}, halfCR);
    }
  `}function gY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
      float ${t}() {
        return sampleTexture(${e}, halfCR);
      }
    `;let[s,a]=r.shapeInfo.texShape,i=cc(e);return`
    float ${t}() {
      vec2 uv = uvFromFlat(${s}, ${a}, ${i});
      return sampleTexture(${e}, uv);
    }
  `}function CY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=zt();return`
    vec4 ${t}(int index) {
      vec2 uv = packedUVfrom1D(
        ${n[0]}, ${n[1]}, index);
      return ${s.texture2D}(${e}, uv);
    }
  `}function xY(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
      float ${t}(int index) {
        ${Wp(r)}
      }
    `;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
      float ${t}(int index) {
        return sampleTexture(${e}, halfCR);
      }
    `;let a=cc(e);return s===1?`
      float ${t}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
        return sampleTexture(${e}, uv);
      }
    `:n===1?`
      float ${t}(int index) {
        vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
        return sampleTexture(${e}, uv);
      }
    `:`
    float ${t}(int index) {
      vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
      return sampleTexture(${e}, uv);
    }
  `}function IY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=zt();if(n!=null&&x.arraysEqual(e,n))return`
      vec4 ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);

        return ${i.texture2D}(${t}, uv);
      }
    `;let l=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],u=Math.ceil(e[1]/2);return`
    vec4 ${o}(int row, int col) {
      vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
      return ${i.texture2D}(${t}, uv);
    }
  `}function yY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&x.arraysEqual(e,n)){let p=n[0],m=n[1];return`
    float ${o}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${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=Gp(r,i),m=["row","col"];return`
      ${Vp(p)}
      float ${o}(int row, int col) {
        return ${o}(${Up(m,a)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
        ${Wp(r)}
      }
    `;let l=n[0],u=n[1],c=cc(t);return u===1?`
    float ${o}(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);
    }
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    float ${o}(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 ${o}(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 NY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Gp(r,p),d=["b","row","col"];return`
        ${tR(f)}
        vec4 ${o}(int b, int row, int col) {
          return ${o}(${Up(d,m)});
        }
      `}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=zt();return`
    vec4 ${o}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${a}, ${i}, ${u}, ${l}, b, row, col);
      return ${c.texture2D}(${t}, uv);
    }
  `}function bY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=x.squeezeShape(e),l=a;if(l.length<e.length){let d=Gp(r,l),h=["row","col","depth"];return`
        ${Vp(d)}
        float ${o}(int row, int col, int depth) {
          return ${o}(${Up(h,i)});
        }
      `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${n}, ${s}, 1)));
        ${Wp(r)}
      }
    `;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
        float ${o}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${s}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${p}.0, ${c}.0);
          return sampleTexture(${t}, uv);
        }
      `;if(p===s&&m==null)return`
    float ${o}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
      return sampleTexture(${t}, uv);
    }
  `;let f=cc(t);return`
      float ${o}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${n} + col * ${s} + depth + ${f};
        vec2 uv = uvFromFlat(${c}, ${p}, index);
        return sampleTexture(${t}, uv);
      }
  `}function SY(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.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=zt();return`
    vec4 ${n}(${p}) {
      int index = ${m};
      int texR = index / ${l};
      int texC = index - texR * ${l};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
      return ${f.texture2D}(${o}, uv);
    }
  `}function _Y(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=x.squeezeShape(e);if(i.length<e.length){let d=Gp(r,i),h=["row","col","depth","depth2"];return`
      ${Vp(d)}
      float ${o}(int row, int col, int depth, int depth2) {
        return ${o}(${Up(h,l)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${a}, ${s}, ${n}, 1)));
        ${Wp(r)}
      }
    `;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${s}, ${n}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${m}.0, ${p}.0);
        return sampleTexture(${t}, uv);
      }
    `;if(m===n&&u==null)return`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${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=cc(t);return`
    float ${o}(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 * ${n} + depth2;
      vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
      return sampleTexture(${t}, uv);
    }
  `}function wY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=x.squeezeShape(e);if(l.length<e.length){let h=Gp(r,l),g=["row","col","depth","depth2","depth3"];return`
      ${Vp(h)}
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        return ${o}(${Up(g,u)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${a}, ${s}, ${n})) +
          depth3;
        ${Wp(r)}
      }
    `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${a}, ${s}, ${n}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${t}, uv);
      }
    `;if(f===n&&c==null)return`
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${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=cc(t);return`
    float ${o}(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 * ${n} + depth3 + ${d};
      vec2 uv = uvFromFlat(${m}, ${f}, index);
      return sampleTexture(${t}, uv);
    }
  `}function vY(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=x.squeezeShape(e);if(n.length<e.length){let g=Gp(r,n),y=["row","col","depth","depth2","depth3","depth4"];return`
      ${Vp(g)}
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${o}(${Up(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 ${o}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${u}, ${l}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${a}, 1)));
        ${Wp(r)}
      }
    `;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${l}, ${i}, ${a})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${f}.0);
        return sampleTexture(${t}, uv);
      }
    `;if(d===a&&p==null)return`
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${e[1]*e[2]*e[3]*e[4]},
               ${e[2]*e[3]*e[4]},
               ${e[3]*e[4]},
               ${e[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${f}.0);
        return sampleTexture(${t}, uv);
      }
    `;let h=cc(t);return`
    float ${o}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${u} + depth * ${l} +
          depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
      vec2 uv = uvFromFlat(${f}, ${d}, index);
      return sampleTexture(${t}, uv);
    }
  `}function Wp(r){let e=r.name,t=x.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
    for (int i = 0; i < ${t}; i++) {
      if (i == index) {
        return ${e}[i];
      }
    }
  `}function TY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=Q$(r.shapeInfo.logicalShape,e.logicalShape),l=Be(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,_)=>`coords.${p[_+u]}`).join(", ");let f="return outputValue;",h=x.sizeFromShape(r.shapeInfo.logicalShape)===1,y=x.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!y)f=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(h&&!y)a===1?f=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:f=`
        return vec4(outputValue.x);
      `;else if(i.length){let b=s-2,_=s-1;i.indexOf(b)>-1&&i.indexOf(_)>-1?f="return vec4(outputValue.x);":i.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(_)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${n}() {
      ${l} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${o}(${m});
      ${f}
    }
  `}function EY(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&x.arraysEqual(a,s))return`
      float ${n}() {
        return sampleTexture(${t}, resultUV);
      }
    `;let u=Be(l),c=Q$(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
    float ${n}() {
      ${u} coords = getOutputCoords();
      ${m}
      return get${o}(${d});
    }
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Please use tf.complex(real, imag).");let n={};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Dr.UPLOAD,refCount:1,complexParentRefCount:0}),n}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,o,n){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. 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Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=x.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...Sl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=W().getBool("WEBGL_PACK")&&n===!0,i=a?Ex(t):t,l=a?new Nk(i):new Ik(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=x.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=x.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=x.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${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 W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:x.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=x.now(),e)}async getQueryTime(e){if(W().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:o,texShape:n,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(n,o),this.textureManager.releaseTexture(t,n,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 W().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Cs().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=t7){let o=this.getCPUBackend();return!W().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. 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Call tf.whereAsync() instead");let t=e.dataSync();return ZY(e.shape,t)}packedUnaryOp(e,t,o){let n=new zs(e.shape,t);return this.compileAndRun(n,[e],o)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=Rx(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Lk,e.dtype);let t=new mo(e.shape,Lk);return this.compileAndRun(t,[e])}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&x.isString(o[0])){let s=o.map(a=>x.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return Cs().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new zk(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Pk(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Tl(e.shape),...El(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Tl(t),...El(t)],a=new th(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Ex(n),i;o?i=new Ck(a):i=new kk(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Nl.DENSE){let h=Sl(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. 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        ${e}
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        float b = getBAtOutCoords();
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      }
    `}};var Al=`
  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 Bs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||x.sizeFromShape(this.outputShape)===1)a=`
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          result.z = 0.;
          result.w = 0.;
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          ${Be(s)} coords = getOutputCoords();
        `,s===1)a+=`
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            result.w = 0.;
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            bool nextRowOutOfBounds =
              (${l[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
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            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);
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        setOutput(result);
      }
    `}};function Ht(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var qR={kernelName:cs,backendName:"webgl",kernelFunc:Ht};function fo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=Ht({inputs:{x:o},backend:t}),l=t.texData.get(i.dataId);l.complexParentRefCount++;let u=Ht({inputs:{x:n},backend:t}),c=t.texData.get(u.dataId);return c.complexParentRefCount++,a.complexTensorInfos={real:i,imag:u},s}var HR={kernelName:Xl,backendName:"webgl",kernelFunc:fo};var Vk="return (a < 0.) ? b * a : a;",Wk=`
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  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:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||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=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new zs(a.shape,e):c=new mo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function at({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&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(_=>{let[w,k]=_,$={dataId:w.dataId,dtype:w.dtype,shape:l.shape},T={dataId:k.dataId,dtype:k.dtype,shape:u.shape},F=new ts(r,l.shape,u.shape);return c.runWebGLProgram(F,[$,T],fr(w.dtype,k.dtype))}),b=fo({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),b}let p=s||fr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(y,p),_=c.texData.get(b.dataId);return _.values=g,b}let m=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Bs(e,l.shape,u.shape,t):f=new ts(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function Dl(r,e=!1){if(r==="linear")return e?VR:MR;if(r==="relu")return e?GR:zR;if(r==="elu")return e?WR:LR;if(r==="relu6")return e?UR:BR;if(r==="prelu")return e?Uk:Gk;if(r==="leakyrelu")return e?Wk:Vk;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var oh=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["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) {
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          ${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 _="rc.x",w="rc.x";e[0]<t[0]?_=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`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 = ${_};
          int batchB = ${w};
          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 jk={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Mx=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,o),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 JR="return a * b;";function qk(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=S.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new Mx(jk.REAL,o.shape,n.shape),c=new Mx(jk.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=fo({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=vR(o.shape,n.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 W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Bs(JR,o.shape,n.shape):a=new ts(JR,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var QR={kernelName:xn,backendName:"webgl",kernelFunc:qk};function eF(r,e,t){let o=[Tl(r.shape),...El(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Tl(e),...El(e)],a=new th(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function me(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=x.sizeFromShape(n.shape),l=x.inferFromImplicitShape(s,i),u=x.sizeFromShape(l);x.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!uc(n.shape,l)&&!(c.texture!==null&&uc(c.shape,l))?eF(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var tF={kernelName:ds,backendName:"webgl",kernelFunc:me};var Lx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%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%o>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 * ${o};

        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);

          ${u}
        }
        setOutput(sumValue);
      }
    `}};var Hk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${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(o/4)*4,p=o%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%o>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 * ${o};

        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 a7(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=S.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function So(r,e,t,o){let n=a7(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new Lx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new Lx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Hk({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var Kk=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Be(this.rank),s=l7(t);this.userCode=`
    void main() {
      ${n} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function l7(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"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var Xk=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Be(this.rank),s=Ok("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
    void main() {
      ${n} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${u};
      if(${l}) {
        result[1] = ${u};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${o[this.rank-2]}) {
        result[2] = ${u};
        if(${l}) {
          result[3] = ${u};
        }
      }
      setOutput(result);
    }
    `}};function $l(r,e,t){let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Xk(r.shape,e):new Kk(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function rF(r,e,t,o){let n=e,s=r.shape.length,a=x.parseAxisParam(n,r.shape),i=a,l=S.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=$l(r,l,o),i=S.getInnerMostAxes(i.length,s)),S.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=S.expandShapeToKeepDim(p,a));let d=x.sizeFromShape(m),g=x.sizeFromShape(r.shape)/d,y=me({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=yu(r.dtype),_=So(y,b,"sum",o),w=me({inputs:{x:_},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(y),o.disposeIntermediateTensorInfo(_),u&&o.disposeIntermediateTensorInfo(c),w}function nh(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return rF(n,s,a,t)}var oF={kernelName:Dn,backendName:"webgl",kernelFunc:nh};function Bt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=jp(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=$l(n,s,a);return u}var nF={kernelName:Pn,backendName:"webgl",kernelFunc:Bt};var Yk=1e3;function pc({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),y=x.sizeFromShape(h),b=x.sizeFromShape(g),_=y===b||y===1||b===1;x.assert(u>=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${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=${o} must match.`);let $=t?[y,p,f]:[y,f,p],T=o?[b,d,m]:[b,m,d],F=me({inputs:{x:r},backend:n,attrs:{shape:$}}),M=me({inputs:{x:e},backend:n,attrs:{shape:T}}),L=[F,M],G=Math.max(y,b),H=t?F.shape[1]:F.shape[2],U=s!=null,Z=a!=null,K=l==="leakyrelu",X=l!=null?Dl(l,!0):null,oe=U||Z||K||X!=null,J;if((f===1||d===1)&&H>Yk&&oe===!1){let ie=F,ae=M;t&&(ie=Bt({inputs:{x:F},backend:n,attrs:{perm:[0,2,1]}}),L.push(ie)),o&&(ae=Bt({inputs:{x:M},backend:n,attrs:{perm:[0,2,1]}}),L.push(ae));let ue=d!==1,le=d===1,ge=ie;ue&&(ge=me({inputs:{x:ie},backend:n,attrs:{shape:[G,H,1]}}),L.push(ge));let we=d===1?2:1,ye=ae;le&&(ye=me({inputs:{x:ae},backend:n,attrs:{shape:[G,1,H]}}),L.push(ye));let ke=qk({inputs:{a:ge,b:ye},backend:n});J=nh({inputs:{x:ke},backend:n,attrs:{axis:we,keepDims:!0}}),L.push(ke)}else{let ie=fr(r.dtype,e.dtype),ae=new oh($,T,[G,f,d],t,o,U,X,Z,K),ue=[F,M];if(s!=null&&ue.push(s),Z&&ue.push(a),K){let le=n.makeTensorInfo([],"float32",x.createScalarValue(i,"float32"));ue.push(le),L.push(le)}J=n.runWebGLProgram(ae,ue,ie)}let Q=me({inputs:{x:J},backend:n,attrs:{shape:k}});L.push(J);for(let ie of L)n.disposeIntermediateTensorInfo(ie);return Q}function u7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return pc({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var sF={kernelName:_s,backendName:"webgl",kernelFunc:u7};var iF="return abs(x);";function c7(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Rx(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new zs(o.shape,iF):n=new mo(o.shape,iF),t.runWebGLProgram(n,[o],o.dtype)}var aF={kernelName:is,backendName:"webgl",kernelFunc:c7};var p7=xr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,m7=Ce({opSnippet:p7}),lF={kernelName:js,backendName:"webgl",kernelFunc:m7};var f7=xr+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,d7=Ce({opSnippet:f7}),uF={kernelName:qs,backendName:"webgl",kernelFunc:d7};var cF="return a + b;",h7=at({opSnippet:cF,packedOpSnippet:cF,supportsComplex:!0,cpuKernelImpl:aR}),pF={kernelName:bo,backendName:"webgl",kernelFunc:h7};var Zk=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${o.join(`
        `)}

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

        vec4 result = ${n};
        setOutput(result);
      }
    `}};function zx(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Ht({inputs:{x:o[0]},backend:t});if(o.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=zx({inputs:o.slice(0,l),backend:t}),c=zx({inputs:o.slice(l),backend:t});return zx({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>fr(l,u)),s=o.map(l=>l.shape),i=W().getBool("WEBGL_PACK")?new Jk(o[0].shape,s):new Zk(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var mF={kernelName:Ho,backendName:"webgl",kernelFunc:zx};function g7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=n;c!=null&&(p=Bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("all",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=So(h,h.dtype,"all",t),y;if(a){let b=S.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 fF={kernelName:Ul,backendName:"webgl",kernelFunc:g7};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=n;c!=null&&(p=Bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,i)),S.assertAxesAreInnerMostDims("any",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=So(h,h.dtype,"any",t),y;if(a){let b=S.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 dF={kernelName:jl,backendName:"webgl",kernelFunc:x7};var Qk=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"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 * ${n};

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

        for (int i = 0; i < ${n}; i++) {
          int inIdx = ${l};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}};var eC=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,x.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.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),n||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"),_=qt("sourceLocA",m-1).concat("inIdx.a"),w=o==="max"?"greaterThan":"lessThan",k=n?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${_.join()})));`,$=`vec4(
            getAChannel(${g.join()}),
            hasNextCol ? getAChannel(${y.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${_.join()}) : 0.)`,T=n?"":`
      float getBestIndicesAChannel(${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()}));
      }
      ${T}
      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 = ${$};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${k}
          vec4 candidate = ${$};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${w}(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 hF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=S.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Qk(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=hF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function gF(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=S.computeOptimalWindowSize(s),i=new eC(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=gF(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Bx(r,e,t,o){let n=[t];if(S.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=S.computeOutAndReduceShapes(e.shape,n),l=x.sizeFromShape(i),u=me({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=hF(r,u,o);s.push(c);let p=me({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return gF(r,e,o)}function y7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=x.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Bx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var xF={kernelName:Ko,backendName:"webgl",kernelFunc:y7};function b7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=x.parseAxisParam(s,n.shape),i=S.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=S.getInnerMostAxes(a.length,l.shape.length)),S.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Bx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var yF={kernelName:oa,backendName:"webgl",kernelFunc:b7};var _7=xr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,w7=Ce({opSnippet:_7}),bF={kernelName:Hs,backendName:"webgl",kernelFunc:w7};var v7=xr+"return log(x + sqrt(x * x + 1.0));",k7=Ce({opSnippet:v7}),_F={kernelName:Ks,backendName:"webgl",kernelFunc:k7};var C7=xr+`
  return atan(x);
`,I7=Ce({opSnippet:C7}),wF={kernelName:Xs,backendName:"webgl",kernelFunc:I7};var N7=YR+`
  return atan(a, b);
`,S7=`
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+ZR+`
  return result;
`,T7=at({opSnippet:N7,packedOpSnippet:S7}),vF={kernelName:Zs,backendName:"webgl",kernelFunc:T7};var E7=xr+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,A7=Ce({opSnippet:E7}),kF={kernelName:Ys,backendName:"webgl",kernelFunc:A7};var Yi=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)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) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let F=">=";this.userCode=`
        const ivec2 strides = ivec2(${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 = ${n?s?g:y:`wR * ${m} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let _="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,$=a%4,T=`
      if (${h}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${_}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${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)
            );

            ${T}
          }

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

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

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

            ${T}
          }
        }
        setOutput(${w});
      }
    `}},mc=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)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 _=t==="avg",w="0.0";if(_||(w="-1.0 / 1e-20"),o){let L=">=";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 ${L} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${n?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 * ${h} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let k="max",$=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&($="avgValue / count");let T=Math.floor(a/4)*4,F=a%4,M=`
      if (${_}) {
        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 = ${w};
      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(${w});
        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 < ${T}; 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)
              );

              ${M}
            }

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

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

              ${M}
            } else if (${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
              );

              ${M}
            }
          }
          setOutput(${$});
        }
      }
    `}};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Xi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;x.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:n},backend:t});let p=new Yi(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var CF={kernelName:Xo,backendName:"webgl",kernelFunc:D7};function $7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=S.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new mc(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var IF={kernelName:na,backendName:"webgl",kernelFunc:$7};var tC=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
      const ivec2 pads = ivec2(${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) / ${n}.0;

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

          for (int wC = 0; wC < ${u};
            wC+= ${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);
      }
    `}},rC=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=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*o*n);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 R7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new rC(m);return t.runWebGLProgram(f,[n],a.dtype)}var NF={kernelName:Hl,backendName:"webgl",kernelFunc:R7};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Xi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=S.computePool2DInfo(a.shape,i,l,1,u),p=new tC(c);return t.runWebGLProgram(p,[n],a.dtype)}var SF={kernelName:ql,backendName:"webgl",kernelFunc:F7};function O7(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return pc({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var TF={kernelName:Yo,backendName:"webgl",kernelFunc:O7};var oC=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(S.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 nC=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(S.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(S.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 P7=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;x.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(i==null||n.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=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=W().getBool("WEBGL_PACK_NORMALIZATION")?new nC(o.shape,n.shape,s.shape,c,p,l):new oC(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},EF={kernelName:an,backendName:"webgl",kernelFunc:P7};var sC=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Be(this.rank),o=`uniform int start[${this.rank}];`,n=M7(this.rank),s,a=e.map((i,l)=>`sourceLoc.${iC[l]} = start[${l}] + coords.${iC[l]};`);s=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${a.join(`
`)}
      `,this.userCode=`
      ${o}
      void main() {
        ${s}
        setOutput(getSource(${n}));
      }
    `}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,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},iC=["x","y","z","w","u","v"];function M7(r){if(r===1)return"sourceLoc";if(r<=6)return iC.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var aC=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Be(this.rank),o=qt("coords",this.rank),n=qt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
      result.x = ${a};
      if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
        ++${n[this.rank-1]};
        result.y = ${a};
        --${n[this.rank-1]};
      }
    `,l=this.rank===1?"":`
      --${o[this.rank-1]};
      if (++${o[this.rank-2]} < ${e[this.rank-2]}) {
        ++${n[this.rank-2]};
        result.z = ${a};
        if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
          ++${n[this.rank-1]};
          result.w = ${a};
        }
      }
    `,u=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${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,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function L7(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.shape=t,a.dtype=r.dtype;let i=sr.computeFlatOffset(e,x.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function qa(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=sr.parseSliceParams(n,s,a);if(sr.assertParamsValid(n,i,l),x.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=SR(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=sr.isSliceContinous(n.shape,i,l);if(u||!c){let p=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aC(l):new sC(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),L7(n,i,l,t)}var AF={kernelName:gs,backendName:"webgl",kernelFunc:qa};var z7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;x.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,_)=>b*_),l=S.getReshaped(n.shape,s,i),u=S.getPermuted(l.length,s.length),c=S.getReshapedPermuted(n.shape,s,i),p=S.getSliceBeginCoords(a,s.length),m=S.getSliceSize(c,a,s.length),f=[],d=me({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Bt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=me({inputs:{x:h},backend:t,attrs:{shape:c}}),y=qa({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},DF={kernelName:sa,backendName:"webgl",kernelFunc:z7};function B7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=$x(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var $F={kernelName:Kl,backendName:"webgl",kernelFunc:B7};var V7="return float(a != b);",lC=at({opSnippet:V7,dtype:"bool"}),RF={kernelName:xi,backendName:"webgl",kernelFunc:lC};function Ha(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Ht({inputs:{x:n.complexTensorInfos.real},backend:t})}var FF={kernelName:fu,backendName:"webgl",kernelFunc:Ha};var W7="return float(int(x));";function OF(r,e){let t=new mo(r.shape,W7),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function uC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ht({inputs:{x:n},backend:t});let a=pt(n.shape),i=uC({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=fo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=Ha({inputs:{input:n},backend:t}),i=uC({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!x.hasEncodingLoss(n.dtype,s)){let a=Ht({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return OF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",x.getTypedArrayFromDType("bool",1)),l=lC({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var PF={kernelName:Fo,backendName:"webgl",kernelFunc:uC};var MF="return ceil(x);",G7=Ce({opSnippet:MF,packedOpSnippet:MF,cpuKernelImpl:uR}),LF={kernelName:Js,backendName:"webgl",kernelFunc:G7};var cC=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(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var pC=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(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function U7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;W().getBool("WEBGL_PACK_CLIP")?i=new pC(n.shape):i=new cC(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var zF={kernelName:Oo,backendName:"webgl",kernelFunc:U7};var mC=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 BF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function j7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new mC(o.shape),a=[BF(o,n.complexTensorInfos.real),BF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var VF={kernelName:ia,backendName:"webgl",kernelFunc:j7};var fC=class{constructor(e){this.outputShape=[],this.outputShape=S.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 o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

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

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

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

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

        ${a[n-1]} = ${a[n-1]} - 1;
        if (${a[n-2]} < ${o[n-2]} &&
            ${a[n-1]} < ${o[n-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function Vx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function fc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Ht({inputs:{x:n.complexTensorInfos.imag},backend:t})}var WF={kernelName:iu,backendName:"webgl",kernelFunc:fc};function dc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>Ha({inputs:{input:d},backend:t})),c=r.map(d=>fc({inputs:{input:d},backend:t})),p=dc(u,e,t),m=dc(c,e,t),f=fo({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(o==="string"){let{tensors2D:u,outShape:c}=GF(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=cR(p,c,o,m),d=S.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=dc(r.slice(0,u),e,t),p=dc(r.slice(u),e,t),m=dc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new dC(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=GF(r,e,t),a=new fC(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=me({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function GF(r,e,t){let o=S.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:o}}function hC(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=x.parseAxisParam(n,e[0].shape)[0],a=S.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 S.assertParamsConsistent(l,s),dc(i,s,t)}var UF={kernelName:as,backendName:"webgl",kernelFunc:hC};var sh=class{constructor(e,t=!1,o=null,n=!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,_=g?3:1,w="",k="";o&&(n?w=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:s?w=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:w=`
          float activation(float x) {
            ${o}
          }
        `,k="result = activation(result);");let $=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${w}

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

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

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

      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 xC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=zt(),y=m==="channelsLast",b=y?0:1,_=y?1:2,w="";for(let k=0;k<=1;k++)for(let $=0;$<=1;$++)w+=`
          blockIndex = rc.y + ${$};
          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[_]} && d1 >= 0) {

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

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

        ${w}

        ${g.output} = result;
      }
    `}};function Wx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,y=[],b=(p===1||m===1)&&c>Yk,_=l[2]%2!=0&&!!u.isPacked;if(b||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let w=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=me({inputs:{x:r},backend:o,attrs:{shape:[1,w,t.inChannels]}}),$=me({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),T=pc({a:k,b:$,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=me({inputs:{x:T},backend:o,attrs:{shape:t.outShape}}),y.push(k),y.push($),y.push(T)}else{let w=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,w,t.inChannels],dtype:r.dtype},$=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,x.assert(uc(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let T=me({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});y.push(T);let F=pc({a:k,b:T,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),M=o.texData.get(F.dataId);x.assert(M.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=$,M.shape=t.outShape,g=Ht({inputs:{x:F},backend:o}),g.shape=t.outShape,y.push(F)}for(let w of y)o.disposeIntermediateTensorInfo(w);return g}function Gx({x:r,filter:e,convInfo:t,backend:o,bias:n=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,_=!1,w=[],k=me({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),$=me({inputs:{x:e},backend:o,attrs:{shape:[1,h,x.sizeFromShape(e.shape)/h]}});w.push(k),w.push($);let T=new xC(y,k.shape,t),F=o.runWebGLProgram(T,[k],"float32"),M=me({inputs:{x:F},backend:o,attrs:{shape:[1,y[0],y[1]]}});w.push(F),w.push(M);let L=n!=null,G=s!=null,H=i==="leakyrelu",U=i?Dl(i,!0):null,Z=new oh(M.shape,$.shape,[1,g,t.outChannels],b,_,L,U,G,H),K=[M,$];if(n&&K.push(n),G&&K.push(s),H){let Q=o.makeTensorInfo([],"float32",x.createScalarValue(a,"float32"));K.push(Q),w.push(Q)}let X=o.runWebGLProgram(Z,K,"float32"),oe=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],J=me({inputs:{x:X},backend:o,attrs:{shape:oe}});w.push(X);for(let Q of w)o.disposeIntermediateTensorInfo(Q);return J}function q7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(n.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=Wx({x:n,filter:s,convInfo:m,backend:t});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=Gx({x:n,filter:s,convInfo:m,backend:t});else{let h=new sh(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=me({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var jF={kernelName:Zo,backendName:"webgl",kernelFunc:q7};var yC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=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} - ${n};

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

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${o} - ${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);
      }
    `}},bC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-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) / ${n}.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 < ${o}; 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 = ${o} - 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);
      }
    `}},_C=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=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 * ${o} - ${a};

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

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${n} - ${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);
      }
    `}},wC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-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 < ${o}; 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 = ${o} - 1 - wR;

            for (int wC = 0; wC < ${n}; 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 = ${n} - 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 H7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new yC(m);return t.runWebGLProgram(f,[n,s],"float32")}var qF={kernelName:Yl,backendName:"webgl",kernelFunc:H7};function K7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new bC(m);return t.runWebGLProgram(f,[n,s],"float32")}var HF={kernelName:Jo,backendName:"webgl",kernelFunc:K7};function X7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=S.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new gC(u);return t.runWebGLProgram(c,[n,s],"float32")}var KF={kernelName:aa,backendName:"webgl",kernelFunc:X7};function Y7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=S.computeConv3DInfo(n.shape,l,a,1,i),c=new _C(u);return t.runWebGLProgram(c,[n,s],"float32")}var XF={kernelName:Zl,backendName:"webgl",kernelFunc:Y7};function Z7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=S.computeConv3DInfo(l,s.shape,i,1,a),c=new wC(u);return t.runWebGLProgram(c,[n,s],"float32")}var YF={kernelName:Jl,backendName:"webgl",kernelFunc:Z7};var J7=Px+`
  return cos(x);
`,Q7=Ce({opSnippet:J7}),ZF={kernelName:Qo,backendName:"webgl",kernelFunc:Q7};var eZ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,tZ=Ce({opSnippet:eZ}),JF={kernelName:Qs,backendName:"webgl",kernelFunc:tZ};var vC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="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(${_});
      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 = ${w};

        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 rZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new vC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},QF={kernelName:ei,backendName:"webgl",kernelFunc:rZ};var Ux=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${eO(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
      uniform float index;
      void main() {
        ${Be(n)} coords = getOutputCoords();
        int end = ${tO(n,"coords")};
        float val = ${s};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${l};
          ${tO(n,"coords")} = idx;
          val += getX(${eO(n,"coords")});
        }
        setOutput(val);
      }
    `}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function eO(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 ${r} is not yet supported`)}function tO(r,e){if(r===1)return`${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 ${r} is not yet supported`)}function oZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=S.getAxesPermutation([s],l),c=n;u!=null&&(c=Bt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=S.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=n.shape[p],f=Ht({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Ux(c.shape,!1,i),g=h.getCustomSetupFunc(d),y=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(y)}if(a){let d=new Ux(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=S.getUndoAxesPermutation(u),h=Bt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var rO={kernelName:en,backendName:"webgl",kernelFunc:oZ};function nZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=$x(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=lR(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${n.shape.length}.`)}var oO={kernelName:Ql,backendName:"webgl",kernelFunc:nZ};var kC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 sZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;x.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.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 kC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var nO={kernelName:ti,backendName:"webgl",kernelFunc:sZ};var ih=class{constructor(e,t=!1,o=null,n=!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="";o&&(n?y=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:s?y=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:y=`
          float activation(float x) {
            ${o}
          }
        `,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&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;
        ${_}
        ${b}
        setOutput(result);
      }
    `}};var ah=class{constructor(e,t=!1,o=null,n=!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 $=0;$<h;$++)y+=`
          vec4 xTexelR${k}C${$*2} = vec4(0.);
          vec4 wR${k}C${$} = vec4(0.);
          vec4 xR${k}C${$} = vec4(0.);`;for(let k=0;k<d;k++)for(let $=0;$<g;$++){let T=$*2;if(y+=`
          xR = xRCorner + ${k*m};
          xC = xCCorner + ${T*f};
        `,p===1){if(T<h&&(u%2==1?y+=`
                xCOffset = xC + 1;
                if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
                  xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if(xCOffset + 1 >= ${i}) {
                    xTexelR${k}C${T}.zw = vec2(0.);
                  }
                } else {
                  xTexelR${k}C${T} = 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${k}C${T} = vec4(previous.zw, xTexelR${k}C${T}.xy);
                } else {
                  xR${k}C${T} = vec4(0, 0, xTexelR${k}C${T}.xy);
                }
              `:y+=`
                if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
                  xTexelR${k}C${T} = getX(batch, xR, xC, d1);
                } else {
                  xTexelR${k}C${T} = vec4(0.);
                }

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

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

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

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

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

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

                xR${k}C${T} = vec4(
                  xTexelR${k}C${T}.xy, xTexelR${k}C${T+2}.xy);
              `,T+1<h&&(y+=`
                  xR${k}C${T+1} = vec4(
                    xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
                `)),y+="}");T<h&&(y+=`
            vec4 wTexelR${k}C${T} = getW(${k}, ${T}, d1, q);
            wR${k}C${T} = vec4(wTexelR${k}C${T}.xz, wTexelR${k}C${T}.xz);
          `,T+1<h&&(y+=`
              vec4 wTexelR${k}C${T+1} = getW(${k}, ${T+1}, d1, q);
              wR${k}C${T+1} =
                vec4(wTexelR${k}C${T+1}.xz, wTexelR${k}C${T+1}.xz);`))}for(let k=0;k<d;k++)for(let $=0;$<h;$++)y+=`dotProd += xR${k}C${$} * wR${k}C${$};`;let b="",_="";o&&(n?b=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:s?b=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:b=`vec4 activation(vec4 x) {
          ${o}
        }`,_="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&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 iZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=S.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return W().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new ah(p):m=new ih(p),t.runWebGLProgram(m,[n,s],"float32")}var sO={kernelName:tn,backendName:"webgl",kernelFunc:iZ};var CC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=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} - ${n};

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

            for (int yC = 0; yC < ${e.outWidth}; yC++) {
              int xC = wC + yC * ${o} - ${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);
      }
    `}},IC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-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) / ${n}.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 < ${o}; 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 = ${o} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${l}; dm++) {
              int d2 = d1 * ${l} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function aZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=S.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new CC(p);return t.runWebGLProgram(m,[n,s],"float32")}var iO={kernelName:eu,backendName:"webgl",kernelFunc:aZ};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=S.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new IC(p);return t.runWebGLProgram(m,[n,s],"float32")}var aO={kernelName:tu,backendName:"webgl",kernelFunc:lZ};var NC=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function uZ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=x.sizeFromShape(o.shape),a=me({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new NC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=me({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var lO={kernelName:ru,backendName:"webgl",kernelFunc:uZ};var SC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
      const ivec2 strides = ivec2(${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 < ${o}) {
                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 cZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=S.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new SC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=me({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var uO={kernelName:la,backendName:"webgl",kernelFunc:cZ};var pZ="return (x >= 0.0) ? x : (exp(x) - 1.0);",mZ=`
  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;
`,fZ=Ce({opSnippet:pZ,packedOpSnippet:mZ}),cO={kernelName:ri,backendName:"webgl",kernelFunc:fZ};var dZ="return (b >= 1.0) ? a : a * (b + 1.0);",hZ=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,gZ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bs(hZ,o.shape,n.shape):new ts(dZ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},pO={kernelName:ou,backendName:"webgl",kernelFunc:gZ};var xZ=`
  return vec4(equal(a, b));
`,yZ="return float(a == b);",bZ=at({opSnippet:yZ,packedOpSnippet:xZ,dtype:"bool"}),mO={kernelName:ni,backendName:"webgl",kernelFunc:bZ};var _Z=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${S.ERF_P};
  float a1 = ${S.ERF_A1};
  float a2 = ${S.ERF_A2};
  float a3 = ${S.ERF_A3};
  float a4 = ${S.ERF_A4};
  float a5 = ${S.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));
`,wZ=Ce({opSnippet:_Z}),fO={kernelName:oi,backendName:"webgl",kernelFunc:wZ};var dO="return exp(x);",TC=Ce({opSnippet:dO,packedOpSnippet:dO,cpuKernelImpl:pR}),hO={kernelName:on,backendName:"webgl",kernelFunc:TC};function jx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(x.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),me({inputs:{x:s},backend:o,attrs:{shape:i}})}var gO={kernelName:ls,backendName:"webgl",kernelFunc:jx};var xO="return exp(x) - 1.0;",vZ=Ce({opSnippet:xO,packedOpSnippet:xO,cpuKernelImpl:mR}),yO={kernelName:si,backendName:"webgl",kernelFunc:vZ};var qx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.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(${n});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${n}; 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 Hx(r,e,t){let o=t.texData.get(r.dataId),n=x.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,l=me({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}).shape,u=new qx("real",l,e),c=new qx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=fo({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 kZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Hx(o,!1,t)}var bO={kernelName:nu,backendName:"webgl",kernelFunc:kZ};var EC=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,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function lh(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||x.inferDtype(n),s==="string"){let a=x.getArrayFromDType(s,x.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new EC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var _O={kernelName:ua,backendName:"webgl",kernelFunc:lh};var AC=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 wO={kernelName:ii,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new AC(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var vO="return floor(x);",CZ=Ce({opSnippet:vO,packedOpSnippet:vO,cpuKernelImpl:fR}),kO={kernelName:nn,backendName:"webgl",kernelFunc:CZ};var IZ=`
  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;
  }
`,NZ=`
  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);
`,SZ=at({opSnippet:IZ,packedOpSnippet:NZ,dtype:"int32"}),CO={kernelName:sn,backendName:"webgl",kernelFunc:SZ};var DC=class{constructor(e){this.variableNames=["A"];let t=zt(),[o,n]=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(${n}.0, ${o}.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 $C=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=zt(),[o,n]=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(${n}.0, ${o}.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 IO={kernelName:Pc,backendName:"webgl",kernelFunc:TZ},qp;function TZ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[u,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],p=[c,u],m=[c,u,s];(i||a||l)&&(qp==null&&(qp=document.createElement("canvas").getContext("2d")),qp.canvas.width=u,qp.canvas.height=c,qp.drawImage(n,0,0,u,c),n=qp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Dr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),n);let d=W().getBool("WEBGL_PACK")?new $C(m):new DC(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function EZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=Wx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)y=Gx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let w=a!=null,k=i!=null,$=f==="leakyrelu",T=f?Dl(f,!1):null,F=new sh(g,w,T,k,$),M=[n,s];if(a&&M.push(a),i&&M.push(i),$){let L=t.makeTensorInfo([],"float32",x.createScalarValue(d,"float32"));M.push(L),b.push(L)}y=t.runWebGLProgram(F,M,"float32")}let _=me({inputs:{x:y},backend:t,attrs:{shape:g.outShape}});return b.push(y),b.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var NO={kernelName:ws,backendName:"webgl",kernelFunc:EZ};function AZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),x.assert(S.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=S.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),y=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?Dl(m,y):null,_=[n,s],w=a!=null,k=i!=null,$=m==="leakyrelu";if(w&&_.push(a),k&&_.push(i),$){let M=t.makeTensorInfo([],"float32",x.createScalarValue(f,"float32"));_.push(M),d.push(M)}let T;y?T=new ah(g,w,b,k,$):T=new ih(g,w,b,k,$);let F=t.runWebGLProgram(T,_,"float32");return d.forEach(M=>t.disposeIntermediateTensorInfo(M)),F}var SO={kernelName:vs,backendName:"webgl",kernelFunc:AZ};var RC=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Be(t.length),s=Be(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
        ${n} strides = ${n}(${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 * ${a};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `}};function DZ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=S.prepareAndValidate(o,n),p=me({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=me({inputs:{x:o},backend:t,attrs:{shape:[x.sizeFromShape(o.shape)/u,u]}}),f=new RC(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=me({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var TO={kernelName:ai,backendName:"webgl",kernelFunc:DZ};var FC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Be(this.rank),n=$Z(e,2);this.userCode=`
      void main() {
        ${o} resRC = getOutputCoords();
        setOutput(getA(${n}));
      }
    `}};function $Z(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function RZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=x.parseAxisParam(a,n.shape)[0],u=S.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=x.sizeFromShape(s.shape),p=[],m=me({inputs:{x:n},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([n,s])||n.dtype==="string"){let b=t.bufferSync(f),_=t.bufferSync(m),w=dR(_,b,d);return p.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,w.dtype,w.values)}let h=new FC(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 EO={kernelName:us,backendName:"webgl",kernelFunc:RZ};var FZ="return float(a > b);",OZ=`
  return vec4(greaterThan(a, b));
`,PZ=at({opSnippet:FZ,packedOpSnippet:OZ,cpuKernelImpl:hR,dtype:"bool"}),AO={kernelName:li,backendName:"webgl",kernelFunc:PZ};var MZ="return float(a >= b);",LZ=`
  return vec4(greaterThanEqual(a, b));
`,zZ=at({opSnippet:MZ,packedOpSnippet:LZ,dtype:"bool"}),DO={kernelName:ln,backendName:"webgl",kernelFunc:zZ};function BZ(r){let{inputs:e,backend:t}=r,{input:o}=e;return Hx(o,!0,t)}var $O={kernelName:su,backendName:"webgl",kernelFunc:BZ};var VZ="return float(!isnan(x) && !isinf(x));",WZ=Ce({opSnippet:VZ,dtype:"bool"}),RO={kernelName:ui,backendName:"webgl",kernelFunc:WZ};var GZ="return float(isinf(x));",UZ=Ce({opSnippet:GZ,dtype:"bool"}),FO={kernelName:ci,backendName:"webgl",kernelFunc:UZ};var jZ="return float(isnan(x));",qZ=Ce({opSnippet:jZ,dtype:"bool"}),OO={kernelName:pi,backendName:"webgl",kernelFunc:qZ};var HZ="return float(a < b);",KZ=`
  return vec4(lessThan(a, b));
`,XZ=at({opSnippet:HZ,packedOpSnippet:KZ,cpuKernelImpl:gR,dtype:"bool"}),PO={kernelName:mi,backendName:"webgl",kernelFunc:XZ};var YZ="return float(a <= b);",ZZ=`
  return vec4(lessThanEqual(a, b));
`,JZ=at({opSnippet:YZ,packedOpSnippet:ZZ,dtype:"bool"}),MO={kernelName:fi,backendName:"webgl",kernelFunc:JZ};function QZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=xR(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var LO={kernelName:au,backendName:"webgl",kernelFunc:QZ};var e9=`if (x < 0.0) return NAN;
  return log(x);`,t9=`
  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;
`,r9=Ce({opSnippet:e9,packedOpSnippet:t9,cpuKernelImpl:yR}),zO={kernelName:cn,backendName:"webgl",kernelFunc:r9};var o9="return log(1.0 + x);",n9=Ce({opSnippet:o9}),BO={kernelName:di,backendName:"webgl",kernelFunc:n9};var s9="return float(a >= 1.0 && b >= 1.0);",i9=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,a9=at({opSnippet:s9,packedOpSnippet:i9,dtype:"bool"}),VO={kernelName:hi,backendName:"webgl",kernelFunc:a9};var l9="return float(!(x >= 1.0));",u9=Ce({opSnippet:l9}),WO={kernelName:Qa,backendName:"webgl",kernelFunc:u9};var c9="return float(a >= 1.0 || b >= 1.0);",p9=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,m9=at({opSnippet:c9,packedOpSnippet:p9,dtype:"bool"}),GO={kernelName:el,backendName:"webgl",kernelFunc:m9};var OC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * 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 * ${l};
        setOutput(val);
      }
    `}};var PC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${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 f9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new PC(n.shape,s,a,i,l):new OC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},UO={kernelName:ca,backendName:"webgl",kernelFunc:f9};var MC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,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(${n}) * norm + float(${o});

          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(${n})
                * float(${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 d9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new MC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},jO={kernelName:lu,backendName:"webgl",kernelFunc:d9};function qO(r,e,t,o){let n=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/n,i=me({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=So(i,r.dtype,"max",o),u=me({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function LC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let _=t.texData.get(f.dataId).values,w=new Array(i);for(let T=0;T<w.length;T++)w[T]=n.shape[c[T]];let k=jp(_,n.shape,n.dtype,c,w);f=t.makeTensorInfo(w,n.dtype);let $=t.texData.get(f.dataId);$.values=k}else f=$l(n,c,t);u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("max",u,i);let[d,h]=S.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=S.expandShapeToKeepDim(d,l));let y;if(m){let _=t.texData.get(f.dataId).values,w=bR(_,x.sizeFromShape(h),g,n.dtype);y=t.makeTensorInfo(g,n.dtype);let k=t.texData.get(y.dataId);k.values=w}else y=qO(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),y}var HO={kernelName:pn,backendName:"webgl",kernelFunc:LC};var h9=Ox+`
  return max(a, b);
`,g9=`
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Al+`
  return result;
`,x9=at({opSnippet:h9,packedOpSnippet:g9,cpuKernelImpl:_R}),KO={kernelName:mn,backendName:"webgl",kernelFunc:x9};function y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Xi(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;x.assert(S.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=S.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return Ht({inputs:{x:n},backend:t});let p=new Yi(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var XO={kernelName:fn,backendName:"webgl",kernelFunc:y9};function b9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=S.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new mc(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var YO={kernelName:pa,backendName:"webgl",kernelFunc:b9};var zC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=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 += ${n}) {
          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) / ${o}.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);
      }
    `}},BC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=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) / ${o}.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) / ${n}.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 _9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=S.computePool3DInfo(a.shape,i,l,p,u,c),f=new mc(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new BC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var ZO={kernelName:cu,backendName:"webgl",kernelFunc:_9};function w9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Xi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=S.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Yi(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new zC(m),y=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),y}var JO={kernelName:uu,backendName:"webgl",kernelFunc:w9};function QO(r,e,t,o){let n=new Yi(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Yi(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var eP={kernelName:pu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;x.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];x.assert(S.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=S.computePool2DInfo(o.shape,n,s,u,a),[p,m]=QO(o,i,c,l);return[p,m]}};function tP(r,e,t,o){let n=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/n,i=me({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=So(i,"float32","mean",o),u=me({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var rP={kernelName:dn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=S.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let w=a.texData.get(d.dataId).values,k=new Array(i);for(let F=0;F<k.length;F++)k[F]=o.shape[c[F]];let $=jp(w,o.shape,o.dtype,c,k);d=a.makeTensorInfo(k,o.dtype);let T=a.texData.get(d.dataId);T.values=$}else d=$l(o,c,a);f.push(d),u=S.getInnerMostAxes(u.length,i)}S.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=S.computeOutAndReduceShapes(d.shape,u),y=h;n&&(y=S.expandShapeToKeepDim(h,l));let b=tP(d,g,y,a);for(let _ of f)a.disposeIntermediateTensorInfo(_);return b}};function v9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=S.getAxesPermutation(u,i),p=n;c!=null&&(p=Bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=S.getInnerMostAxes(u.length,n.shape.length)),S.assertAxesAreInnerMostDims("min",u,i);let[m,f]=S.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=me({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=So(h,h.dtype,"min",t),y;if(a){let b=S.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 oP={kernelName:hn,backendName:"webgl",kernelFunc:v9};var k9=Ox+`
  return min(a, b);
`,C9=`
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  `+Al+`
  return result;
`,I9=at({opSnippet:k9,packedOpSnippet:C9,cpuKernelImpl:wR}),nP={kernelName:gn,backendName:"webgl",kernelFunc:I9};var VC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Be(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
        int start = ${a};
        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 < ${n}; 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 WC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Be(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=qt("rc",n),u=qt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===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});
        ${l[n-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${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});
        ${l[n-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${u.join()}), ${p});
        }
        rc = outputLoc;
        ${l[n-2]} += 1;
        if(${l[n-2]} < ${this.outputShape[n-2]}) {
          ${d}
          result[2] = getChannel(getX(${u.join()}), ${p});
          ${l[n-1]} += 1;
          if(${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 N9=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new WC(o.shape,n,s):new VC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},sP={kernelName:ma,backendName:"webgl",kernelFunc:N9};var S9=`if (b == 0.0) return NAN;
  return mod(a, b);`,T9=`
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  `+Al+`
  return result;
`,E9=at({opSnippet:S9,packedOpSnippet:T9}),iP={kernelName:gi,backendName:"webgl",kernelFunc:E9};var GC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],this.userCode=`
      uniform float seed;

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

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

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

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

        // If no other event happened, last event happened.
        setOutput(float(${t-1}));
      }
    `}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var A9=`
if (a == b) {
  return 1.0;
};
return a / b;`,D9=`
  // 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;
`,UC=at({opSnippet:A9,packedOpSnippet:D9,checkOutOfBounds:!0}),aP={kernelName:rn,backendName:"webgl",kernelFunc:UC};var lP="return a - b;",jC=at({opSnippet:lP,packedOpSnippet:lP,supportsComplex:!0,cpuKernelImpl:ER}),uP={kernelName:Fn,backendName:"webgl",kernelFunc:jC};function qC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=x.parseAxisParam([s],n.shape),i=LC({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=S.expandShapeToKeepDim(i.shape,a),u=me({inputs:{x:i},backend:t,attrs:{shape:l}}),c=jC({inputs:{a:n,b:u},backend:t}),p=TC({inputs:{x:c},backend:t}),m=nh({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=me({inputs:{x:m},backend:t,attrs:{shape:l}}),d=UC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var cP={kernelName:$n,backendName:"webgl",kernelFunc:qC};function $9(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:qC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new GC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var pP={kernelName:mu,backendName:"webgl",kernelFunc:$9};var mP="return -x;";function R9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=kR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new zs(o.shape,mP):n=new mo(o.shape,mP),t.runWebGLProgram(n,[o],o.dtype)}var fP={kernelName:ps,backendName:"webgl",kernelFunc:R9};var F9=Er.nonMaxSuppressionV3Impl;function O9(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=F9(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var dP={kernelName:yi,backendName:"webgl",kernelFunc:O9};var P9=Er.nonMaxSuppressionV4Impl;function M9(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=P9(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var hP={kernelName:bi,backendName:"webgl",kernelFunc:M9};var L9=Er.nonMaxSuppressionV5Impl;function z9(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:y}=L9(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var gP={kernelName:_i,backendName:"webgl",kernelFunc:z9};var HC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${n}), float(${o}),
                      float(index == coords.y)));
      }
    `}};var B9=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=x.sizeFromShape(n.shape),u=new HC(l,s,a,i),c=me({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=me({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},xP={kernelName:yn,backendName:"webgl",kernelFunc:B9};function uh(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ha({inputs:{input:o},backend:t}),s=uh({inputs:{x:n},backend:t}),a=fc({inputs:{input:o},backend:t}),i=uh({inputs:{x:a},backend:t}),l=fo({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return lh({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var yP={kernelName:bs,backendName:"webgl",kernelFunc:uh};function bP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ha({inputs:{input:o},backend:t}),s=bP({inputs:{x:n},backend:t}),a=fc({inputs:{input:o},backend:t}),i=uh({inputs:{x:a},backend:t}),l=fo({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return lh({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var _P={kernelName:ms,backendName:"webgl",kernelFunc:bP};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return jx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});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=jx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=hC({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var wP={kernelName:fs,backendName:"webgl",kernelFunc:V9};var KC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Be(n),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,n);if(n===1){this.userCode=`
        int start = ${a};
        int end = ${i};

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(float(${o}));
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${l}));
        }
      }
    `}};var XC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Be(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=qt("rc",n),u=qt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
       if(${c}) {
      `,n===1?"":`}
       rc = outputLoc;
       ${l[n-2]} += 1;
       if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":`  ${l[n-1]} += 1;
         if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h<g;h++)d+=`
        ${m[h]}
        if (${f}) {
          result[${h}] = float(${o});
        } else {
          ${s} source = rc - start;
          result[${h}] = getChannel(getX(${u.join()}), ${p});
        }
      `;d+=n===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 YC=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XC(n.shape,s,a):new KC(n.shape,s,a);return t.runWebGLProgram(i,[n],n.dtype)},vP={kernelName:bn,backendName:"webgl",kernelFunc:YC};var W9=`
  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);
`,G9=`
  // 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));
  `+Al+`
  return result;
`,U9=at({opSnippet:W9,packedOpSnippet:G9}),kP={kernelName:_n,backendName:"webgl",kernelFunc:U9};function j9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=x.parseAxisParam(s,n.shape),c=u,p=S.getAxesPermutation(c,i),m=n;p!=null&&(m=Bt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,i),l.push(m)),S.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:y}=CR(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,y,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=x.sizeFromShape(h),y=me({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=yu(n.dtype),_=So(y,b,"prod",t);f=me({inputs:{x:_},backend:t,attrs:{shape:d}}),l.push(y),l.push(_)}if(a){l.push(f);let d=S.expandShapeToKeepDim(f.shape,u);f=me({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var CP={kernelName:wi,backendName:"webgl",kernelFunc:j9};var ZC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=IR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},IP={kernelName:fa,backendName:"webgl",kernelFunc:ZC};var q9="return 1.0 / x;",H9=Ce({opSnippet:q9}),NP={kernelName:vi,backendName:"webgl",kernelFunc:H9};var K9=xr+`
  return (x < 0.0) ? 0.0 : x;
`,X9=`
  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;
`,Y9=Ce({opSnippet:K9,packedOpSnippet:X9}),SP={kernelName:vn,backendName:"webgl",kernelFunc:Y9};var Z9=xr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,J9=`
  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;
`,Q9=Ce({opSnippet:Z9,packedOpSnippet:J9}),TP={kernelName:Cn,backendName:"webgl",kernelFunc:Q9};var JC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],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 QC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],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 < ${o-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 eJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new QC(n.shape,l,u,s,a):new JC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var EP={kernelName:kn,backendName:"webgl",kernelFunc:eJ};var eI=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&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), ${n-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 tJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new eI(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var AP={kernelName:hu,backendName:"webgl",kernelFunc:tJ};var tI=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"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 rJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new tI(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var DP={kernelName:da,backendName:"webgl",kernelFunc:rJ};var rI=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&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(${n}) - 1),
                ${o} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

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

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

        setOutput(accumulator);
      }
    `}};function oJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new rI(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var $P={kernelName:du,backendName:"webgl",kernelFunc:oJ};var oI=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Be(o);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}};var nI=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=qt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Be(o);o===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${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 = ${l(n.slice())};
          if(${s}){
            result.g = ${u(n.slice())};
          }
          if(${a}) {
            result.b = ${c(n.slice())};
            if(${s}) {
              result.a = ${p(n.slice())};
            }
          }
          setOutput(result);
        }
    `;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,_)=>f(_,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function nJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=x.parseAxisParam(s,n.shape);if(a===0)return Ht({inputs:{x:n},backend:t});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nI(n.shape,i):new oI(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var RP={kernelName:In,backendName:"webgl",kernelFunc:nJ};var sI=class{constructor(e,t,o,n){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]=S.getImageCenter(n,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof o=="number"?f=`float outputValue = ${o.toFixed(2)};`:f=`
        vec3 fill = vec3(${o.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 < ${s}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var FP={kernelName:$i,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new sI(o.shape,n,s,a);return i.runWebGLProgram(l,[o],o.dtype)}};var sJ=`
  // 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;
    }
  }
`,iJ=Ce({opSnippet:sJ}),OP={kernelName:Nn,backendName:"webgl",kernelFunc:iJ};var aJ="return inversesqrt(x);",lJ=Ce({opSnippet:aJ,cpuKernelImpl:NR}),PP={kernelName:Sn,backendName:"webgl",kernelFunc:lJ};var ch=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Be(s.length),u=Be(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${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 uJ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=S.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=me({inputs:{x:n},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 ch(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 MP={kernelName:ki,backendName:"webgl",kernelFunc:uJ};var iI=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Be(o);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${n});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function cJ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new iI(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],fr(n.dtype,s.dtype))}var LP={kernelName:hs,backendName:"webgl",kernelFunc:cJ};var pJ=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${S.SELU_SCALEALPHA};
  float scale = ${S.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,mJ=Ce({opSnippet:pJ}),zP={kernelName:Ci,backendName:"webgl",kernelFunc:mJ};var fJ="return 1.0 / (1.0 + exp(-1.0 * x));",dJ=Ce({opSnippet:fJ}),BP={kernelName:En,backendName:"webgl",kernelFunc:dJ};var hJ=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,gJ=Ce({opSnippet:hJ}),VP={kernelName:Ni,backendName:"webgl",kernelFunc:gJ};var xJ=Px+`
  return sin(x);
`,yJ=Ce({opSnippet:xJ}),WP={kernelName:Tn,backendName:"webgl",kernelFunc:yJ};var bJ=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,_J=Ce({opSnippet:bJ}),GP={kernelName:Ii,backendName:"webgl",kernelFunc:_J};var wJ=`
  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;
`,vJ=Ce({opSnippet:wJ}),UP={kernelName:Si,backendName:"webgl",kernelFunc:vJ};var kJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;x.assert(n.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<n.shape.length;++y)l.push([0,0]);let u=[],c=YC({inputs:{x:n},backend:t,attrs:{paddings:l,constantValue:0}}),p=S.getReshaped(c.shape,s,i,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,i,!1),d=me({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Bt({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},jP={kernelName:ha,backendName:"webgl",kernelFunc:kJ};function CJ(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=S.calculateShapes(s,n,i),m=!1,f=new ch(u,l,n.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,n,a],s.dtype),h=me({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var qP={kernelName:gu,backendName:"webgl",kernelFunc:CJ};function IJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=x.parseAxisParam(a,n.shape)[0],l=S.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),p=n.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=qa({inputs:{x:n},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var HP={kernelName:xs,backendName:"webgl",kernelFunc:IJ};var NJ="return sqrt(x);",SJ=Ce({opSnippet:NJ}),KP={kernelName:An,backendName:"webgl",kernelFunc:SJ};var TJ="return x * x;",EJ=Ce({opSnippet:TJ}),XP={kernelName:ga,backendName:"webgl",kernelFunc:EJ};var YP="return (a - b) * (a - b);",AJ=at({opSnippet:YP,packedOpSnippet:YP}),ZP={kernelName:Rn,backendName:"webgl",kernelFunc:AJ};function DJ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=xr+`
    return x > 0.0 ? 1.0 : float(${e.alpha});
  `,s=new mo(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var JP={kernelName:Di,backendName:"webgl",kernelFunc:DJ};var aI=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Be(o.length),a=Be(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${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 $J(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:y,outShape:b}=sr.sliceInfo(n.shape,s,a,i,l,u,c,p,m),_=me({inputs:{x:n},backend:t,attrs:{shape:y}}),w;if(f){let $=qa({inputs:{x:_},backend:t,attrs:{begin:d,size:g}});w=me({inputs:{x:$},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo($)}else if(b.some($=>$===0))w=t.makeTensorInfo(b,n.dtype,[]);else if(t.shouldExecuteOnCPU([_])){let F=t.texData.get(_.dataId).values,M=Ie(_.shape,_.dtype,F),L=TR(b,M,h,d);w=t.makeTensorInfo(b,_.dtype,L.values)}else{let T=new aI(d,h,b);w=t.runWebGLProgram(T,[_],_.dtype)}let k=me({inputs:{x:w},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(_),t.disposeIntermediateTensorInfo(w),k}var QP={kernelName:Ti,backendName:"webgl",kernelFunc:$J};var RJ="return tan(x);",FJ=Ce({opSnippet:RJ}),eM={kernelName:Ei,backendName:"webgl",kernelFunc:FJ};var OJ=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,PJ=Ce({opSnippet:OJ}),tM={kernelName:On,backendName:"webgl",kernelFunc:PJ};var lI=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=Be(this.rank),s=MJ(e);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function MJ(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function uI(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype==="string"){let u=t.readSync(n.dataId).map(m=>x.decodeString(m)),c=Ie(n.shape,n.dtype,u),p=AR(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new lI(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var rM={kernelName:_o,backendName:"webgl",kernelFunc:uI};function LJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=t.readSync(n.dataId),[l,u]=DR(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var oM={kernelName:Ai,backendName:"webgl",kernelFunc:LJ};function zJ(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Xi(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=$R(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var nM={kernelName:xu,backendName:"webgl",kernelFunc:zJ};function BJ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,l=n.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=qa({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 sM={kernelName:ys,backendName:"webgl",kernelFunc:BJ};var cI=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let l="0.0",u="sumValue",c=Math.floor(o/4)*4,p=o%4,m=`
        sumValue += dot(values, segFilter);
    `,f="";s%o>0&&(f=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);let d="";s%o>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(
          ${a})) * float(${o}));
        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 VJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=S.getAxesPermutation([u],i),p=n;c!=null&&(p=Bt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=S.getInnerMostAxes(1,i)[0]);let m=S.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(n.dtype),g=(w,k,$,T,F)=>{let M=w.shape[0],L=w.shape[1],G=S.segment_util.segOpComputeOptimalWindowSize(L,F),H={windowSize:G,inSize:L,batchSize:M,numSegments:F},U=new cI(H,k),Z=t.compileAndRun(U,[w,$],T);if(l.push(Z),Z.shape[1]===F)return Z;let K=ZC({backend:t,attrs:{start:0,stop:F,step:1,dtype:"float32"}}),X=uI({inputs:{x:K},backend:t,attrs:{reps:[L/G]}});return l.push(K),l.push(X),g(Z,k,X,T,F)},y=g(d,"unsortedSegmentSum",s,h,a),b=me({inputs:{x:y},backend:t,attrs:{shape:m}}),_=b;if(c!=null){l.push(b);let w=S.getUndoAxesPermutation(c);_=Bt({inputs:{x:_},backend:t,attrs:{perm:w}})}return l.forEach(w=>t.disposeIntermediateTensorInfo(w)),_}var iM={kernelName:xa,backendName:"webgl",kernelFunc:VJ};var WJ=[UO,jO,sF,aF,lF,uF,pF,mF,fF,dF,xF,yF,bF,_F,vF,wF,kF,IF,CF,NF,SF,TF,EF,DF,$F,PF,LF,zF,VF,HR,UF,qF,HF,jF,XF,YF,KF,ZF,JF,QF,rO,oO,nO,iO,aO,sO,lO,uO,cO,pO,mO,fO,hO,gO,yO,bO,_O,wO,kO,CO,IO,NO,SO,TO,EO,AO,DO,qR,$O,WF,RO,FO,OO,KR,PO,MO,LO,BO,zO,VO,WO,GO,HO,YO,XO,ZO,JO,eP,KO,rP,oP,nP,sP,iP,pP,QR,fP,dP,hP,gP,RF,xP,_P,wP,vP,kP,XR,CP,IP,FF,aP,NP,TP,SP,tF,EP,AP,DP,$P,RP,FP,OP,PP,MP,LP,zP,BP,VP,WP,GP,AF,cP,UP,jP,qP,HP,KP,XP,ZP,JP,QP,uP,oF,eM,tM,rM,oM,nF,nM,sM,iM,yP];for(let r of WJ)tl(r);var aM="2.8.3";var GJ={"tfjs-core":Zb,"tfjs-backend-cpu":GA,"tfjs-backend-webgl":jR,"tfjs-data":_x,"tfjs-layers":xl,"tfjs-converter":fx,tfjs:aM};var Vt;(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"})(Vt||(Vt={}));var Rl;(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"})(Rl||(Rl={}));var lM;function UJ(r){lM=r.wasm.cwrap(_s,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.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}=o,m=t.dataIdMap.get(n.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 ${F.shape.length}.`);d=F.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=Rl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],_=n.shape[0],w=t.makeOutput([_,y,b],n.dtype),k=t.dataIdMap.get(w.dataId).id,$=new Uint8Array(new Int32Array(n.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return lM(m,$,n.shape.length,f,T,s.shape.length,l,u,g,d,h,p||0,k),w}var uM={kernelName:_s,backendName:"wasm",setupFunc:UJ,kernelFunc:jJ};function Ft(r){let e;function t(n){e=n.wasm.cwrap(r,null,["number","number"])}function o(n){let{backend:s,inputs:{x:a}}=n,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:o}}var cM=Ft(is);function kt(r,e,t){let o;function n(a){o=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=S.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,_=()=>o(p,g,u.shape.length,m,y,c.shape.length,Vt[u.dtype],b);if(e&&u.dtype==="float32")return _(),h;let w=S.getBroadcastDims(u.shape,d),k=S.getBroadcastDims(c.shape,d),$=w.every((F,M)=>F===M),T=k.every((F,M)=>F===M);if($&&T)return _(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var qJ=!0,pM=kt(bo,qJ);var mM;function HJ(r){mM=r.wasm.cwrap(Ho,null,["array","number","number","number"])}function KJ(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(x.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return mM(s,n.length,Vt[o.dtype],a),o}var fM={kernelName:Ho,backendName:"wasm",setupFunc:HJ,kernelFunc:KJ};function hc(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var dM={kernelName:cs,backendName:"wasm",kernelFunc:hc};var hM;function XJ(r){hM=r.wasm.cwrap(Pn,null,["number","array","number","number","number","array","number"])}function Hp(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=ZJ(e.x.shape,o.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=YJ(e.x.shape,o.perm),l={dataId:e.x.dataId,shape:n,dtype:e.x.dtype};if(a){let d=hc({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 hM(c,f,l.shape.length,Vt[l.dtype],p,m,s.length),u}function YJ(r,e){let t=new Array(r.length);for(let o=0;o<t.length;o++)t[o]=r[e[o]];return t}function ZJ(r,e){let t=[],o=[];for(let n=0;n<r.length;++n)r[n]!==1&&t.push(r[n]),r[e[n]]!==1&&o.push(e[n]);for(let n=0;n<o.length;++n){let s=-1;for(let a=0;a<o.length;++a)o[a]>=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var gM={kernelName:Pn,backendName:"wasm",kernelFunc:Hp,setupFunc:XJ};function rs(r,e,t){let o=r.shape,n=r.shape.length,s=x.parseAxisParam(e,o),a=s,i=S.getAxesPermutation(a,n),l=null,u=!1;if(i!=null){let c=new Array(n);for(let f=0;f<c.length;f++)c[f]=o[i[f]];a=S.getInnerMostAxes(a.length,n),l=Hp({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 xM;function JJ(r){xM=r.wasm.cwrap(Ko,null,["number","number","number","number","number"])}function QJ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n}=o,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=a,l=s,{transposed:u,axes:c,inputWasTransposed:p}=rs(s,n,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 xM(i,Vt[l.dtype],h,g,d),p&&e.disposeData(u.dataId),f}var yM={kernelName:Ko,backendName:"wasm",kernelFunc:QJ,setupFunc:JJ};var bM;function eQ(r){bM=r.wasm.cwrap(Xo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tQ(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=S.computePool2DInfo(n.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,_=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let w=o.makeOutput(c.outShape,"float32"),k=o.dataIdMap.get(w.dataId).id;return bM(s,n.shape[0],n.shape[1],n.shape[2],p,m,f,d,h,g,y,b,_,k),w}var _M={kernelName:Xo,backendName:"wasm",setupFunc:eQ,kernelFunc:tQ};function Wr(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=x.sizeFromShape(o.shape),a=x.inferFromImplicitShape(n,s);return x.assert(s===x.sizeFromShape(a),()=>`new shape: ${a}, old shape: ${o.shape}. 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AL={kernelName:_i,backendName:"wasm",setupFunc:tee,kernelFunc:ree};var oee=!1,DL=kt(xi,oee,"bool");var $L;function nee(r){$L=r.wasm.cwrap(yn,null,["number","number","number","number","number"])}function see(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=t.makeOutput([...n.shape,s],"int32"),u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(n.dataId).id;return $L(p,s,a,i,u),l}var RL={kernelName:yn,backendName:"wasm",setupFunc:nee,kernelFunc:see};function iee(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var FL={kernelName:ms,backendName:"wasm",kernelFunc:iee};function aee(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Kx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(l=>{x.assertShapesMatch(s,l.shape,"All tensors passed to stack must have matching shapes"),x.assert(a===l.dtype,()=>"All tensors passed to stack must have matching 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WL={kernelName:wi,backendName:"wasm",setupFunc:fee,kernelFunc:dee};var hee=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Ud(o,n,s,a),l=e.makeOutput([i.length],a);return e.typedArrayFromHeap(l).set(i),l},GL={kernelName:fa,backendName:"wasm",kernelFunc:hee};var gee=!0,UL=kt(rn,gee);var jL=Ft(vn);var qL=Ft(Cn);var HL;function xee(r){HL=r.wasm.cwrap(kn,null,["number","number","number","number","number","number","number","number","number","number"])}function yee(r){let{backend:e,inputs:t,attrs:o}=r,{images:n}=t,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,[c,p,m,f]=n.shape,d=[c,l,u,f],h=e.dataIdMap.get(n.dataId),g;h.dtype!=="float32"&&(g=gc({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let y=h.id,b=e.makeOutput(d,"float32");if(x.sizeFromShape(n.shape)===0)return b;let _=e.dataIdMap.get(b.dataId).id;return HL(y,c,p,m,f,l,u,s?1:0,a?1:0,_),g!=null&&e.disposeData(g.dataId),b}var KL={kernelName:kn,backendName:"wasm",setupFunc:xee,kernelFunc:yee};var XL;function bee(r){XL=r.wasm.cwrap(In,null,["number","array","number","array","number","number"])}function _ee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=x.parseAxisParam(s,n.shape);if(n.shape.length===0)return hc({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),l=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new Int32Array(n.shape).buffer);return XL(l,c,a.length,p,n.shape.length,u),Wr({inputs:{x:i},attrs:{shape:n.shape},backend:t})}var YL={kernelName:In,backendName:"wasm",kernelFunc:_ee,setupFunc:bee};var ZL;function wee(r){ZL=r.wasm.cwrap($i,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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as DataStorage,Ql as DenseBincount,ti as DepthToSpace,tn as DepthwiseConv2dNative,eu as DepthwiseConv2dNativeBackpropFilter,tu as DepthwiseConv2dNativeBackpropInput,ru as Diag,la as Dilation2D,Oc as Dilation2DBackpropFilter,Fc as Dilation2DBackpropInput,db as ENV,Zg as EarlyStopping,ri as Elu,ou as EluGrad,zh as Environment,ni as Equal,oi as Erf,on as Exp,ls as ExpandDims,si as Expm1,nu as FFT,ua as Fill,ii as FlipLeftRight,nn as Floor,sn as FloorDiv,Pc as FromPixels,an as FusedBatchNorm,ws as FusedConv2D,vs as FusedDepthwiseConv2D,ai as GatherNd,us as GatherV2,mx as GraphModel,li as Greater,ln as GreaterEqual,$g as History,su as IFFT,cs as Identity,iu as Imag,At as InputSpec,ui as IsFinite,ci as IsInf,pi as IsNan,Ws as KernelBackend,ca as LRN,lu as LRNGrad,bf as LayerVariable,Io as LayersModel,un as LeakyRelu,mi as Less,fi as LessEqual,au as LinSpace,cn as Log,di as Log1p,bb as LogSoftmax,hi as LogicalAnd,Qa as LogicalNot,el as LogicalOr,pn as Max,fn as MaxPool,pa as MaxPool3D,cu as MaxPool3DGrad,uu as MaxPoolGrad,pu as MaxPoolWithArgmax,mn as Maximum,dn as Mean,hn as Min,gn as Minimum,ma as MirrorPad,gi as Mod,ip as MomentumOptimizer,mu as Multinomial,xn as Multiply,ps as Neg,yi as NonMaxSuppressionV3,bi as NonMaxSuppressionV4,_i as NonMaxSuppressionV5,xi as NotEqual,XI as OP_SCOPE_SUFFIX,yn as OneHot,ms as OnesLike,Mr as Optimizer,fs as Pack,bn as PadV2,w3 as Pool,_n as Pow,wn as Prelu,wi as Prod,ap as RMSPropOptimizer,co as RNN,fa as Range,Cb as Rank,fu as Real,rn as RealDiv,vi as Reciprocal,Ut as Reduction,vn as Relu,Cn as Relu6,ds as Reshape,kn as ResizeBilinear,hu as ResizeBilinearGrad,da as ResizeNearestNeighbor,du as ResizeNearestNeighborGrad,In as Reverse,$i as RotateWithOffset,Nn as Round,Sn as Rsqrt,cl as SGDOptimizer,ki as ScatterNd,hs as Select,Ci as Selu,ji as Sequential,En as Sigmoid,Ni as Sign,Tn as Sin,Ii as Sinh,gs as Slice,$n as Softmax,Si as Softplus,ha as SpaceToBatchND,gu as SparseToDense,xs as SplitV,An as Sqrt,ga as Square,Rn as SquaredDifference,Di as Step,Ti as StridedSlice,Fn as Sub,Dn as Sum,Vr as SymbolicTensor,Ei as Tan,On as Tanh,R as Tensor,ct as TensorBuffer,_o as Tile,Ai as TopK,Pn as Transpose,xu as Unique,ys as Unpack,xa as UnsortedSegmentSum,ol as Variable,bs as ZerosLike,_s as _FusedMatMul,Et as abs,wm as acos,vm as acosh,ee as add,Qb as addN,W_ as addStrict,ku as all,il as any,al as argMax,km as argMin,Cm as asin,Im as asinh,Nm as atan,Sm as atan2,Tm as atanh,ka as avgPool,Em as avgPool3d,Jb as backend,S as backend_util,bW as basicLSTMCell,Ln as batchNorm,o_ as batchNorm2d,n_ as batchNorm3d,s_ as batchNorm4d,Ca as batchToSpaceND,i_ as bincount,SU as booleanMaskAsync,ll as broadcastTo,Yh as browser,Ie as buffer,W1 as callbacks,ne as cast,Am as ceil,ir as clipByValue,Po as clone,wo as complex,Je as concat,a_ as concat1d,l_ as concat2d,u_ as concat3d,c_ as concat4d,xw as constraints,Nu as conv1d,Xr as conv2d,Su as conv2dTranspose,Dm as conv3d,BW as conv3dTranspose,C3 as copyRegisteredKernels,Ia as cos,Tu as cosh,tf as cosineWindow,Eu as cumsum,Zr as customGrad,jv as data,p_ as denseBincount,Ot as deprecationWarn,$m as depthToSpace,Lo as depthwiseConv2d,j1 as deregisterOp,Vc as device_util,KW as diag,Rm as dilation2d,$V as disableDeprecationWarnings,Ae as dispose,RV as disposeVariables,fe as div,Fm as divNoNan,G_ as divStrict,m_ as dot,Q_ as dropout,Is as elu,DV as enableDebugMode,AV as enableProdMode,ew as enclosingPowerOfTwo,Cs as engine,W as env,Yr as equal,P_ as equalStrict,Om as erf,Qt as exp,br as expandDims,Pm as expm1,Xc as eye,Fa as fft,Na as fill,zV as findBackend,BV as findBackendFactory,Ns as floor,vu as floorDiv,Vn as fused,zn as gather,J_ as gatherND,Zh as gather_util,MV as getBackend,Bh as getGradient,dm as getKernel,hm as getKernelsForBackend,kG as grad,CG as grads,Xt as greater,Or as greaterEqual,M_ as greaterEqualStrict,L_ as greaterStrict,Li as ifft,Au as imag,As as image,ZU as inTopKAsync,vw as initializers,Hg as input,Cr as io,Wu as irfft,f_ as isFinite,d_ as isInf,h_ as isNaN,Dt as keep,Er as kernel_impls,nv as layers,Sa as leakyRelu,Ta as less,no as lessEqual,z_ as lessEqualStrict,B_ as lessStrict,sw as linalg,g_ as linspace,bE as loadGraphModel,E1 as loadLayersModel,Mm as localResponseNormalization,ar as log,Du as log1p,x_ as logSigmoid,$u as logSoftmax,zm as logSumExp,dr as logicalAnd,Ea as logicalNot,Ru as logicalOr,w_ as logicalXor,Gj as losses,je as matMul,NN as math,lr as max,Aa as maxPool,Bm as maxPool3d,v_ as maxPoolWithArgmax,Ir as maximum,U_ as maximumStrict,bt as mean,qc as memory,lv as metrics,Mi as min,Bo as minimum,j_ as minimumStrict,Vm as mirrorPad,Fu as mod,q_ as modStrict,S1 as model,uv as models,Yc as moments,jU as movingAverage,O as mul,H_ as mulStrict,QG as multiRNNCell,k_ as multinomial,He as neg,rf as nextFrame,Uu as norm,vo as notEqual,V_ as notEqualStrict,ks as oneHot,Nr as ones,rr as onesLike,N as op,n4 as outerProduct,Pr as pad,a4 as pad1d,u4 as pad2d,p4 as pad3d,f4 as pad4d,C_ as pool,_r as pow,K_ as powStrict,$a as prelu,Vb as print,Ou as prod,FV as profile,v4 as rand,A4 as randomGamma,lg as randomNormal,Ts as randomUniform,Jc as range,PV as ready,ul as real,Gm as reciprocal,wu as registerBackend,A1 as registerCallbackConstructor,wb as registerGradient,tl as registerKernel,U1 as registerOp,cv as regularizers,Sr as relu,Mu as relu6,LV as removeBackend,z as reshape,Yt as reverse,z4 as reverse1d,V4 as reverse2d,G4 as reverse3d,j4 as reverse4d,Oa as rfft,Um as round,Lu as rsqrt,ce as scalar,Z_ as scatterND,Jh as scatter_util,zu as selu,jm as separableConv2d,T1 as sequential,te as serialization,WN as setBackend,VV as setPlatform,Zee as setWasmPath,Jee as setWasmPaths,R_ as setdiff1dAsync,Kr as sigmoid,qm as sign,Wj as signal,Bu as sin,Vu as sinh,Fe as slice,Hm as slice1d,ug as slice2d,Km as slice3d,Qc as slice4d,sr as slice_util,Ra as softmax,Ss as softplus,Da as spaceToBatchND,ef as sparseToDense,Vj as spectral,ur as split,_t as sqrt,Me as square,Pa as squaredDifference,X_ as squaredDifferenceStrict,ko as squeeze,Wt as stack,Es as step,Xm as stridedSlice,pe as sub,Y_ as subStrict,be as sum,yu as sumOutType,Ym as tan,Pi as tanh,Fr as tensor,Gt as tensor1d,zi as tensor2d,jb as tensor3d,xU as tensor4d,yU as tensor5d,bU as tensor6d,Mn as tensor_util,zN as test_util,V as tidy,zo as tile,OV as time,Zm as topk,pl as train,qe as transpose,Gu as truncatedNormal,ep as unique,k3 as unregisterGradient,v3 as unregisterKernel,Jm as unsortedSegmentSum,cr as unstack,fr as upcastType,x as util,IG as valueAndGrad,NG as valueAndGrads,F_ as variable,sg as variableGrads,GJ as version,fx as version_converter,Zb as version_core,xl as version_layers,Qee as version_wasm,$t as where,Qm as whereAsync,pt as zeros,Ne 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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