face-api/dist/face-api.js

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

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r=++this.pendingBackendInitId,s=n.then(a=>r<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,Qs(`Initialization of backend ${e} failed`),Qs(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Qs(`Initialization of backend ${e} failed`),Qs(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,s=this.readSync(t),a=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to 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");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Zl.nextTensorId++}nextVariableId(){return Zl.nextVariableId++}clone(e){let t=M.runKernel(Oo,{x:e}),n={x:e},r=a=>({x:()=>{let o="float32",i={x:a},c={dtype:o};return M.runKernel(wo,i,c)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,s,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Rh(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),s=0;n.forEach(i=>{s+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-s-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,c=my(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(my(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Rh(h,this.backendName);D(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let b=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>x.rank!=null?x:this.makeTensorFromTensorInfo(x));if(r){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!r||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,m,g),g}}let{inputs:u,attrs:l}=e,p=my(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(c,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(c,u,t,p,n,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:c,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let r=Ny(e);if(r!=null){let s=r.inputsToSave||[],a=r.outputsToSave||[],o;r.saveAllInputs?(D(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(c=>t[c])):o=s.map(c=>t[c]);let i=n.filter((c,u)=>a[u]);return o.concat(i)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let s=e;n==="string"&&ea(e[0])&&(s=e.map(i=>Ad(i)));let a=r.write(s,t,n),o=new Te(t,n,a,this.nextTensorId());if(this.trackTensor(o,r),n==="string"){let i=this.state.tensorInfo.get(a),c=Z1(s);this.state.numBytes+=c-i.bytes,i.bytes=c}return o}makeTensorFromDataId(e,t,n,r){n=n||"float32";let s={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(s,r)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:r,dtype:s}=e,a=new Te(r,s,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let s=new ia(e,t,n,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}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Cy(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ia||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Cy(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,s,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=Ny(e);i!=null&&(r=i.gradFunc),r!=null&&(o.gradient=c=>(c=c.map((u,l)=>{if(u==null){let p=n[l],d=df(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return u}),r(c.length>1?c:c[0],s,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Rv(e),n=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!n.has(a.id)&&a.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===r.id&&this.track(s)})}gradients(e,t,n,r=!1){if(D(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));D(s instanceof Te,()=>"The result y returned by f() must be a tensor.");let a=TF(this.state.activeTape,t,s);if(!r&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let o={};o[s.id]=n==null?OF(s.shape):n,CF(o,a,c=>this.tidy(c),MF);let i=t.map(c=>o[c.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(c=>{for(let u of c.saved)u.dispose()}),this.state.activeTape=null),{value:s,grads:i}})}customGrad(e){return D(oa(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{D(t.every(o=>o instanceof Te),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((o,i)=>{r[i]=o});let s=(o,i)=>(n=e(...t,i),D(n.value instanceof Te,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),D(oa(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let c=n.gradFunc(o,i),u=Array.isArray(c)?c:[c];D(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),D(u.every(p=>p instanceof Te),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let l={};return u.forEach((p,d)=>{l[d]=()=>p}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Yl(),n=await this.backend.time(e);return n.wallMs=Yl()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new dk;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Zl.nextTensorId=0;Zl.nextVariableId=0;function OF(e){let t=_v(ht(e),"float32");return M.makeTensor(t,e,"float32")}function pS(){let e=nS();if(e._tfengine==null){let t=new tS(e);e._tfengine=new Zl(t)}return aF(e._tfengine.ENV),AF(()=>e._tfengine),e._tfengine}var M=pS();function MF(e,t){let n={a:e,b:t};return M.runKernel(ba,n)}var $d={};Ae($d,{isBrowser:()=>hS,isMobile:()=>BF,mockIsMobile:()=>zF});function LF(){return typeof navigator!="undefined"&&navigator!=null}var Ry;function zF(e){Ry=e}function BF(e){if(Ry!==void 0)return Ry;if(e||LF()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let n=e;return 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The output of every math call will be downloaded to CPU and checked for NaNs. 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o=JSON.parse(s);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer),o.initializerSignature!=null&&(t.initializerSignature=o.initializerSignature),o.trainingConfig!=null&&(t.trainingConfig=o.trainingConfig)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=HF(a),t}};so.URL_SCHEME="localstorage://";var SS=e=>q().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(so.URL_SCHEME)?uR(e.slice(so.URL_SCHEME.length)):null;$t.registerSaveRouter(SS);$t.registerLoadRouter(SS);function uR(e){return new so(e)}var lR=class{constructor(){D(q().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),D(typeof window=="undefined"||typeof 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pO(e,t,n,r,s,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),c=_(n,"variance","batchNorm"),u;s!=null&&(u=_(s,"scale","batchNorm"));let l;return r!=null&&(l=_(r,"offset","batchNorm")),D(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),D(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),D(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&D(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&D(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${l.rank}.`),ka(o,i,c,l,u,a)}var ix=z({batchNorm4d_:pO});function hO(e,t,n){let r=_(e,"x","bincount"),s=_(t,"weights","bincount");D(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),D(n>=0,()=>`size must be non-negative, but got 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m=Y(B(fe(h,Y(un(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&_e(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),O(()=>{this.accBeta1.assign($s(this.beta1,this.iterations_+1)),this.accBeta2.assign($s(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Tm.className="Adam";Ia(Tm);var Cm=class extends Rs{constructor(e,t,n,r=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],O(()=>{this.iteration=xe(0).variable(),this.accBeta1=xe(t).variable()}),r==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);O(()=>{let n=de(1,this.accBeta1),r=fe(-this.learningRate,Y(B(this.iteration,this.decay),1));t.forEach((s,a)=>{let o=M.registeredVariables[s],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:qe(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:qe(o).variable(i)});let c=Array.isArray(e)?e[a].tensor:e[s];if(c==null)return;let u=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,p=Y(B(u,this.beta1),B(c,1-this.beta1)),d=B(l,this.beta2),h=Lt(c),f=hs(d,h);u.assign(p),l.assign(f);let m=Y(B(fe(r,n),fe(p,Y(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(Y(this.iteration,1)),this.accBeta1.assign(B(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&_e(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&_e(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)}};Cm.className="Adamax";Ia(Cm);var jd=class extends Rs{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=Array.isArray(e)?e[r].tensor:e[n];if(s==null)return;let a=M.registeredVariables[n];O(()=>{let o=Y(B(this.c,s),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Jt(xe(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Nm.className="Momentum";Ia(Nm);var _m=class extends Rs{constructor(e,t=.9,n=0,r=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,r==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,r)=>{let s=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${n}/rms`,variable:O(()=>qe(s).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${n}/momentum`,variable:O(()=>qe(s).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${n}/mg`,variable:O(()=>qe(s).variable(a))});let o=Array.isArray(e)?e[r].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[r].variable,c=this.accumulatedMoments[r].variable;O(()=>{let u=Y(B(i,this.decay),B(it(o),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[r].variable,p=Y(B(l,this.decay),B(o,1-this.decay)),d=fe(B(o,this.learningRate),un(de(u,Y(it(p),this.epsilon)))),h=Y(B(c,this.momentum),d);i.assign(u),l.assign(p),c.assign(h);let f=de(s,h);s.assign(f)}else{let l=Y(B(i,this.decay),B(it(o),1-this.decay)),p=Y(B(c,this.momentum),fe(B(o,this.learningRate),un(Y(l,this.epsilon))));i.assign(l),c.assign(p);let d=de(s,p);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&_e(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&_e(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&_e(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};_m.className="RMSProp";Ia(_m);var Zs=class{static sgd(e){return new jd(e)}static momentum(e,t,n=!1){return new Nm(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,s=!1){return new _m(e,t,n,r,s)}static adam(e=.001,t=.9,n=.999,r=null){return new Tm(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new km(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,s=0){return new Cm(e,t,n,r,s)}static 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Vz(e,t){let n=e[0].length;e.forEach((s,a)=>{D(s.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),D(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((s,a)=>{for(let o=0;o<n;o++)D(o===t||s[o]===r[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${s}) does not match the shape of the rest (${r}) along the non-concatenated axis ${a}.`)})}function Uz(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var rs;(function(e){e[e.FIRST_DIM_SIZE=0]="FIRST_DIM_SIZE",e[e.VALUE_ROWIDS=1]="VALUE_ROWIDS",e[e.ROW_LENGTHS=2]="ROW_LENGTHS",e[e.ROW_SPLITS=3]="ROW_SPLITS",e[e.ROW_LIMITS=4]="ROW_LIMITS",e[e.ROW_STARTS=5]="ROW_STARTS"})(rs||(rs={}));function Gz(e,t,n){let r=new Array;if(n==null&&t==null)return r;if(t==null)for(;r.length<e+n.length;)r.push(-1);else r=t.slice();if(n==null)return r;if(e+n.length!==r.length)throw new Error(`rt input.shape and shape=${t} are 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Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],s=t[n];if(s==null)continue;let a=r.rank;if(s.ndim!=null&&a!==s.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${s.ndim}, found ndim=${a}`);if(s.maxNDim!=null&&a>s.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${s.maxNDim}, found ndim=${a}`);if(s.minNDim!=null&&a<s.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${s.minNDim}, found ndim=${a}.`);if(s.dtype!=null&&r.dtype!==s.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${s.dtype}, found dtype=${r.dtype}.`);if(s.axes){let o=r.shape;for(let i in s.axes){let c=Number(i),u=s.axes[i],l=c>=0?o[c]:o[o.length+c];if(u!=null&&[u,null].indexOf(l)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${c} of input shape to have value ${u} but got shape ${o}.`)}}if(s.shape!=null)for(let o=0;o<s.shape.length;++o){let i=s.shape[o],c=r.shape[o];if(i!=null&&c!=null&&i!==c)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${s.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=vt(e),r=!0;for(let a of n)if(!(a instanceof Vr)){r=!1;break}let s=!0;for(let a of n)if(a instanceof Vr){s=!1;break}if(r===s)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return Za(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of vt(e))a.push(o.shape);this.build(Mn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(e),s){let a=this.call(e,t),o=vt(a),i=[];for(let c of o)n.indexOf(c)!==-1&&(c=c.clone()),i.push(c);if(a=Mn(i),this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=bV(e),o=this.computeOutputShape(a),i,c=yV(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,l)=>new Vr(c,u,this,vt(e),t,this.name,l)):i=new Vr(c,o,this,vt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Pe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ks(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ks(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Wr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Vh(this.weights)}build(e){this.built=!0}getWeights(e=!1){return jy(e?this.trainableWeights:this.weights)}setWeights(e){O(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},r={},s={},a={},o=[],i=(b,y,v,x,k,S)=>{(x==null||k==null||S==null)&&(x=b.sourceLayer,k=b.nodeIndex,S=b.tensorIndex);let C=x.inboundNodes[k];if(v.indexOf(C)!==-1)throw new Wr(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(C)!==-1)return;this.containerNodes.add(ns.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(C)===-1&&v.push(C);let E=C.inboundLayers.length;for(let $=0;$<E;$++){let F=C.inputTensors[$],A=C.inboundLayers[$],R=C.nodeIndices[$],T=C.tensorIndices[$];i(F,y,v,A,R,T)}for(y.push(C);v.indexOf(C)>=0;)v.splice(v.indexOf(C),1);o.push(C)},c=[],u=[];for(let b of this.outputs)i(b,c,u);let l=o.slice().reverse();for(let b of l){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=r[b.outboundLayer.id]==null?0:r[b.outboundLayer.id];y=Math.max(y,v),r[b.outboundLayer.id]=y,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],S=b.nodeIndices[x],C=k.inboundNodes[S],E=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(y+1,E),n[C.id]=C}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in r){let y=r[b];y in d||(d[y]=[]),d[y].push(s[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(ih);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],S=a[x.id];return k<S?-1:k>S?1:0});for(let v of y)v instanceof ns&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(ih);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new Wr(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new Wr(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Lm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new U(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,r++}let s=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)s.push([n[o],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new U(`${a.length} of ${r} weights are not set: ${a}`)}lw(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${xw}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Xy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return O(()=>{e=vt(e);let n=new ja;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Pl(this.outputs,n,t)})}computeMask(e,t){return O(()=>{e=vt(e);let n;return t==null?n=uo(null,e.length):n=vt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Wh(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],c=t[o],u=i.name+"_0_0";n[u]=c}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(ih);if(r.length>1)for(let o of r){let i=this.nodesByDepth[o];for(let c of i){let u=c.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let l=[];for(let f=0;f<c.inboundLayers.length;f++){let m=c.inboundLayers[f],g=c.nodeIndices[f],b=c.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];l.push(v)}let p=u.computeOutputShape(Mn(l)),d=Wh(p),h=u.inboundNodes.indexOf(c);for(let f=0;f<d.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=d[f]}}}let s=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],c=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],l=`${i.name}_${c}_${u}`;a.push(l)}for(let o=0;o<a.length;o++){let i=a[o];ss(i in n),s.push(n[i])}return Mn(s)}runInternalGraph(e,t){t==null&&(t=uo(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let c=this.inputs[i],u=e[i],l=t[i];n[c.id]=[u,l]}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(ih);for(let i of r){let c=this.nodesByDepth[i];for(let u of c){let l=u.outboundLayer,p=u.inputTensors,d=u.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=vt(l.call(v,f)),y=vt(l.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=vt(l.call(m,f)),y=vt(l.computeMask(m,g));if(l.activityRegularizer)throw new Pe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],S=y[v];n[x.id]=[k,S]}}}}let s=[],a=[],o=[];for(let i of this.outputs){ss(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[c,u]=n[i.id];o.push(c.shape),s.push(c),a.push(u)}return[s,a,o]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof ns?1:0;for(let s=0;s<r.inboundNodes.length;s++){let a=ns.nodeKey(r,s);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`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 U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return O(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=ns.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),c=[];for(let l=0;l<a.inboundNodes.length;l++){let p=a.inboundNodes[l],d=ns.nodeKey(a,l),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=ns.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}c.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=c,n.push(u)}e.layers=n;let r=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],c=ns.nodeKey(o,i);if(!this.containerNodes.has(c))continue;let u=t[c];u==null&&(u=0);let l=this.inputLayersTensorIndices[a];r.push([o.name,u,l])}e.inputLayers=r;let s=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],c=ns.nodeKey(o,i);if(!this.containerNodes.has(c))continue;let u=t[c];u==null&&(u=0);let l=this.outputLayersTensorIndices[a];s.push([o.name,u,l])}return e.outputLayers=s,e}static fromConfig(e,t,n={},r=!1){let s={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],S=v[2];if(y=v[3]==null?{}:v[3],!(x in s)){o(m,g);return}let C=s[x];if(C.inboundNodes.length<=k){o(m,g);return}let E=C.inboundNodes[k];b.push(E.outputTensors[S])}b.length>0&&m.apply(Mn(b),y)}function c(m){let g=m.name,b=Gr(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(r),s[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);o(b,v)})}let u=t.name,l=t.layers;for(let m of l)c(m);for(;!H4(a);)for(let m of l){let g=s[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)i(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];ss(g in s);let x=s[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];ss(g in s);let x=s[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:u})}get stateful(){if(this._stateful)throw new U("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(){O(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function bU(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>null);if(r===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let s=[];return t.forEach(a=>{a in e?s.push(e[a]):s.push(null)}),s}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function OC(e,t){return bU(e,t,"classWeight")}async function MC(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let s=O(()=>{if(e.shape.length===1)return is(e);if(e.shape.length===2){if(e.shape[1]>1)return ao(e,1);if(e.shape[1]===1)return W(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await s.data());_e(s);let o=[];return a.forEach(i=>{if(n[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`);o.push(n[i])}),Ke(o,"float32")}else return null}function yU(e,t){return B(e,t)}var vU=32;function LC(e,t){let n,r,s=t;n=s.xs,r=s.ys,w.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Bk("input",e.inputNames,n),o=Bk("output",e.outputNames,r),i=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let c=0;c<a.length;c++)w.assert(a[c].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[c]} has ${a[c].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let c=0;c<o.length;c++)w.assert(o[c].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[c]} has ${o[c].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function Bk(e,t,n){if(n instanceof Te)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let s of t){if(n[s]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${s}'.`);r.push(n[s])}return r}}function xU(e){if(e.length===3)throw new Pe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function wU(e,t,n){let r=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let s=n.validationData!=null,a,o;if(s)if(Wk(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=xU(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),c=e.getDedupedMetricsNames(),u;s?u=c.slice().concat(c.map(g=>"val_"+g)):u=c.slice();let l=_C(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=EC(l,p,n.epochs,null,null,IU(t,n),null,s,u);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(r||(m=await t.iterator());!r||b<n.batchesPerEpoch;){let v=await m.next();if(r&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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t=[],n,r=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let c=0;c<this.inputs.length;++c)a.push({key:this.inputs[c],value:r[c]});let o=new ja(a),i=Pl(this.outputs,o);for(let c=0;c<this.lossFunctions.length;++c){let u=this.lossFunctions[c],l=Ct(u(s[c],i[c]));c===0?n=l:n=Y(n,l),t.push(n)}for(let c=0;c<this.metricsTensors.length;++c){let u=this.metricsTensors[c][0],l=this.metricsTensors[c][1],p=Ct(u(s[l],i[l]));t.push(p)}return t})}async fit(e,t,n={}){return CU(this,e,t,n)}async fitDataset(e,t){return wU(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],s=n[1],o=this.makeTrainFunction()(r.concat(s)),i=[];for(let c of o){let u=await c.data();i.push(u[0])}return _e(o),zr(n[0],e),zr(n[1],t),Mn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let 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this.metrics=="string"||typeof this.metrics=="function")return[Ss(lh(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ss(lh(e)));{let e={};for(let t in this.metrics)e[t]=Ss(lh(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=nd(e.optimizer_config),n=Gr(t),r;if(typeof e.loss=="string")r=Ga(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(a=>Ga(a));else if(e.loss!=null){r={};for(let a in e.loss)r[a]=Ga(e.loss[a])}let s;if(Array.isArray(e.metrics))s=e.metrics.map(a=>Ga(a));else if(e.metrics!=null){s={};for(let a in e.metrics)s[a]=Ga(e.metrics[a])}this.compile({loss:r,metrics:s,optimizer:n})}async save(e,t){if(typeof e=="string"){let c=Ut.getSaveHandlers(e);if(c.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(c.length>1)throw new U(`Found more than one (${c.length}) save handlers for URL '${e}'`);e=c[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Ut.encodeWeights(this.getNamedWeights(t)),r=!1,s=null,o={modelTopology:this.toJSON(s,r),format:$U,generatedBy:`TensorFlow.js tfjs-layers v${xw}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let c="optimizer",{data:u,specs:l}=await Ut.encodeWeights(await this.optimizer.getWeights(),c);n.specs.push(...l),n.data=Ut.concatenateArrayBuffers([n.data,u])}return this.userDefinedMetadata!=null&&(zk(this.userDefinedMetadata,this.name,!0),o.userDefinedMetadata=this.userDefinedMetadata),o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){zk(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Es.className="Model";se.registerClass(Es);var BC=class extends Es{};BC.className="Functional";se.registerClass(BC);async function DU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=nd(n),s=Gr(r,t);if(e.weightsManifest!=null){let a=await Ut.loadWeights(e.weightsManifest,e.pathPrefix,s.weights.map(i=>i.originalName)),o={};for(let i of s.weights)o[i.originalName]=a[i.originalName];s.loadWeights(o),_e(a)}return s}async function FU(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Ut.getLoadHandlers(e,t);if(n.length===0)n.push(Ut.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return RU(e,void 0,t)}async function RU(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),s=r.modelTopology;s.model_config!=null&&(s=s.model_config);let a=n.strict==null?!0:n.strict,o=r.weightData!=null&&r.weightSpecs!=null&&a,i=Gr(nd(s),t,o),c=r.trainingConfig;if(c!=null&&i.loadTrainingConfig(c),r.userDefinedMetadata!=null&&i.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. 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compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Wr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Wr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Wr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let s,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");s=t}else w.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 o=new e(a);if(!(o instanceof Tc))throw new Pe(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of s){let u=Gr(i,void 0,r);r&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new U("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 U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Tc.className="Sequential";se.registerClass(Tc);function OU(e){return new Es(e)}function MU(e){return new Tc(e)}function WC(e){return xC(e)}function LU(e,t){_r.registerCallbackConstructor(e,t)}var Un=class extends se.Serializable{getConfig(){return{}}},VC=class extends Un{apply(e,t=1){return oV(e,t)}};VC.className="elu";se.registerClass(VC);var UC=class extends Un{apply(e){return lm(e)}};UC.className="selu";se.registerClass(UC);var GC=class extends Un{apply(e){return Xe(e)}};GC.className="relu";se.registerClass(GC);var HC=class extends Un{apply(e){return O(()=>Vu(6,Xe(e)))}};HC.className="relu6";se.registerClass(HC);var qC=class extends Un{apply(e){return e}};qC.className="linear";se.registerClass(qC);var jC=class extends Un{apply(e){return dr(e)}};jC.className="sigmoid";se.registerClass(jC);var KC=class extends 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e=="string"){let t={};return t.className=e,t.config={},Iy(t)}else return e instanceof Un?e:Iy(e)}function kw(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var tN=class extends se.Serializable{},Qd=class extends tN{constructor(e){super(),kw(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 O(()=>{let t=kt([1]);return this.hasL1&&(t=Y(t,ge(B(this.l1,Lt(e))))),this.hasL2&&(t=Y(t,ge(B(this.l2,Yd(e))))),W(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Qd.className="L1L2";se.registerClass(Qd);function zU(e){return kw(e),new Qd({l1:e!=null?e.l1:null,l2:0})}function BU(e){return kw(e),new Qd({l2:e!=null?e.l2:null,l1:0})}var Hk={l1l2:"L1L2"};function ut(e){return ew(e)}function qk(e,t={}){return Kd(e,se.SerializationMap.getMap().classNameMap,t,"regularizer")}function Tt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Hk?Hk[e]:e,config:{}};return qk(n)}else return e instanceof tN?e:qk(e)}var Sw=class extends Ue{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ce(e);let n=Xe(e);return this.maxValue!=null&&(n=en(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Sw.className="ReLU";se.registerClass(Sw);var Tw=class extends Ue{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ce(e);return Ld(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Tw.className="LeakyReLU";se.registerClass(Tw);var Cw=class extends 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e={alphaInitializer:Nt(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:jt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Cw.className="PReLU";se.registerClass(Cw);var Nw=class extends Ue{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Pe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ce(e);return Lu(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Nw.className="ELU";se.registerClass(Nw);var _w=class extends Ue{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Ce(e);return B(n,ce(An(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};_w.className="ThresholdedReLU";se.registerClass(_w);var Ew=class extends Ue{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Iw().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ce(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ew.className="Softmax";se.registerClass(Ew);function pc(e,t,n){if(typeof e=="number")return uo(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let s=e[r];if(!nV(s))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${s}`)}return e}function Hr(e,t,n,r,s=1){if(e==null)return e;let a=t+(t-1)*(s-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+r-1)/r)}function as(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+ua([n-t,0]);else if(r==="same")e=e*t;else throw new U(`Unsupport padding mode: ${r}.`);return e}function Aw(e,t){return O(()=>(Pt(t),t==="channelsFirst"?Ee(e,[0,2,3,1]):e))}function nN(e,t){return O(()=>(Pt(t),t==="channelsFirst"?Ee(e,[0,2,3,4,1]):e))}function WU(e,t,n,r=1,s="valid",a,o=1){return O(()=>{if(a==null&&(a=jr()),Pt(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ee(e,[0,2,1])),s==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Xf(e,t,r,s==="same"?"same":"valid","NWC",o);return n!=null&&(i=Yr(i,n)),i})}function jk(e,t,n,r=[1,1],s="valid",a,o,i=null){return O(()=>{if(a==null&&(a=jr()),Pt(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let c=Aw(e,a);if(s==="causal")throw new Pe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return c=Ic.conv2d({x:c,filter:t,strides:r,pad:s==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(c=Ee(c,[0,3,1,2])),c})}function VU(e,t,n,r=[1,1,1],s="valid",a,o){return O(()=>{if(a==null&&(a=jr()),Pt(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=nN(e,a);if(s==="causal")throw new Pe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=mx(i,t,r,s==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Yr(i,n)),a==="channelsFirst"&&(i=Ee(i,[0,4,1,2,3])),i})}var $w=class extends Ue{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",$w.verifyArgs(t),this.rank=e,Qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Pe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=pc(t.kernelSize,e,"kernelSize"),this.strides=pc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,br(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Pt(this.dataFormat),this.activation=da(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=St(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Kt(t.biasConstraint),this.biasRegularizer=Tt(t.biasRegularizer),this.activityRegularizer=Tt(t.activityRegularizer),this.dilationRate=pc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(ss("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!tw(e.kernelSize,"number",1,3))throw new U(`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:la(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:jt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ep=class extends $w{constructor(e,t){super(e,t),this.kernel=null,ep.verifyArgs(t),this.filters=t.filters,Qt(this.filters,"filters"),this.kernelInitializer=St(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Kt(t.kernelConstraint),this.kernelRegularizer=Tt(t.kernelRegularizer)}build(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return O(()=>{e=Ce(e);let n,r=this.bias==null?null:this.bias.read(),s=lC(this.activation.getClassName());if(s!=null&&this.rank===2)n=jk(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=WU(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=jk(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=VU(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Pe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=tt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=Hr(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:jt(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 U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},tp=class extends ep{constructor(e){super(2,e),tp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!tw(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};tp.className="Conv2D";se.registerClass(tp);var np=class extends ep{constructor(e){super(3,e),np.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 U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};np.className="Conv3D";se.registerClass(np);var Dw=class extends tp{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=tt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Ce(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=r[a],c=r[o],u=this.kernelSize[0],l=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=as(i,p,u,this.padding),f=as(c,d,l,this.padding),m=[s,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,1]));let g=Yf(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ee(g,[0,3,1,2])),this.bias!=null&&(g=Yr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=tt(e);let t=e.slice(),n,r,s;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3):(n=3,r=1,s=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],c=this.strides[1];return t[n]=this.filters,t[r]=as(t[r],i,a,this.padding),t[s]=as(t[s],c,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Dw.className="Conv2DTranspose";se.registerClass(Dw);var Fw=class extends np{constructor(e){if(super(e),this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=tt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{let n=Ce(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,s=r[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let c=r[i],u=r[a],l=r[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=as(c,f,p,this.padding),y=as(u,m,d,this.padding),v=as(l,g,h,this.padding),x=[s,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Ee(n,[0,2,3,4,1]));let k=gx(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Ee(k,[0,4,1,2,3])),this.bias!==null&&(k=Yr(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=tt(e);let t=e.slice(),n,r,s,a;this.dataFormat==="channelsFirst"?(n=1,r=2,s=3,a=4):(n=4,r=1,s=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],c=this.kernelSize[2],u=this.strides[0],l=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[r]=as(t[r],u,o,this.padding),t[s]=as(t[s],l,i,this.padding),t[a]=as(t[a],p,c,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Fw.className="Conv3DTranspose";se.registerClass(Fw);var rN=class extends ep{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("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 U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Tt(t.depthwiseRegularizer),this.depthwiseConstraint=Kt(t.depthwiseConstraint),this.pointwiseInitializer=St(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Tt(t.pointwiseRegularizer),this.pointwiseConstraint=Kt(t.pointwiseConstraint)}build(e){if(e=tt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${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 U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let o=0;o<this.rank;++o)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"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 zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return O(()=>{e=Ce(e);let n;if(this.rank===1)throw new Pe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ee(e,[0,2,3,1])),n=Ca(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Yr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ee(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=jt(this.depthwiseConstraint),e.pointwiseConstraint=jt(this.pointwiseConstraint),e}};rN.className="SeparableConv";var Rw=class extends rN{constructor(e){super(2,e)}};Rw.className="SeparableConv2D";se.registerClass(Rw);var Wm=class extends ep{constructor(e){super(1,e),Wm.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"&&!tw(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Wm.className="Conv1D";se.registerClass(Wm);var Pw=class extends Ue{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 O(()=>{if(e=Ce(e),this.dataFormat==="channelsLast"){let n=uh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return uh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=uh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return uh(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Pw.className="Cropping2D";se.registerClass(Pw);var Ow=class extends Ue{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,Pt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Q4(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return O(()=>{let n=Ce(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Ee(n,[0,2,3,1]);let s=this.size[0]*r[2],a=this.size[1]*r[3],o=this.interpolation==="nearest"?Br.resizeNearestNeighbor(n,[s,a]):Br.resizeBilinear(n,[s,a]);return Ee(o,[0,3,1,2])}else{let s=this.size[0]*r[1],a=this.size[1]*r[2];return this.interpolation==="nearest"?Br.resizeNearestNeighbor(n,[s,a]):Br.resizeBilinear(n,[s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};Ow.className="UpSampling2D";se.registerClass(Ow);function UU(e,t,n=[1,1],r="valid",s,a){return O(()=>{s==null&&(s=jr()),Pt(s);let o=Aw(e,s);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Sa(o,t,n,r==="same"?"same":"valid","NHWC",a),s==="channelsFirst"&&(o=Ee(o,[0,3,1,2])),o})}var Mw=class extends $w{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=St(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Kt(e.depthwiseConstraint),this.depthwiseRegularizer=Tt(e.depthwiseRegularizer)}build(e){if(e=tt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return O(()=>{e=Ce(e);let n=UU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Yr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=Hr(t,this.kernelSize[0],this.padding,this.strides[0]),a=Hr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,s,a]:[e[0],s,a,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=jt(this.depthwiseRegularizer),e}};Mw.className="DepthwiseConv2D";se.registerClass(Mw);function sN(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function s(a){return a==null||Array.isArray(a)?a:[a]}return t=s(t),n=s(n),{inputs:e,initialState:t,constants:n}}function aN(e,t,n,r=!1,s,a,o=!1,i=!1){return O(()=>{let c=t.shape.length;if(c<3)throw new U(`Input should be at least 3D, but is ${c}D.`);let u=[1,0].concat(qr(2,c));if(t=Ee(t,u),a!=null)throw new Pe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),s!=null&&(s=ce(ce(s,"bool"),"float32"),s.rank===c-1&&(s=Zt(s,-1)),s=Ee(s,u)),r&&(t=fr(t,0),s!=null&&(s=fr(s,0)));let l=[],p,d=n,h=t.shape[0],f=lt(t),m;s!=null&&(m=lt(s));for(let b=0;b<h;++b){let y=f[b],v=O(()=>e(y,d));if(s==null)p=v[0],d=v[1];else{let x=O(()=>{let k=m[b],S=de(rr(k),k),C=Y(B(v[0],k),B(d[0],S)),E=d.map(($,F)=>Y(B(v[1][F],k),B($,S)));return{output:C,newStates:E}});p=x.output,d=x.newStates}i&&l.push(p)}let g;return i&&(g=Ft(l,1)),[p,g,d]})}var ms=class extends Ue{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Gm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new zt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return qr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){qy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[r].concat(s)}else return r}computeMask(e,t){return O(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(s=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Pe("Constants support is not implemented in RNN yet.");qy(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new zt({shape:[n,null,...r]});let s=[e[0]].concat(e.slice(2));this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new U(`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(o=>new zt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new ks("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("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(r=>kt([n,r])):this.states_=[kt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>kt([n,r])):this.states_[0]=kt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`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()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let s=e[r],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,o=[n,a];if(!w.arraysEqual(s.shape,o))throw new U(`State ${r} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${s.shape}`);this.states_[r]=s}}this.states_=this.states_.map(r=>Jt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=sN(e,n,r,this.numConstants);e=s.inputs,n=s.initialState,r=s.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let c of n)this.stateSpec.push(new zt({shape:c.shape}));o=o.concat(this.stateSpec)}if(r!=null&&(t.constants=r,a=a.concat(r),this.numConstants=r.length),a[0]instanceof Vr){let c=[e].concat(a),u=this.inputSpec.concat(o),l=this.inputSpec;this.inputSpec=u;let p=super.apply(c,t);return this.inputSpec=l,p}else return super.apply(e,t)}call(e,t){return O(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;e=Ce(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 U(`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 o={training:r},c=aN((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),u=c[0],l=c[1],p=c[2];this.stateful&&this.resetStates(p,r);let d=this.returnSequences?l:u;return this.returnState?[d].concat(p):d})}getInitialState(e){return O(()=>{let t=kt(e.shape);return t=ge(t,[1,2]),t=Xd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Gy(t,[1,n]):t):this.cell.stateSize>1?[Gy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===ms.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),e),t)}static fromConfig(e,t,n={}){let r=t.cell,s=Gr(r,n);return new e(Object.assign(t,{cell:s}))}};ms.className="RNN";se.registerClass(ms);var rp=class extends Ue{},Vm=class extends rp{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,Qt(this.units,"units"),this.activation=da(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=kc([1,ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kc([1,ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=tt(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 O(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=pa({ones:()=>rr(e),rate:this.dropout,training:r,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=pa({ones:()=>rr(n),rate:this.recurrentDropout,training:r,dropoutFunc:this.dropoutFunc}));let s,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?s=cs(B(e,a),this.kernel.read()):s=cs(e,this.kernel.read()),this.bias!=null&&(s=Yr(s,this.bias.read())),o!=null&&(n=B(n,o));let i=Y(s,cs(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:la(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),recurrentConstraint:jt(this.recurrentConstraint),biasConstraint:jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},e),t)}};Vm.className="SimpleRNNCell";se.registerClass(Vm);var Lw=class extends ms{constructor(e){e.cell=new Vm(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return new e(t)}};Lw.className="SimpleRNN";se.registerClass(Lw);var Um=class extends rp{constructor(e){if(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",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Qt(this.units,"units"),this.activation=da(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=da(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=kc([1,ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kc([1,ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=tt(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 O(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=pa({ones:()=>rr(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=pa({ones:()=>rr(r),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,a=this.recurrentDropoutMask,o,i,c;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let u=cs(e,this.kernel.read());this.useBias&&(u=Yr(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=B(r,a[0]));let l=this.recurrentKernel.read(),[p,d]=zn(l,[2*this.units,this.units],l.rank-1),h=cs(r,p),[f,m,g]=zn(u,3,u.rank-1),[b,y]=zn(h,2,h.rank-1);o=this.recurrentActivation.apply(Y(f,b)),i=this.recurrentActivation.apply(Y(m,y));let v=cs(B(i,r),d);c=this.activation.apply(Y(g,v));let x=Y(B(o,r),B(Y(1,xt(o)),c));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:la(this.activation),recurrentActivation:la(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),recurrentConstraint:jt(this.recurrentConstraint),biasConstraint:jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},e),t)}};Um.className="GRUCell";se.registerClass(Um);var zw=class extends ms{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 Um(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};zw.className="GRU";se.registerClass(zw);var sp=class extends rp{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,Qt(this.units,"units"),this.activation=da(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=da(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=St(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Tt(e.kernelRegularizer),this.recurrentRegularizer=Tt(e.recurrentRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.kernelConstraint=Kt(e.kernelConstraint),this.recurrentConstraint=Kt(e.recurrentConstraint),this.biasConstraint=Kt(e.biasConstraint),this.dropout=kc([1,ua([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=kc([1,ua([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=tt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;r=new(t=class extends Pr{apply(i,c){let u=s.apply([a]),l=new $m().apply([a]),p=s.apply([a*2]);return Ak(Ak(u,l),p)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=pa({ones:()=>rr(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=pa({ones:()=>rr(r),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,c,u,l;0<this.dropout&&this.dropout<1&&(e=B(e,a[0]));let p=cs(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=B(r,o[0])),p=Y(p,cs(r,this.recurrentKernel.read())),this.useBias&&(p=Yr(p,this.bias.read()));let[d,h,f,m]=zn(p,4,p.rank-1);i=this.recurrentActivation.apply(d),c=this.recurrentActivation.apply(h),u=Y(B(c,s),B(i,this.activation.apply(f))),l=this.recurrentActivation.apply(m);let g=B(l,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:la(this.activation),recurrentActivation:la(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),recurrentConstraint:jt(this.recurrentConstraint),biasConstraint:jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},e),t)}};sp.className="LSTMCell";se.registerClass(sp);var Bw=class extends ms{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 sp(e),super(e)}call(e,t){return O(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Bw.className="LSTM";se.registerClass(Bw);var Gm=class extends rp{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 O(()=>{e=e;let n=e.slice(1),r=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?r.push(n.splice(0,o.stateSize.length)):r.push(n.splice(0,1));r.reverse();let s=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=r[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),s.push(a.slice(1))}n=[];for(let o of s.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){qy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Za(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),r={cells:this.cells.map(t)};return Object.assign(Object.assign({},e),r)}static fromConfig(e,t,n={}){let r=[];for(let s of t.cells)r.push(Gr(s,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return jy(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,s=e.splice(r);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}lw(t)}};Gm.className="StackedRNNCells";se.registerClass(Gm);function pa(e){let{ones:t,rate:n,training:r=!1,count:s=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):bC(t(),n),i=()=>Zd(o,t,r);return!s||s<=1?Jt(i().clone()):Array(s).fill(void 0).map(i).map(u=>Jt(u.clone()))}var GU=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var s=0,r=Object.getOwnPropertySymbols(e);s<r.length;s++)t.indexOf(r[s])<0&&Object.prototype.propertyIsEnumerable.call(e,r[s])&&(n[r[s]]=e[r[s]]);return n},oN=class extends ms{constructor(e){if(e.unroll)throw new Pe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Pe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new zt({ndim:5})]}call(e,t){return O(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,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 O(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)],a=kt(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){O(()=>{if(!this.stateful)throw new ks("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),s=[r[0],...r.slice(2)];if(n[0]==null)throw new U("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(()=>kt(s)):this.states_=[kt(s)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>kt(s)):this.states_[0]=kt(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`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()):_e(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],c=s;if(!w.arraysEqual(i.shape,c))throw new U(`State ${o} is incompatible with layer ${this.name}: expected shape=${c}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Jt(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:s,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",c=e[i?3:2],u=e[i?4:3],l=Hr(c,r[0],s,a[0],o[0]),p=Hr(u,r[1],s,a[1],o[1]);return[...e.slice(0,2),...i?[n,l,p]:[l,p,n]]}};oN.className="ConvRNN2D";var Hm=class extends sp{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:s,dataFormat:a,dilationRate:o}=e;super(Object.assign(Object.assign({},e),{units:t})),this.filters=t,Qt(this.filters,"filters"),this.kernelSize=pc(n,2,"kernelSize"),this.kernelSize.forEach(i=>Qt(i,"kernelSize")),this.strides=pc(r||1,2,"strides"),this.strides.forEach(i=>Qt(i,"strides")),this.padding=s||"valid",br(this.padding),this.dataFormat=a||"channelsLast",Pt(this.dataFormat),this.dilationRate=pc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Qt(i,"dilationRate"))}build(e){var t;e=tt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],s=4,a=this.kernelSize.concat([r,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let c=this.biasInitializer,u=this.filters;i=new(t=class extends Pr{apply(p,d){let h=c.apply([u]),f=Qn([u]),m=c.apply([u*2]);return nw([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return O(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],s=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=pa({ones:()=>rr(r),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,c=(Z,J,ee)=>!J||!J[ee]?Z:B(J[ee],Z),u=c(r,i,0),l=c(r,i,1),p=c(r,i,2),d=c(r,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=pa({ones:()=>rr(s),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=c(s,h,0),m=c(s,h,1),g=c(s,h,2),b=c(s,h,3),y=3,[v,x,k,S]=zn(this.kernel.read(),o,y),[C,E,$,F]=this.useBias?zn(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,v,C,this.padding),l=this.inputConv(l,x,E,this.padding),p=this.inputConv(p,k,$,this.padding),d=this.inputConv(d,S,F,this.padding);let[A,R,T,L]=zn(this.recurrentKernel.read(),o,y);f=this.recurrentConv(f,A),m=this.recurrentConv(m,R),g=this.recurrentConv(g,T),b=this.recurrentConv(b,L);let V=this.recurrentActivation.apply(Y(u,f)),G=this.recurrentActivation.apply(Y(l,m)),j=Y(B(G,a),B(V,this.activation.apply(Y(p,g)))),H=B(this.recurrentActivation.apply(Y(d,b)),this.activation.apply(j));return[H,H,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=GU(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),r)}inputConv(e,t,n,r){let s=Dt(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Yr(s,n,this.dataFormat):s}recurrentConv(e,t){return Dt(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Hm.className="ConvLSTM2DCell";se.registerClass(Hm);var Ww=class extends oN{constructor(e){let t=new Hm(e);super(Object.assign(Object.assign({},e),{cell:t}))}static fromConfig(e,t){return new e(t)}};Ww.className="ConvLSTM2D";se.registerClass(Ww);var qm=class extends Ue{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,s=this.getNoiseShape(n);return Zd(()=>bC(n,this.rate,s,this.seed),()=>n,r)}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()}};qm.className="Dropout";se.registerClass(qm);var Vw=class extends qm{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Vw.className="SpatialDropout1D";se.registerClass(Vw);var Uw=class extends Ue{constructor(e){if(super(e),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,Qt(this.units,"units"),this.activation=da(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=St(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=St(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Kt(e.kernelConstraint),this.biasConstraint=Kt(e.biasConstraint),this.kernelRegularizer=Tt(e.kernelRegularizer),this.biasRegularizer=Tt(e.biasRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=tt(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=tt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e),r=lC(this.activation.getClassName()),s;return r!=null?s=cs(n,this.kernel.read(),r,this.bias?this.bias.read():null):(s=cs(n,this.kernel.read()),this.bias!=null&&(s=Yr(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:la(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),biasConstraint:jt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Uw.className="Dense";se.registerClass(Uw);var Gw=class extends Ue{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=tt(e);for(let t of e.slice(1))if(t==null)throw new U(`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],aa(e,1)]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let s=2;s<n.rank;++s)r.push(s);r.push(1),n=Ee(n,r)}return aV(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Gw.className="Flatten";se.registerClass(Gw);var Hw=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.activation=da(e.activation)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e);return this.activation.apply(n)})}getConfig(){let e={activation:la(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Hw.className="Activation";se.registerClass(Hw);var qw=class extends Ue{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 O(()=>(e=Ce(e),rV(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};qw.className="RepeatVector";se.registerClass(qw);var jw=class extends Ue{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),s=1,a=null;for(let i=0;i<r.length;++i){let c=r[i];if(this.isUnknown(c))if(a===null)a=i;else throw new U("Can only specifiy one unknown dimension.");else s*=c}let o=aa(e);if(a!==null){if(s===0||o%s!==0)throw new U(n);r[a]=o/s}else if(o!==s)throw new U(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e),r=n.shape,s=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return W(n,s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};jw.className="Reshape";se.registerClass(jw);var Kw=class extends Ue{constructor(e){if(super(e),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=qr(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=tt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return Ee(Ce(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Kw.className="Permute";se.registerClass(Kw);var Xw=class extends Ue{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ce(e),r=-1;return Ql(co(n,this.maskValue),r)}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e),r=-1,s=!0,a=Ql(co(n,this.maskValue),r,s);return B(n,ce(a,n.dtype))})}};Xw.className="Masking";se.registerClass(Xw);var Yw=class extends Ue{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(vt(e.inputLength))}this.inputDim=e.inputDim,Qt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Qt(this.outputDim,"outputDim"),this.embeddingsInitializer=St(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Tt(e.embeddingsRegularizer),this.activityRegularizer=Tt(e.activityRegularizer),this.embeddingsConstraint=Kt(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 O(()=>this.maskZero?(e=Ce(e),co(e,qe(e))):null)}computeOutputShape(e){if(e=tt(e),this.inputLength==null)return[...e,this.outputDim];let t=vt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let s=t[r],a=e[r+1];if(s!=null&&a!=null&&s!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return O(()=>{this.invokeCallHook(e,t);let n=Ce(e);n.dtype!=="int32"&&(n=vi(n,"int32"));let r=gC(this.embeddings.read(),W(n,[n.size]));return W(r,tt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:jt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Yw.className="Embedding";se.registerClass(Yw);var wi=class extends Ue{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Pe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let s=e[e.length-t.length+r],a=t[r];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[tt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};n0.className="Concatenate";se.registerClass(n0);function Al(e,t){for(;e<0;)e+=t;return e}function HU(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Pe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Pe("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,s=t.shape.length;n==null&&(n=[r-1,s-2]);let a=n;return O(()=>{let o;if(r>s){o=r-s;let c=[];for(let u=0;u<o;++u)c.push(1);t=W(t,t.shape.concat(c))}else if(s>r){o=s-r;let c=[];for(let u=0;u<o;++u)c.push(1);e=W(e,e.shape.concat(c))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ge(B(e,t),a[0]):i=ge(B(Ee(e,[1,0]),t),a[1]);else{let c=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=De(e,t,c,u)}if(o>0){let c;r>s?c=r+s-3:c=r-1;let u=[];for(let l=c;l<c+o;++l)u.push(l);i=Na(i,u)}return i.shape.length===1&&(i=Zt(i,1)),i})}var r0=class extends wi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Pe("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new U(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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Ue{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 O(()=>{this.invokeCallHook(e,t);let n=Ce(e);return Zd(()=>Y(Am(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};s0.className="GaussianNoise";se.registerClass(s0);var a0=class extends Ue{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 O(()=>{this.invokeCallHook(e,t);let n=Ce(e);return this.rate>0&&this.rate<1?Zd(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return B(n,Am(n.shape,1,s))},()=>n,t.training||!1):n})}};a0.className="GaussianDropout";se.registerClass(a0);var o0=class extends Ue{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return O(()=>{let n=t.training==null?!1:t.training,r=Ce(e),s=r.shape,a=s.length,o=qr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let c=uo(1,a);c[i]=s[i];let u=o.slice();u.sort();let l=!w.arraysEqual(u,qr(0,a).slice(0,a-1)),p=()=>{if(l){let b=W(this.movingMean.read(),c),y=W(this.movingVariance.read(),c),v=this.center?W(this.beta.read(),c):null,x=this.scale?W(this.gamma.read(),c):null;return rd(r,b,y,v,x,this.epsilon)}else return rd(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=KU(r,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(b,y,v)=>{O(()=>{let x=1-v,k=b.read(),S=B(de(k,y),x);b.write(de(k,S))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer),betaConstraint:jt(this.betaConstraint),gammaConstraint:jt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};i0.className="BatchNormalization";se.registerClass(i0);var c0=class extends Ue{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=St(e.betaInitializer||"zeros"),this.gammaInitializer=St(e.gammaInitializer||"ones"),this.betaRegularizer=Tt(e.betaRegularizer),this.gammaRegularizer=Tt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=tt(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!==sa(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Ce(e),r=n.shape,s=r.length;return O(()=>{let{mean:o,variance:i}=Wd(n,this.axis,!0),c=uo(1,s);for(let f of this.axis)c[f]=r[f];let u=f=>f!=null&&f.shape.length!==s?W(f,c):f,l=this.scale?u(this.gamma.read()):null,p=this.center?u(this.beta.read()):null,d=[],h=[];for(let f=0;f<s;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),h.push(1)):(d.push(1),h.push(r[f]));return o=Ln(o,d),i=Ln(i,d),l!=null&&(l=Ln(l,h)),p!=null&&(p=Ln(p,h)),rd(n,o,i,p,l,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};c0.className="LayerNormalization";se.registerClass(c0);function XU(e,t,n){return O(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=jr()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],gr(e,r)})}var u0=class extends Ue{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?jr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=tt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return O(()=>XU(Ce(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};u0.className="ZeroPadding2D";se.registerClass(u0);function jm(e,t,n,r,s,a){return O(()=>{Pt(s),pC(a),br(r),n==null&&(n=[1,1]),r==null&&(r="valid"),s==null&&(s=jr()),a==null&&(a="max"),e=Aw(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Rt(e,t,n,i):o=mr(e,t,n,i),s==="channelsFirst"&&(o=Ee(o,[0,3,1,2])),o})}function iN(e,t,n,r,s,a){return O(()=>{Pt(s),pC(a),br(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),s==null&&(s=jr()),a==null&&(a="max"),e=nN(e,s);let o,i=r==="same"?"same":"valid";return a==="max"?o=Dx(e,t,n,i):o=sx(e,t,n,i),s==="channelsFirst"&&(o=Ee(o,[0,4,1,2,3])),o})}var cN=class extends Ue{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 U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Qt(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 U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,br(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=tt(e);let t=Hr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return O(()=>{this.invokeCallHook(e,t),e=Xd(Ce(e),2);let n=this.poolingFunction(Ce(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Na(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},l0=class extends cN{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),br(r),jm(e,t,n,r,s,"max")}};l0.className="MaxPooling1D";se.registerClass(l0);var d0=class extends cN{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),br(r),jm(e,t,n,r,s,"avg")}};d0.className="AveragePooling1D";se.registerClass(d0);var uN=class extends Ue{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 U(`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];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),br(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Hr(t,this.poolSize[0],this.padding,this.strides[0]),n=Hr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(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}},p0=class extends uN{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),br(r),jm(e,t,n,r,s,"max")}};p0.className="MaxPooling2D";se.registerClass(p0);var h0=class extends uN{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),br(r),jm(e,t,n,r,s,"avg")}};h0.className="AveragePooling2D";se.registerClass(h0);var lN=class extends Ue{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 U(`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];Qt(this.poolSize,"poolSize"),Qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),br(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=tt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Hr(t,this.poolSize[0],this.padding,this.strides[0]),n=Hr(n,this.poolSize[1],this.padding,this.strides[1]),r=Hr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return O(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ce(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}},f0=class extends lN{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),br(r),iN(e,t,n,r,s,"max")}};f0.className="MaxPooling3D";se.registerClass(f0);var m0=class extends lN{constructor(e){super(e)}poolingFunction(e,t,n,r,s){return Pt(s),br(r),iN(e,t,n,r,s,"avg")}};m0.className="AveragePooling3D";se.registerClass(m0);var dN=class extends Ue{constructor(e){super(e),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Pe}},g0=class extends dN{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=Ce(e);return Ct(n,1)})}};g0.className="GlobalAveragePooling1D";se.registerClass(g0);var b0=class extends dN{constructor(e){super(e||{})}call(e,t){return O(()=>{let n=Ce(e);return pr(n,1)})}};b0.className="GlobalMaxPooling1D";se.registerClass(b0);var pN=class extends Ue{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Pt(this.dataFormat),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Pe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},y0=class extends pN{call(e,t){return O(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?Ct(n,[1,2]):Ct(n,[2,3])})}};y0.className="GlobalAveragePooling2D";se.registerClass(y0);var v0=class extends pN{call(e,t){return O(()=>{let n=Ce(e);return this.dataFormat==="channelsLast"?pr(n,[1,2]):pr(n,[2,3])})}};v0.className="GlobalMaxPooling2D";se.registerClass(v0);var hN=class extends Ue{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,s=Gr(r,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},x0=class extends hN{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=tt(e),e.length<3)throw new U(`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=tt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return O(()=>(e=Ce(e),aN((a,o)=>[Ce(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};x0.className="TimeDistributed";se.registerClass(x0);function YU(e){yi(J4,"BidirectionalMergeMode",e)}var ZU="concat",w0=class extends hN{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Gr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Gr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?ZU:e.mergeMode,YU(this.mergeMode),e.weights)throw new Pe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):Mn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let s=sN(e,n,r,this.numConstants);if(e=s.inputs,n=s.initialState,r=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let c=n.length;if(c%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(l=>new zt({shape:l.shape}));this.forwardLayer.stateSpec=u.slice(0,c/2),this.backwardLayer.stateSpec=u.slice(c/2),o.push(...u)}if(r!=null)throw new Pe("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof Vr;for(let c of a)if(c instanceof Vr!==i)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let c=[e].concat(a),u=this.inputSpec.concat(o),l=this.inputSpec;this.inputSpec=u;let p=super.apply(c,t);return this.inputSpec=l,p}else return super.apply(e,t)}call(e,t){return O(()=>{let n=t.initialState,r,s;if(n==null)r=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),c=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:c}))}let a;this.returnState&&(Array.isArray(r)&&(a=r.slice(1).concat(s.slice(1))),r=r[0],s=s[0]),this.returnSequences&&(s=fr(s,1));let o;return this.mergeMode==="concat"?o=nw([r,s]):this.mergeMode==="sum"?o=Y(r,s):this.mergeMode==="ave"?o=B(.5,Y(r,s)):this.mergeMode==="mul"?o=B(r,s):this.mergeMode==null&&(o=[r,s]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Za(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Za(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Gr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Pe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};w0.className="Bidirectional";se.registerClass(w0);var I0=class extends Ue{constructor(e){super(e),this.scale=e.scale,e.offset?this.offset=e.offset:this.offset=0}getConfig(){let e={scale:this.scale,offset:this.offset},t=super.getConfig();return Object.assign(e,t),e}call(e,t){return 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s=I("x",e,t,n),a=I("weights",e,t,n),o=I("size",e,t,n);return[r.bincount(s,a,o)];case"DenseBincount":{let i=I("x",e,t,n),c=I("weights",e,t,n),u=I("size",e,t,n),l=I("binaryOutput",e,t,n);return[r.denseBincount(i,c,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},S6=(e,t,n,r=ln)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),a=I("axis",e,t,n),o=I("tensors",e,t,n);return o=o.slice(0,s),[r.concat(o,a)]}case"Gather":{let s=I("x",e,t,n),a=I("indices",e,t,n);return[r.gather(s,r.cast(a,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),a=I("batchDims",e,t,n),o=I("x",e,t,n),i=I("indices",e,t,n);return[r.gather(o,r.cast(i,"int32"),s,a)]}case"Reverse":{let s=I("dims",e,t,n),a=[];for(let i=0;i<s.length;i++)s[i]&&a.push(i);let o=I("x",e,t,n);return[r.reverse(o,a)]}case"ReverseV2":{let s=I("axis",e,t,n),a=I("x",e,t,n);return[r.reverse(a,s)]}case"Slice":{let s=I("begin",e,t,n),a=I("size",e,t,n);return[r.slice(I("x",e,t,n),s,a)]}case"StridedSlice":{let 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File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(a)?a.then(o=>[].concat(o)):[].concat(a)}var n1=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${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 r1(e,t,n,r){let s=new Set,a=[],o=null,i=null,c=new Set,u=Object.keys(e).map(d=>Zn(d)[0]),l=[];r!=null&&(l=r.map(d=>Zn(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((HN(d)||F6(d)||R6(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>s.has(h))),s.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&l.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{c.has(h.name)||(c.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function E6(e,t,n){let{usedNodes:r,inputs:s}=n,a=[],o=Object.keys(s).map(l=>Zn(l)[0]).map(l=>e.nodes[l]),i=e.initNodes;o.forEach(l=>{r.has(l.name)&&a.push(l)}),e.weights.forEach(l=>{r.has(l.name)&&a.push(l)}),i!=null&&i.forEach(l=>{r.has(l.name)&&a.push(l)});let c=new Set,u=[];for(;a.length>0;){let l=a.pop();c.add(l.name),t[l.name]||u.push(l),l.children.forEach(p=>{!c.has(p.name)&&r.has(p.name)&&p.inputs.every(d=>c.has(d.name))&&a.push(p)})}return u}var A6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],$6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],D6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function HN(e){return A6.indexOf(e.op)>=0}function F6(e){return $6.indexOf(e.op)>=0}function R6(e){return D6.indexOf(e.op)>=0}var lv=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new lv(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(s=>s.name).sort(),r=t.map(s=>s.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=r1(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){let o=t.map(c=>c.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${r}]`)}return E6(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(l=>this.graph.nodes[Zn(l)[0]]),s=t.map(l=>Zn(l)[0]),a=s.map(l=>this.graph.nodes[l]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(r,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let c={},u={};return O(()=>{let l=new n1(this.weightMap,c,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=Zn(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=t1(m,p,l,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,l,d,s,h)}}return this.parent==null&&l.dispose(d),t.map(f=>Sn(f,p,l))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let c=OH(i.name,n,r);c!=null&&c.forEach(u=>{if(u&&!u.kept&&!s.has(u.id)){let l=o[u.id];if(l===1){if(!this.keepTensorForDebug)u.dispose();else{let[p,d]=os(t.name,r);this.intermediateTensors[p]?this.intermediateTensors[p][d]=u:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=u)}delete o[u.id]}else l!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,r={},s={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=q().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let a=new n1(this.weightMap,r,s,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(u=>Sn(u,this.tensorsMap,a)),i=o.map(u=>u.id),c=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...i,...c,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let r=e.reduce((s,a,o)=>(s[this.inputs[o].name]=a,s),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let s=Object.keys(e),a=s.map(y=>this.graph.nodes[Zn(y)[0]]),o=n.map(y=>Zn(y)[0]),i=o.map(y=>this.graph.nodes[y]);i.length===0&&(i=this._outputs);let{usedNodes:c,missingInputs:u,dynamicNode:l,syncInputs:p}=r1(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(y=>{let[v,x]=Zn(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,o,f,c);await Promise.all(y)}l==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=i.filter(y=>!HN(y)&&!Sn(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw l!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${u}]. ${y}`)}return h}processStack(e,t,n,r,s,a,o,i,c){let u=[];for(;t.length>0;){let l=t.pop();n.currentContext=l.contexts;let p="";if(l.node.op==="Enter"&&I("isConstant",l.node,r,n)&&([p]=os(l.node.name,n)),r[l.node.name]==null){let d=t1(l.node,r,n,this._resourceManager);p||([p]=os(l.node.name,n));let h=n.currentContext;w.isPromise(d)?u.push(d.then(f=>(r[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,l.node,r,n,a,o,i),this.processChildNodes(l.node,t,n,r,s,c),f))):(r[p]=d,this.checkTensorForDisposal(p,l.node,r,n,a,o,i),this.processChildNodes(l.node,t,n,r,s,c))}else this.processChildNodes(l.node,t,n,r,s,c)}return u}processChildNodes(e,t,n,r,s,a){e.children.forEach(o=>{let[i]=os(o.name,n);s[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(c=>!!Sn(c,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(c=>!!Sn(c,r,n))&&(s[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Zn(t),s=this.graph.nodes[r];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,c)=>a[c]===-1||a[c]===i);w.assert(o,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&w.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Zn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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[n]=Zn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},P6=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},O6="?tfjs-format=file",M6="model.json",A0=class{constructor(e,t={},n=Ut){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new P6}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return w.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new lv(Zk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Zk.Instance.transformGraph(e.modelInitializer);this.initializer=new lv(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){let n=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){let r=n instanceof Te?[n]:n,s={};return r.forEach((a,o)=>s[this.structuredOutputKeys[o]]=a),s}return n}normalizeInputs(e){if(!(e instanceof Te)&&!Array.isArray(e)){if(this.signature!=null&&this.signature.inputs!=null)for(let r in this.signature.inputs){let s=this.signature.inputs[r];s.resourceId!=null&&(e[r]=this.resourceIdToCapturedInput[s.resourceId])}return e}e=Array.isArray(e)?e:[e];let t=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+t!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-t} non-resource placeholders, while there are ${e.length} input tensors provided.`);let n=0;return this.inputNodes.reduce((r,s)=>{let a=this.signature?this.signature.inputs[s]:null;return a!=null&&a.resourceId!=null?r[s]=this.resourceIdToCapturedInput[a.resourceId]:r[s]=e[n++],r},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=Object.keys(this.initializerSignature.outputs);for(let n=0;n<t.length;n++){let r=t[n],s=this.initializerSignature.outputs[r];this.resourceIdToCapturedInput[s.resourceId]=e[n]}}}execute(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&_e(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function L6(e,t={},n=Ut){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof e=="string"&&(e=B6(e));let r=new A0(e,t,n);return await r.load(),r}function z6(e){if(e==null)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let t;if(e instanceof Array){let[r,s]=e;if(!r)throw new Error("modelJSON must be the first element of the array");if(!s||!(s instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in r))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in r))throw new Error("Model JSON is missing 'weightsManifest'");let a=Ut.getWeightSpecs(r.weightsManifest),o=Ut.getModelArtifactsForJSONSync(r,a,s);t=Ut.fromMemorySync(o)}else if("load"in e)t=e;else if("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e)t=Ut.fromMemorySync(e);else throw new Error("Unknown model format");let n=new A0(t);return n.load(),n}function B6(e){return e.endsWith("/")||(e=e+"/"),`${e}${M6}${O6}`}var 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At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!q().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new n_(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,s)=>n.set(r,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),Cn(n,t)}},r_=class extends tn{constructor(e,t){if(super(),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=Ke([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-r)/2,o=s+n,i=r+a;this.cropBox=$r([a,s,i,o],[1,4])}else this.cropBox=$r([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!q().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new r_(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.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=fi.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return O(()=>{let t=Zt(ce(e,"float32"),0),n;n=Br.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return W(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},s_=class{},a_=class extends tn{split(e){return new fq(this,e)}},fq=class extends a_{constructor(e,t){super(),this.upstream=e,this.impl=new mq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mq=class extends F0{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},gq=class extends tn{decodeUTF8(){return new bq(this)}},bq=class extends a_{constructor(e){super(),this.upstream=e,this.impl=new yq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yq=class extends F0{constructor(e){if(super(),this.upstream=e,q().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=G1();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return q().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},o_=class extends gq{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(q().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,r)));else{let s=new FileReader;s.onload=o=>{let i=s.result;if(i instanceof 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s_{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return i_(this.url)?new c_(this.url,this.fileOptions).iterator():vq(this.url,this.fileOptions)}};function wq(e,t={}){return new t_(new u_(e),t)}function Iq(e){let t=D0(e);return Yn(async()=>t)}function kq(e){return Yn(async()=>{let t=await e();return D0(()=>t.next())})}async function Sq(e,t){return r_.create(e,t)}async function Tq(e){return n_.create(e)}var Cq="4.0.0";function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Nq=fs.whereImpl,R0=class extends ld{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new uf(this,Er())}nextDataId(){return R0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,q().get("IS_NODE")&&N.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. 
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Jn(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.data.get(r.dataId).values,o=n.data.get(s.dataId).values,i=n.makeTensorInfo(r.shape,"complex64"),c=n.data.get(i.dataId);return c.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",a),imag:n.makeTensorInfo(s.shape,"float32",o)},i}var Aq={kernelName:gf,backendName:"cpu",kernelFunc:Jn};function Jh(e,t,n="float32"){if(n==="complex64"){let s=Jh(e,t,"float32"),a=Jh(e,t,"float32");return Jn({inputs:{real:s,imag:a},backend:e})}let r=w.makeZerosTypedArray(w.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function ds(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var $q={kernelName:Oo,backendName:"cpu",kernelFunc:ds};function lo(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.data.get(r.dataId).complexTensorInfos.real,a=n.data.get(s.dataId).values;return n.makeTensorInfo(s.shape,s.dtype,a)}var Dq={kernelName:Lf,backendName:"cpu",kernelFunc:lo};function p_(e,t,n,r){if(r==="int32"){let s=Int32Array.from(e);return[t,"int32",s]}if(r==="bool"){let s=w.toTypedArray([0],n),[a,o]=Vt((i,c)=>i!==c?1:0)(t,[],e,s,"bool");return[o,"bool",a]}throw new Error(`Error in Cast: failed to cast ${n} to ${r}`)}function ha(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return ds({inputs:{x:s},backend:n});let l=Jh(n,s.shape,s.dtype),p=ha({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),d=Jn({inputs:{real:p,imag:l},backend:n});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(p),d}if(s.dtype==="complex64"){let l=lo({inputs:{input:s},backend:n}),p=ha({inputs:{x:l},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(l),p}if(!w.hasEncodingLoss(s.dtype,a)){let l=ds({inputs:{x:s},backend:n});return{dataId:l.dataId,shape:l.shape,dtype:a}}let o=n.data.get(s.dataId).values,[i,c,u]=p_(o,s.shape,s.dtype,a);return n.makeTensorInfo(i,c,u)}var Fq={kernelName:wo,backendName:"cpu",kernelFunc:ha};function nn(e,t,n,r){return n==null?({inputs:s,backend:a})=>{let{a:o,b:i}=s,c=a;be([o,i],e);let u=c.data.get(o.dataId).values,l=c.data.get(i.dataId).values,p=o.dtype==="string"?N.fromUint8ToStringArray(u):u,d=o.dtype==="string"?N.fromUint8ToStringArray(l):l,h=r||o.dtype,[f,m]=t(o.shape,i.shape,p,d,h);return c.makeTensorInfo(m,h,f)}:({inputs:s,backend:a})=>{let{a:o,b:i}=s,c=a;if(o.dtype==="complex64"||i.dtype==="complex64"){let u=ha({inputs:{x:o},backend:c,attrs:{dtype:"complex64"}}),l=c.data.get(u.dataId),p=l.complexTensorInfos.real,d=l.complexTensorInfos.imag,h=c.data.get(p.dataId).values,f=c.data.get(d.dataId).values,m=ha({inputs:{x:i},backend:c,attrs:{dtype:"complex64"}}),g=c.data.get(m.dataId),b=g.complexTensorInfos.real,y=g.complexTensorInfos.imag,v=c.data.get(b.dataId).values,x=c.data.get(y.dataId).values,[k,S,C]=n(o.shape,i.shape,h,f,v,x),E=c.makeTensorInfo(C,"float32",k),$=c.makeTensorInfo(C,"float32",S),F=Jn({inputs:{real:E,imag:$},backend:c});return c.disposeIntermediateTensorInfo(u),c.disposeIntermediateTensorInfo(m),c.disposeIntermediateTensorInfo(E),c.disposeIntermediateTensorInfo($),F}else{let u=c.data.get(o.dataId).values,l=c.data.get(i.dataId).values,p=r||o.dtype,[d,h]=t(o.shape,i.shape,u,l,p);return c.makeTensorInfo(h,p,d)}}}function P0(e){return(t,n,r,s,a,o)=>{let i=N.assertAndGetBroadcastShape(t,n),c=w.sizeFromShape(i),u=i.length,l=w.computeStrides(i),p=w.getTypedArrayFromDType("float32",c),d=w.getTypedArrayFromDType("float32",c),h=N.getBroadcastDims(t,i),f=N.getBroadcastDims(n,i),m=N.mergeRealAndImagArrays(r,s),g=N.mergeRealAndImagArrays(a,o),b=t.length,y=w.computeStrides(t),v=n.length,x=w.computeStrides(n);if(h.length+f.length===0)for(let k=0;k<p.length;k++){let S=k%m.length,C=k%g.length,E=e(m[S*2],m[S*2+1],g[C*2],g[C*2+1]);p[k]=E.real,d[k]=E.imag}else for(let k=0;k<p.length;k++){let S=w.indexToLoc(k,u,l),C=S.slice(-b);h.forEach(R=>C[R]=0);let E=w.locToIndex(C,b,y),$=S.slice(-v);f.forEach(R=>$[R]=0);let F=w.locToIndex($,v,x),A=e(m[E*2],m[E*2+1],g[F*2],g[F*2+1]);p[k]=A.real,d[k]=A.imag}return[p,d,i]}}var h_=Vt((e,t)=>e+t),Rq=P0((e,t,n,r)=>({real:e+n,imag:t+r})),_c=nn(ba,h_,Rq),Pq={kernelName:ba,backendName:"cpu",kernelFunc:_c};function O0(e,t,n,r,s){let a=w.sizeFromShape(r),o=w.makeZerosTypedArray(s,n);for(let i=0;i<e.length;i++){let c=e[i];if(c<0)throw new Error("Input x must be non-negative!");c>=s||(a>0?o[c]+=t[i]:o[c]+=1)}return o}function f_(e,t,n,r=!1){let s=e.shape[0],a=e.shape[1],o=Me([s,n],t.dtype);for(let i=0;i<s;i++)for(let c=0;c<a;c++){let u=e.get(i,c);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(r?o.set(1,i,u):t.size>0?o.set(o.get(i,u)+t.get(i,c),i,u):o.set(o.get(i,u)+1,i,u))}return o}function _a(e){return(t,n,r)=>{let s=w.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)s[a]=e(t[a],r);return s}}function at(e,t,n){return({inputs:r,attrs:s,backend:a})=>{let{x:o}=r;if(be(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,c=i.data.get(o.dataId).values,u=w.sizeFromShape(o.shape),l=n||o.dtype,p=w.getArrayFromDType(l,u);for(let d=0;d<u;++d)p[d]=t(c[d],s);return i.makeTensorInfo(o.shape,l,p)}}function Ku(e,t,n){return({inputs:r,attrs:s,backend:a})=>{let{x:o}=r;if(be(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,c=i.data.get(o.dataId).values,u=n||o.dtype,l=t(c,u,s);return i.makeTensorInfo(o.shape,u,l)}}var m_=_a(e=>Math.ceil(e)),Oq=Ku(Io,m_),Mq={kernelName:Io,backendName:"cpu",kernelFunc:Oq};function M0(e,t,n,r){let s=w.getArrayFromDType(n,w.sizeFromShape(t));if(r&&n!=="string"){let a=0;e.forEach(o=>{let i=w.sizeFromShape(o.shape);s.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?N.fromUint8ToStringArray(o.vals):o.vals,c=0;for(let u=0;u<o.shape[0];++u){let l=u*t[1]+a;for(let p=0;p<o.shape[1];++p)s[l+p]=i[c++]}a+=o.shape[1]})}return s}var g_=Vt((e,t)=>e===t?1:0),b_=nn(Xc,g_,null,"bool"),Lq={kernelName:Xc,backendName:"cpu",kernelFunc:b_},y_=_a(e=>Math.exp(e)),v_=Ku($o,y_,"float32"),zq={kernelName:$o,backendName:"cpu",kernelFunc:v_},x_=_a(e=>Math.expm1(e)),Bq=Ku(Zc,x_),Wq={kernelName:Zc,backendName:"cpu",kernelFunc:Bq},w_=_a(e=>Math.floor(e)),Vq=Ku(Do,w_),Uq={kernelName:Do,backendName:"cpu",kernelFunc:Vq};function I_(e,t,n,r,s,a,o,i,c){let u=Me([r,a],n);for(let l=0;l<r;l++){let p=[],d=0;for(let h=0;h<s;h++){let f=e[l*s+h];d+=f*o[h],p.push(f)}if(d<0||d>=c/a)throw new Error(`Invalid indices: ${p} does not index into ${i}`);for(let h=0;h<a;h++)u.values[l*a+h]=t.get(...t.indexToLoc(d*a+h))}return u}function k_(e,t,n){let r=Me(n,e.dtype);for(let s=0;s<r.size;++s){let o=r.indexToLoc(s).slice(),i=o[0],c=o[2],u=t.locToIndex([i,c]);o[2]=t.values[u];let l=e.locToIndex(o);0<=l&&l<e.values.length&&(r.values[s]=e.values[l])}return r}var S_=Vt((e,t)=>e>t?1:0),Gq=nn(tu,S_,null,"bool"),Hq={kernelName:tu,backendName:"cpu",kernelFunc:Gq},T_=Vt((e,t)=>e>=t?1:0),qq=nn(Po,T_,null,"bool"),jq={kernelName:Po,backendName:"cpu",kernelFunc:qq},C_=Vt((e,t)=>e<t?1:0),Kq=nn(au,C_,null,"bool"),Xq={kernelName:au,backendName:"cpu",kernelFunc:Kq},N_=Vt((e,t)=>e<=t?1:0),Yq=nn(ou,N_,null,"bool"),Zq={kernelName:ou,backendName:"cpu",kernelFunc:Yq};function __(e,t,n){let r=(t-e)/(n-1),s=w.makeZerosTypedArray(n,"float32");s[0]=e;for(let a=1;a<s.length;a++)s[a]=s[a-1]+r;return s}var E_=_a(e=>Math.log(e)),Jq=Ku(Lo,E_),Qq={kernelName:Lo,backendName:"cpu",kernelFunc:Jq};function A_(e,t,n,r){let s=w.getTypedArrayFromDType(r,w.sizeFromShape(n));for(let a=0;a<s.length;++a){let o=a*t,i=e[o];for(let c=0;c<t;++c){let u=e[o+c];(Number.isNaN(u)||u>i)&&(i=u)}s[a]=i}return s}var $_=Vt((e,t)=>Math.max(e,t)),e5=nn(Bo,$_),t5={kernelName:Bo,backendName:"cpu",kernelFunc:e5},D_=Vt((e,t)=>Math.min(e,t)),n5=nn(Go,D_),r5={kernelName:Go,backendName:"cpu",kernelFunc:n5},L0=Vt((e,t)=>e*t),s5=P0((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),Km=nn(qo,L0,s5),a5={kernelName:qo,backendName:"cpu",kernelFunc:Km};function F_(e,t,n){let r=w.createScalarValue(-1,n);return L0([],t,r,e,n)}function o5(e){let{inputs:t,backend:n}=e,{x:r}=t;be(r,"neg");let s=n.data.get(r.dataId).values,[a,o]=F_(s,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,a)}var i5={kernelName:pu,backendName:"cpu",kernelFunc:o5},R_=Vt((e,t)=>e!==t?1:0),c5=nn(hu,R_,null,"bool"),u5={kernelName:hu,backendName:"cpu",kernelFunc:c5};function z0(e,t,n,r,s){let a=t.length,o=w.sizeFromShape(t),i=w.computeStrides(t),c=w.computeStrides(s),u=w.getTypedArrayFromDType(n,w.sizeFromShape(s));for(let l=0;l<o;++l){let p=w.indexToLoc(l,a,i),d=new Array(p.length);for(let f=0;f<d.length;f++)d[f]=p[r[f]];let h=w.locToIndex(d,a,c);u[h]=e[l]}return u}function Vn(e){let{inputs:t,attrs:n,backend:r}=e,{x:s}=t,{perm:a}=n;be(s,"transpose");let o=s.shape.length,i=new Array(o);for(let p=0;p<i.length;p++)i[p]=s.shape[a[p]];let c=r.data.get(s.dataId).values,u=z0(c,s.shape,s.dtype,a,i);return{dataId:r.write(u,i,s.dtype),shape:i,dtype:s.dtype}}var l5={kernelName:_s,backendName:"cpu",kernelFunc:Vn};function P_(e,t,n,r){let[s,a]=N.computeOutAndReduceShapes(e,r),o=hr(t,"int32"),i=w.makeZerosTypedArray(w.sizeFromShape(s),o),c=w.sizeFromShape(a);for(let u=0;u<i.length;++u){let l=u*c,p=1;for(let d=0;d<c;++d)p*=n[l+d];i[u]=p}return{outVals:i,outShape:s,outDtype:o}}function d5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;be(s,"prod");let i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=N.getAxesPermutation(c,i),l=c,p=s,d=[];u!=null&&(p=Vn({inputs:{x:s},backend:n,attrs:{perm:u}}),d.push(p),l=N.getInnerMostAxes(l.length,i));let h=n.data.get(p.dataId).values,{outVals:f,outShape:m,outDtype:g}=P_(p.shape,p.dtype,h,l),b=m;return o&&(b=N.expandShapeToKeepDim(m,c)),d.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(b,g,f)}var p5={kernelName:Zo,backendName:"cpu",kernelFunc:d5};function h5(e,t,n){e.forEach((r,s)=>{if(r<0||r>=n){let a=w.indexToLoc(s,t.length,w.computeStrides(t)).join(",");throw new Error(`indices[${a}] = ${r} is not in [0, ${n})`)}})}function f5(e,t){for(let n=0;n<e.length;++n){let r=e[n],s=n===e.length-1?t:e[n+1].length;if(r.length===0)throw new Error("Ragged splits may not be empty");if(r[0]<0)throw new Error("Ragged splits must be non-negative");if(r[r.length-1]>s)throw new Error("Ragged splits must not point past values");for(let a=1;a<r.length;++a)if(r[a-1]>r[a])throw new Error("Ragged splits must be sorted in ascending order")}}function m5(e,t,n,r){let s=[],a=0,o=t.length-1+n.length,i=new Array(o).fill(null).map(()=>[0]);f5(n,r);let c=1;for(let u=0;u<t.length-1;++u){c*=t[u];let l=t[u+1];for(let p=1;p<c+1;++p)i[u].push(p*l)}for(let u=0;u<e.length;++u){let l=e[u],p=e[u]+1;for(let d=0;d<n.length;++d){let h=n[d],f=d+t.length-1;if(f>=0){let m=i[f],g=m[m.length-1]-h[l];for(let b=l;b<p;++b)i[f].push(h[b+1]+g)}l=h[l],p=h[p]}p!==l&&(s.push([l,p]),a+=p-l)}return{outSplits:i,valueSlices:s,numValues:a}}function g5(e){let t=[];for(let n=0;n<e.length;++n){let r=e[n].length,s=w.getArrayFromDType("int32",r);t.push(s),e[n].forEach((a,o)=>s[o]=a)}return t}function i1(e,t){let n=e.slice(0,t);for(;n.length<t;)n.push(1);for(let r=t;r<e.length;r++)n[t-1]*=e[r];return n}function b5(e,t,n,r,s,a){let o=i1(t,2)[1],i=i1(a,2)[1],c=0;for(let u of n)for(let l=u[0];l<u[1];++l){for(let p=0;p<r;++p)s[c*i+p]=e[l*o+p];++c}}function y5(e,t,n,r,s){let a=t.slice();a[0]=s;let o=w.getArrayFromDType(n,w.sizeFromShape(a)),i=e.length,c=i===0?0:i/t[0];return b5(e,t,r,c,o,a),[o,a]}function O_(e,t,n,r,s,a,o,i){if(e.length===0)throw new Error("paramsNestedSplits must be non empty");if(t[0].length===0)throw new Error("Split tensors must not be scalars");let c=t[0][0]-1;if(h5(a,o,c),r.length===0)throw new Error("params.rank must be nonzero");let u=r[0],{outSplits:l,valueSlices:p,numValues:d}=m5(a,o,e,u),h=g5(l),f=y5(n,r,s,p,d);return[h,f[0],f[1]]}var c1=2147483647;function M_(e,t,n,r,s,a,o){if(t.length>1)throw new Error("starts must be a scalar or vector");if(s.length>1)throw new Error("limits must be a scalar or vector");if(o.length>1)throw new Error("deltas must be a scalar or vector");let i=t.length===0,c=s.length===0,u=o.length===0,l=[];i||l.push(t[0]),c||l.push(s[0]),u||l.push(o[0]);for(let g=1;g<l.length;++g)if(l[g]!==l[g-1])throw new Error("starts, limits, and deltas must have the same shape");let p=l.length===0?1:l[0],d=w.getArrayFromDType("int32",p+1);d[0]=0;for(let g=0;g<p;++g){let b=i?e[0]:e[g],y=c?r[0]:r[g],v=u?a[0]:a[g];if(v===0)throw new Error("Requires delta != 0");let x;if(v>0&&y<b||v<0&&y>b)x=0;else if(x=Math.ceil(Math.abs((y-b)/v)),x>c1)throw new Error(`Requires ((limit - start) / delta) <= ${c1}`);d[g+1]=d[g]+x}let h=d[p],f=w.getArrayFromDType(n,h),m=0;for(let g=0;g<p;++g){let b=d[g+1]-d[g],y=i?e[0]:e[g],v=u?a[0]:a[g];for(let x=0;x<b;++x)f[m++]=y,y+=v}return[d,f]}var Cr=N.RowPartitionType,dv=class{constructor(e,t,n,r,s,a,o,i,c,u){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=r,this.valuesDType=s,this.defaultValue=a,this.defaultValueShape=o,this.rowPartitionValues=i,this.rowPartitionValuesShapes=c,this.rowPartitionTypes=N.getRowPartitionTypesHelper(u),this.raggedRank=N.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===Cr.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===Cr.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case Cr.VALUE_ROWIDS:return dv.getMaxWidthValueRowID(t);case Cr.ROW_SPLITS:return dv.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${Cr[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let t=e.length;if(t===0||t===1)return 0;let n=0;for(let r=0;r<t-1;++r){let s=e[r+1]-e[r];s>n&&(n=s)}return n}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let n=0,r=e[0],s=0;for(let a=1;a<t;++a){let o=e[a];o!==r&&(r=o,s=Math.max(a-n,s),n=a)}return Math.max(t-n,s)}tensorShapeFromTensor(e,t,n=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return l1(e,n)}calculateOutputSize(e){let t=this.valuesShape,n=this.defaultValueShape;N.validateDefaultValueShape(n,t);let r=this.tensorShapeFromTensor(this.shape,this.shapeShape),a=N.combineRaggedTensorToTensorShapes(this.raggedRank,r,t);a[0]<0&&(a[0]=e);for(let o=1;o<=this.raggedRank;++o)a[o]<0&&(a[o]=this.getMaxWidth(o));return a}calculateFirstParentOutputIndex(e,t,n){let r=Math.min(e,n),s=[],a=0;for(let o=0;o<r;++o,a+=t)s.push(a);for(let o=r;o<e;++o)s.push(-1);return w.assert(s.length===e,()=>"Final length of result must be equal to firstDimension."),s}calculateOutputIndexRowSplit(e,t,n,r){let s=e.length,a=[];for(let o=0;o<s-1;++o){let i=e[o+1]-e[o],c=Math.min(r,i),u=t[o];u===-1&&(c=0);for(let l=0;l<c;++l)a.push(u),u+=n;for(let l=0;l<i-c;++l)a.push(-1)}if(s>0&&a.length!==e[s-1])throw new Error("Invalid row split size.");return a}calculateOutputIndexValueRowID(e,t,n,r){let s=e.length,a=[];if(s===0)return[];let o=0,i=e[0];if(i>=t.length)throw new Error(`Got currentValueRowId=${i}, which is not less than ${t.length}`);let c=t[i];a.push(c);for(let u=1;u<s;++u){let l=e[u];if(l===i)c>=0&&(++o,o<r?c+=n:c=-1);else{if(o=0,i=l,l>=t.length)throw new Error(`Got nextValueRowId=${l} which is not less than ${t.length}`);c=t[l]}a.push(c)}if(a.length!==e.length)throw new Error("Invalid row ids.");return a}calculateOutputIndex(e,t,n,r){let s=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case Cr.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,t,n,r);case Cr.ROW_SPLITS:if(s.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(s,t,n,r);default:throw new Error(`Unsupported partition type: ${Cr[a]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case Cr.FIRST_DIM_SIZE:return e[0];case Cr.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Cr.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${Cr[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let t=this.getFirstDimensionSize(),n=this.calculateOutputSize(t),r=new Array(this.raggedRank+1);r[r.length-1]=1;for(let i=r.length-2;i>=0;--i)r[i]=r[i+1]*n[i+1];let s=l1(n,!1),a=w.getArrayFromDType(this.valuesDType,w.sizeFromShape(s));if(r[0]*n[0]>0){let i=this.calculateFirstParentOutputIndex(t,r[0],n[0]);for(let c=1;c<=this.raggedRank;++c)i=this.calculateOutputIndex(c-1,i,r[c],n[c]);this.setOutput(this.raggedRank,i,a,s)}return[s,a]}setOutput(e,t,n,r){if(n.length===0)return;let s=this.values,a=n,o=r.slice();o=o.slice(e+1);let i=w.sizeFromShape(o),c=t.length,u=this.defaultValue;if(u.length!==i&&u.length!==1){let h=this.defaultValueShape;O(()=>{let f=W(u,h);u=Ya(f,o).dataSync()})}let l=0,p=0,d=0;for(let h=0;h<=c;++h){let f=h<c?t[h]:-1;if(f===d){++d;continue}if(p<d){let m=s.subarray(l*i),g=a.subarray(p*i),b=(d-p)*i;u1(g,m,b)}if(h>=c){let 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d=r.length,h=[];if(d>0){h[d-1]=1;for(let g=d-2;g>=0;--g)h[g]=h[g+1]*r[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*c[g+1]}let m=w.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let b=0;for(let y=0;y<d;++y)b+=e[g*d+y]*h[y];for(let y=0;y<i;++y)m[g*i+y]=Math.trunc(b/f[y]),b%=f[y]}return[m,[o,i],c]}function W0(e,t,n,r,s,a=!1,o=0){let i=r.length,c=[t[0],e.length/t[0]],u=c[1],p=i>0?s[i-1]+1:0;if(p<0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=t.slice();d[0]=p;let h=d.reduce((v,x)=>v*x,1),f=w.getArrayFromDType(n,h);if(i===0)return p>0&&f.fill(o),[f,d];if(p<=0)throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,g=1,b=0,y=s[m];for(;;){let v=0;if(g<i){if(v=s[g],y===v){++g;continue}if(y>=v)throw new Error(N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(y<0||y>=p)throw new Error(N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y,p));y>b&&f.fill(o,b*u,y*u);for(let 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x=o?[g,l,d]:[g,d,l],k=i?[b,h,p]:[b,p,h],S=ft({inputs:{x:s},backend:n,attrs:{shape:x}}),C=ft({inputs:{x:a},backend:n,attrs:{shape:k}}),E=o?S.shape[1]:S.shape[2],$=o?S.shape[2]:S.shape[1],F=i?C.shape[1]:C.shape[2],A=Math.max(g,b),R=n.data.get(S.dataId).values,T=n.data.get(C.dataId).values,L=w.computeStrides(S.shape),V=w.computeStrides(C.shape),[G,j,H]=o?[L[0],1,L[1]]:[L[0],L[1],1],[Z,J,ee]=i?[1,V[1],V[0]]:[V[1],1,V[0]],re=$*F,te=Me([A,$,F],S.dtype),ie=te.values,ne=n.blockSize;for(let le=0;le<A;le++)for(let ue=0;ue<$;ue+=ne)for(let ye=0;ye<F;ye+=ne)for(let ve=0;ve<E;ve+=ne){let Se=Math.min(ue+ne,$),Le=Math.min(ye+ne,F),je=Math.min(ve+ne,E);for(let nt=ue;nt<Se;nt++)for(let rt=ye;rt<Le;rt++){let st=0;for(let Ze=ve;Ze<je;Ze++){let ze=Math.min(le,g-1)*G,dt=Math.min(le,b-1)*ee,qn=R[ze+nt*j+Ze*H],At=T[Ze*Z+rt*J+dt];st+=qn*At}ie[le*re+(nt*F+rt)]+=st}}return n.disposeIntermediateTensorInfo(S),n.disposeIntermediateTensorInfo(C),n.makeTensorInfo(v,te.dtype,te.values)}var B5={kernelName:xo,backendName:"cpu",kernelFunc:t2};function W5(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:c,transposeB:u,activation:l,leakyreluAlpha:p}=r,d,h,f,m=[];d=t2({inputs:{a:s,b:a},attrs:{transposeA:c,transposeB:u},backend:n}),o&&(h=_c({inputs:{a:d,b:o},backend:n}),m.push(d),d=h),l&&(f=ef(n,d,l,i,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var V5={kernelName:eo,backendName:"cpu",kernelFunc:W5},U5=at(Rc,e=>Math.acos(e)),G5={kernelName:Rc,backendName:"cpu",kernelFunc:U5},H5=at(Pc,e=>Math.acosh(e)),q5={kernelName:Pc,backendName:"cpu",kernelFunc:H5};function j5(e){let{inputs:t,backend:n}=e,r=t;be(t,"addN");let s=r.map(i=>n.data.get(i.dataId).values),a=Me(r[0].shape,r[0].dtype),o=a.values;for(let i=0;i<r.length;i++){let c=s[i];for(let u=0;u<o.length;u++)o[u]+=c[u]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var K5={kernelName:bo,backendName:"cpu",kernelFunc:j5};function X5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;be(s,"all");let i=w.parseAxisParam(a,s.shape),c=i,u=N.getAxesPermutation(c,s.shape.length),l=s;u!=null&&(l=Vn({inputs:{x:s},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,s.shape.length)),N.assertAxesAreInnerMostDims("all",c,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,c),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),l.dtype),m=n.data.get(l.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v&&k}f[b]=v}u!=null&&n.disposeIntermediateTensorInfo(l);let g=n.makeTensorInfo(p,l.dtype,f);if(o){let b=N.expandShapeToKeepDim(p,i),y=ft({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var Y5={kernelName:Oc,backendName:"cpu",kernelFunc:X5};function Z5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;be(s,"any");let i=w.parseAxisParam(a,s.shape),c=i,u=N.getAxesPermutation(c,s.shape.length),l=s;u!=null&&(l=Vn({inputs:{x:s},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,s.shape.length)),N.assertAxesAreInnerMostDims("any",c,l.shape.length);let[p,d]=N.computeOutAndReduceShapes(l.shape,c),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),l.dtype),m=n.data.get(l.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}u!=null&&n.disposeIntermediateTensorInfo(l);let g=n.makeTensorInfo(p,l.dtype,f);if(o){let b=N.expandShapeToKeepDim(p,i),y=ft({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var J5={kernelName:Mc,backendName:"cpu",kernelFunc:Z5};function Q5(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;be(s,"argMax");let o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=Vn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=N.getInnerMostAxes(o.length,c.shape.length)),o=[o[0]],N.assertAxesAreInnerMostDims("argMax",o,c.shape.length);let[l,p]=N.computeOutAndReduceShapes(c.shape,o),d=w.sizeFromShape(l),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(c.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k>y&&(y=k,v=x)}h[g]=v}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(l,"int32",h)}var ej={kernelName:yo,backendName:"cpu",kernelFunc:Q5};function tj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r;be(s,"argMin");let o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=Vn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=N.getInnerMostAxes(o.length,c.shape.length)),o=[o[0]],N.assertAxesAreInnerMostDims("argMin",o,c.shape.length);let[l,p]=N.computeOutAndReduceShapes(c.shape,o),d=w.sizeFromShape(l),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(c.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return u.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(l,"int32",h)}var nj={kernelName:dd,backendName:"cpu",kernelFunc:tj},rj=at(Lc,e=>Math.asin(e)),sj={kernelName:Lc,backendName:"cpu",kernelFunc:rj},aj=at(zc,e=>Math.asinh(e)),oj={kernelName:zc,backendName:"cpu",kernelFunc:aj},ij=at(Bc,e=>Math.atan(e)),cj={kernelName:Bc,backendName:"cpu",kernelFunc:ij},uj=Vt((e,t)=>Math.atan2(e,t)),lj=nn(Vc,uj),dj={kernelName:Vc,backendName:"cpu",kernelFunc:lj},pj=at(Wc,e=>Math.atanh(e)),hj={kernelName:Wc,backendName:"cpu",kernelFunc:pj};function q0(e,t,n,r,s,a){let o=s.strideHeight,i=s.strideWidth,c=s.dilationHeight,u=s.dilationWidth,l=s.effectiveFilterHeight,p=s.effectiveFilterWidth,d=s.padInfo.top,h=s.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=Me(s.outShape,n),g=m.values,b=s.outShape[1]*s.outShape[2]*s.outShape[3],y=s.outShape[2]*s.outShape[3],v=s.outShape[3];for(let x=0;x<s.batchSize;++x){let k=x*b,S=x*r[0];for(let C=0;C<s.inChannels;++C)for(let E=0;E<s.outHeight;++E){let 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V=L-C,G=m.get(g,R,L,b);G>F&&(F=G,s?A=a?((g*r.inHeight+R)*r.inWidth+L)*r.inChannels+b:(R*r.inWidth+L)*r.inChannels+b:A=T*d+V)}}o.set(A,g,y,S,b)}}return o}function r2(e,t,n,r,s,a){let o=s.strideDepth,i=s.strideHeight,c=s.strideWidth,u=s.dilationDepth,l=s.dilationHeight,p=s.dilationWidth,d=s.effectiveFilterDepth,h=s.effectiveFilterHeight,f=s.effectiveFilterWidth,m=s.padInfo.front,g=s.padInfo.top,b=s.padInfo.left,y=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=Me(s.outShape,n),x=v.values,k=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],S=s.outShape[2]*s.outShape[3]*s.outShape[4],C=s.outShape[3]*s.outShape[4],E=s.outShape[4];for(let $=0;$<s.batchSize;++$){let F=$*k,A=$*r[0];for(let R=0;R<s.inChannels;++R)for(let T=0;T<s.outDepth;++T){let L=T*o-m,V=L;for(;V<0;)V+=u;let G=Math.min(s.inDepth,d+L),j=F+T*S;for(let H=0;H<s.outHeight;++H){let Z=H*i-g,J=Z;for(;J<0;)J+=l;let ee=Math.min(s.inHeight,h+Z),re=j+H*C;for(let te=0;te<s.outWidth;++te){let ie=te*c-b,ne=ie;for(;ne<0;)ne+=p;let le=Math.min(s.inWidth,f+ie),ue=re+te*E,ye=y,ve=0,Se=0;for(let je=V;je<G;je+=u){let nt=A+je*r[1];for(let rt=J;rt<ee;rt+=l){let st=nt+rt*r[2];for(let Ze=ne;Ze<le;Ze+=p){let ze=st+Ze*r[3],dt=e[ze+R];if(a==="max"&&dt>ye?ye=dt:a==="avg"&&(ve+=dt,Se++),isNaN(ye))break}if(isNaN(ye))break}if(isNaN(ye))break}let Le=ue+R;x[Le]=a==="avg"?ve/Se:ye}}}}return v}function fj(e,t){let n=Me(t.outShape,"int32"),r=t.strideDepth,s=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,c=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*r-d,v=y;for(;v<0;)v+=o;let x=Math.min(t.inDepth,u+y);for(let k=0;k<t.outHeight;++k){let S=k*s-h,C=S;for(;C<0;)C+=i;let E=Math.min(t.inHeight,l+S);for(let $=0;$<t.outWidth;++$){let F=$*a-f,A=F;for(;A<0;)A+=c;let R=Math.min(t.inWidth,p+F),T=Number.NEGATIVE_INFINITY,L=-1;for(let V=v;V<x;V+=o){let G=V-y;for(let j=C;j<E;j+=i){let H=j-S;for(let Z=A;Z<R;Z+=c){let J=Z-F,ee=e.get(m,V,j,Z,g);ee>=T&&(T=ee,L=G*l*p+H*l+J)}}}n.set(L,m,b,k,$,g)}}}return n}function mj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;be(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,u=1;w.assert(N.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let l=N.computePool2DInfo(s.shape,a,o,u,i,c),p;if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))p=ds({inputs:{x:s},backend:n});else{let d=n.data.get(s.dataId).values,h=w.computeStrides(s.shape),f=q0(d,s.shape,s.dtype,h,l,"avg");p=n.makeTensorInfo(l.outShape,s.dtype,f.values)}return p}var gj={kernelName:vo,backendName:"cpu",kernelFunc:mj};function bj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:c,dataFormat:u}=r;be(s,"avgPool3d");let l=N.computePool3DInfo(s.shape,a,o,1,i,c,u),p=n.data.get(s.dataId).values,d=r2(p,s.shape,s.dtype,w.computeStrides(s.shape),l,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var yj={kernelName:pd,backendName:"cpu",kernelFunc:bj};function vj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,{filterSize:o,strides:i,pad:c,dimRoundingMode:u}=r;be([s,a],"avgPool3DGrad");let l=N.computePool3DInfo(a.shape,o,i,1,c,u),p=l.strideDepth,d=l.strideHeight,h=l.strideWidth,f=l.filterDepth,m=l.filterHeight,g=l.filterWidth,b=l.dilationDepth,y=l.dilationHeight,v=l.dilationWidth,x=l.effectiveFilterDepth,k=l.effectiveFilterHeight,S=l.effectiveFilterWidth,C=x-1-l.padInfo.front,E=S-1-l.padInfo.left,$=k-1-l.padInfo.top,F=Me(a.shape,"float32"),A=1/(f*m*g),R=n.bufferSync(s);for(let T=0;T<l.batchSize;++T)for(let L=0;L<l.inChannels;++L)for(let V=0;V<l.inDepth;++V)for(let G=0;G<l.inHeight;++G)for(let j=0;j<l.inWidth;++j){let H=V-C,Z=G-$,J=j-E,ee=0;for(let re=0;re<x;re+=b){let te=(H+re)/p;if(!(te<0||te>=l.outDepth||Math.floor(te)!==te))for(let ie=0;ie<k;ie+=y){let ne=(Z+ie)/d;if(!(ne<0||ne>=l.outHeight||Math.floor(ne)!==ne))for(let le=0;le<S;le+=v){let ue=(J+le)/h;if(ue<0||ue>=l.outWidth||Math.floor(ue)!==ue)continue;let ye=R.get(T,te,ne,ue,L);ee+=ye}}}F.set(ee*A,T,V,G,j,L)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var xj={kernelName:hf,backendName:"cpu",kernelFunc:vj};function wj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;be([s,a],"avgPoolGrad");let{filterSize:i,strides:c,pad:u}=r,l=N.computePool2DInfo(o.shape,i,c,1,u),p=l.strideHeight,d=l.strideWidth,h=l.filterHeight,f=l.filterWidth,m=l.dilationHeight,g=l.dilationWidth,b=l.effectiveFilterHeight,y=l.effectiveFilterWidth,v=y-1-l.padInfo.left,x=b-1-l.padInfo.top,k=Me(o.shape,"float32"),S=1/(h*f),C=n.data.get(s.dataId).values,E=Me(s.shape,"float32",C);for(let $=0;$<l.batchSize;++$)for(let F=0;F<l.inChannels;++F)for(let A=0;A<l.inHeight;++A)for(let R=0;R<l.inWidth;++R){let T=A-x,L=R-v,V=0;for(let G=0;G<b;G+=m){let j=(T+G)/p;if(!(j<0||j>=l.outHeight||Math.floor(j)!==j))for(let H=0;H<y;H+=g){let Z=(L+H)/d;if(Z<0||Z>=l.outWidth||Math.floor(Z)!==Z)continue;let J=E.get($,j,Z,F);V+=J}}k.set(V*S,$,A,R,F)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var Ij={kernelName:pf,backendName:"cpu",kernelFunc:wj};function kj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,scale:a,offset:o,mean:i,variance:c}=t;w.assert(i.shape.length===c.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),be([s,i,c,a,o],"batchNorm");let{varianceEpsilon:u}=r;u==null&&(u=.001);let l=n.data.get(s.dataId).values,p=n.data.get(i.dataId).values,d=n.data.get(c.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(l.length),g=f.length,b=h.length,y=d.length,v=p.length,x=0,k=0,S=0,C=0;for(let E=0;E<l.length;++E)m[E]=f[x++]+(l[E]-p[k++])*h[S++]/Math.sqrt(d[C++]+u),x>=g&&(x=0),k>=v&&(k=0),S>=b&&(S=0),C>=y&&(C=0);return n.makeTensorInfo(s.shape,s.dtype,m)}var Sj={kernelName:Ro,backendName:"cpu",kernelFunc:kj};function Tj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;be([s],"batchToSpaceND");let i=a.reduce((b,y)=>b*y),c=N.getReshaped(s.shape,a,i),u=N.getPermuted(c.length,a.length),l=N.getReshapedPermuted(s.shape,a,i),p=N.getSliceBeginCoords(o,a.length),d=N.getSliceSize(l,o,a.length),h=ft({inputs:{x:s},backend:n,attrs:{shape:c}}),f=Vn({inputs:{x:h},backend:n,attrs:{perm:u}}),m=ft({inputs:{x:f},backend:n,attrs:{shape:l}}),g=po({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Cj={kernelName:Uc,backendName:"cpu",kernelFunc:Tj};function Nj(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.data.get(s.dataId).values,c=n.data.get(a.dataId).values,u=O0(i,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var 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        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
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      }

      #define isnan(value) isnan_custom(value)
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      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `):(e="",t="attribute",n="varying",r="varying",s="texture2D",a="gl_FragColor",o="",i=`
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `,c=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,u=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:s,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:c,defineRound:u}}function Ii(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / ${s}`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${s}`:`index -= ${e[a]} * ${s}`;return`${o}; ${i};`}).join("")}function Xm(e,t,n="index"){let r=w.computeStrides(t);return r.map((s,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===r.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function uY(e,t){let n=e.length,r=e.map(a=>`${t}[${a}]`),s=new Array(n-1);s[n-2]=r[n-1];for(let a=n-3;a>=0;--a)s[a]=`(${s[a+1]} * ${r[a+1]})`;return s}function lY(e,t,n="index"){let r=e.map((a,o)=>o),s=uY(r,t);return s.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${s[o]}`,c=o===s.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${s[o]}`:`index -= ${e[o]} * ${s[o]}`;return`${i}; ${c};`}).join("")}function Z0(e){let t=w.computeStrides(e).map(n=>n.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function J0(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var L2=`
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
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`),a=e.map(h=>pY(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
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`)}function Zu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return RY(e,t);case 1:return OY(e,t);case 2:return LY(e,t);case 3:return BY(e,t);case 4:return VY(e,t);case 5:return UY(e);case 6:return GY(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function B2(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return FY(e);case 1:return PY(e,t);case 2:return MY(e,t);case 3:return zY(e,t);default:return WY(e,t)}}function pY(e,t,n=!1,r){let s="";n?s+=B2(e,r):s+=Zu(e,r);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?s+=HY(e,t):s+=qY(e,t)),s}function hY(e,t,n){switch(e.length){case 0:return W2();case 1:return kY(e,t,n);case 2:return $Y(e,t,n);case 3:return TY(e,t,n);default:return NY(e,t,n)}}function fY(e,t,n){switch(e.length){case 0:return W2();case 1:return SY(e,t,n);case 2:return DY(e,t,n);case 3:return CY(e,t,n);case 4:return _Y(e,t,n);case 5:return EY(e,t);case 6:return AY(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function mY(e){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `}function gY(e){return`
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `}function bY(e){return`
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `}function yY(e){return`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFs} vec2 resultUV;
    ${e.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

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

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

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

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

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

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${vY}
    ${xY}
    ${wY}
  `}var vY=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,xY=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,wY=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,IY=`
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;function W2(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function kY(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return r[0]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${r[1]}.0);
      }
    `:r[1]===1?n?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${r[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${r[0]}, ${r[1]}));
      return 2 * (resTexRC.x * ${r[1]} + resTexRC.y);
    }
  `}function SY(e,t,n){return t[0]===1?n?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?n?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:n?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function TY(e,t,n){if(n)return`
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[2]/2),a=s*Math.ceil(e[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${r[0]}, ${r[1]}));
      int index = resTexRC.x * ${r[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `}function CY(e,t,n){if(n)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Xm(["r","c","d"],e)}
    return ivec3(r, c, d);
  }
`;let r=Ii(["r","c","d"],e);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${r}
      return ivec3(r, c, d);
    }
  `}function NY(e,t,n){if(n)return`
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],s=Math.ceil(e[e.length-1]/2),a=s*Math.ceil(e[e.length-2]/2),o=a,i="",c="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
      int b${u} = index / ${o};
      index -= b${u} * ${o};
    `+i,c=`b${u}, `+c;return`
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${r[0]}, ${r[1]}));
      int index = resTexRC.x * ${r[1]} + resTexRC.y;

      ${i}

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

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

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

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

      ${n}

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

      ${n}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function $Y(e,t,n){let r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));
      }
    `;let s=Math.ceil(e[1]/2);return n?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${r[0]}, ${r[1]}));

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

      return ivec2(r, c);
    }
  `}function DY(e,t,n){return w.arraysEqual(e,t)?n?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:e[1]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:e[0]===1?n?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:n?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `}function ki(e){return`offset${e}`}function FY(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=$n();return`
    vec4 ${n}() {
      return ${r.texture2D}(${t}, halfCR);
    }
  `}function RY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;let[s,a]=e.shapeInfo.texShape;if(s===1&&a===1)return`
      float ${r}() {
        return sampleTexture(${n}, halfCR);
      }
    `;let o=ki(n);if(t)return`
    float ${r}() {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
      return sampleTexture(${n}, uv);
    }
  `;let[i,c]=e.shapeInfo.texShape;return`
    float ${r}() {
      vec2 uv = uvFromFlat(${i}, ${c}, ${o});
      return sampleTexture(${n}, uv);
    }
  `}function PY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,a=$n();if(t)return`
    vec4 ${r}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${a.texture2D}(${n}, uv);
    }
  `;let o=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];return`
    vec4 ${r}(int index) {
      vec2 uv = packedUVfrom1D(
        ${o[0]}, ${o[1]}, index);
      return ${a.texture2D}(${n}, uv);
    }
  `}function OY(e,t){let n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
      float ${r}(int index) {
        ${Ju(e)}
      }
    `;let s=e.shapeInfo.texShape,a=s[0],o=s[1];if(o===1&&a===1)return`
      float ${r}(int index) {
        return sampleTexture(${n}, halfCR);
      }
    `;let i=ki(n);return o===1?t?`
      float ${r}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${r}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
        return sampleTexture(${n}, uv);
      }
    `:a===1?t?`
      float ${r}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${r}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
        return sampleTexture(${n}, uv);
      }
    `:t?`
    float ${r}(int index) {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${r}(int index) {
      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
      return sampleTexture(${n}, uv);
    }
  `}function MY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],c=$n();if(a!=null&&w.arraysEqual(n,a))return t?`
      vec4 ${s}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);

        return ${c.texture2D}(${r}, uv);
      }
    `:`
      vec4 ${s}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);

        return ${c.texture2D}(${r}, uv);
      }
    `;if(t)return`
    vec4 ${s}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${c.texture2D}(${r}, uv);
    }
  `;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=Math.ceil(n[1]/2);return`
    vec4 ${s}(int row, int col) {
      vec2 uv = packedUVfrom2D(${l}, ${u[0]}, ${u[1]}, row, col);
      return ${c.texture2D}(${r}, uv);
    }
  `}function LY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
      float ${s}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
        return sampleTexture(${r}, uv);
      }
    `;let d=a[0],h=a[1];return`
    float ${s}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
      return sampleTexture(${r}, uv);
    }
  `}let{newShape:o,keptDims:i}=w.squeezeShape(n),c=o;if(c.length<n.length){let d=Qu(e,c),h=["row","col"];return`
      ${Zu(d,t)}
      float ${s}(int row, int col) {
        return ${s}(${el(h,i)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${Ju(e)}
      }
    `;let u=a[0],l=a[1],p=ki(r);return l===1?t?`
      float ${s}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
      return sampleTexture(${r}, uv);
    }
  `:u===1?t?`
      float ${s}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${r}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);
        return sampleTexture(${r}, uv);
      }
    `:`
    float ${s}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
      return sampleTexture(${r}, uv);
    }
  `:t?`
      float ${s}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${r}Shape[1] + col + ${p};
        vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);
        return sampleTexture(${r}, uv);
      }
    `:`
  float ${s}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${p};
    vec2 uv = uvFromFlat(${u}, ${l}, index);
    return sampleTexture(${r}, uv);
  }
`}function zY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=Qu(e,d),m=["b","row","col"];return`
        ${B2(f,t)}
        vec4 ${s}(int b, int row, int col) {
          return ${s}(${el(m,h)});
        }
      `}let i=$n();if(t)return`
    vec4 ${s}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${i.texture2D}(${r}, uv);
    }
  `;let c=o[0],u=o[1],l=Math.ceil(n[2]/2),p=l*Math.ceil(n[1]/2);return`
    vec4 ${s}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${c}, ${u}, ${p}, ${l}, b, row, col);
      return ${i.texture2D}(${r}, uv);
    }
  `}function BY(e,t){let n=e.shapeInfo.logicalShape,r=e.name,s="get"+r.charAt(0).toUpperCase()+r.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:c}=w.squeezeShape(n),u=i;if(u.length<n.length){let m=Qu(e,u),g=["row","col","depth"];return`
        ${Zu(m,t)}
        float ${s}(int row, int col, int depth) {
          return ${s}(${el(g,c)});
        }
      `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${a}, ${o}, 1)));
        ${Ju(e)}
      }
    `;let l=e.shapeInfo.texShape,p=l[0],d=l[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
      float ${s}(int row, int col, int depth) {
        int stride1 = ${r}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
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        return sampleTexture(${r}, uv);
      }
    `:`
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          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${o}, 1));
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      `;if(d===o&&h==null)return t?`
      float ${s}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);
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      }
    `:`
    float ${s}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
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    float ${s}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
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      int stride1 = ${r}Shape[2];
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    `:`
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        // Explicitly use integer operations as dot() only works on floats.
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      int texC = index - texR * ${l};
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      ${Zu(y,t)}
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        return ${s}(${el(v,u)});
      }
    `}if(e.shapeInfo.isUniform)return`
      float ${s}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
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        ${m}
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    `:`
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    `;if(h===a&&l==null)return t?`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
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    `:`
      float ${s}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
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        float texC = float(depth2);
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                  vec2(${h}.0, ${d}.0);
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      }
    `;let b=ki(r);return t?`
    float ${s}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
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      ${m}
      ${g}
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  `:`
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      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} +
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      ${Zu(m)}
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        return ${r}(${el(g,u)});
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    `}if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
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          depth3;
        ${Ju(e)}
      }
    `;let l=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&l==null)return`
      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
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      float ${r}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
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          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
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        return sampleTexture(${n}, uv);
      }
    `;let f=ki(n);return`
    float ${r}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${o} + depth * ${a} +
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    }
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                    int depth2, int depth3, int depth4) {
        return ${r}(${el(b,a)});
      }
    `}let o=t[5],i=t[4]*o,c=t[3]*i,u=t[2]*c,l=t[1]*u;if(e.shapeInfo.isUniform)return`
      float ${r}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
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          vec4(${l}, ${u}, ${c}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${o}, 1)));
        ${Ju(e)}
      }
    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===l&&p==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
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          vec4(${u}, ${c}, ${i}, ${o})) +
               float(depth4);
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                   vec2(${f}.0, ${h}.0);
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    `;if(f===o&&p==null)return`
      float ${r}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
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               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
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        return sampleTexture(${n}, uv);
      }
    `;let m=ki(n);return`
    float ${r}(int row, int col, int depth,
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      // Explicitly use integer operations as dot() only works on floats.
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      return sampleTexture(${n}, uv);
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    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
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      return vec4(outputValue.xy, outputValue.xy);
    `;else if(m&&!b)o===1?h=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:h=`
        return vec4(outputValue.x);
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    vec4 ${s}() {
      ${c} coords = getOutputCoords();
      ${l}
      vec4 outputValue = get${r}(${d});
      ${h}
    }
  `}function qY(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),s="get"+r+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,c=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===c&&e.shapeInfo.flatOffset==null&&w.arraysEqual(o,a))return`
      float ${s}() {
        return sampleTexture(${n}, resultUV);
      }
    `;let u=mt(c),l=z2(e.shapeInfo.logicalShape,t.logicalShape),p=c-i,d,h=["x","y","z","w","u","v"];i===0?d="":c<2&&l.length>=1?d="coords = 0;":d=l.map(m=>`coords.${h[m+p]} = 0;`).join(`
`);let f="";return c<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
    float ${s}() {
      ${u} coords = getOutputCoords();
      ${d}
      return get${r}(${f});
    }
  `}function mt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Q0(e,t,n){let{newShape:r,keptDims:s}=w.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):r,c=!e&&a>1&&!w.arraysEqual(t,n)&&r.length<a||o;return{useSqueezeShape:c,uniformShape:c?i:t,keptDims:s}}function Qu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function el(e,t){return t.map(n=>e[n]).join(", ")}function jY(e,t,n,r){let s=n.map((l,p)=>{let d={logicalShape:l.shape,texShape:l.isUniform?null:l.texData.texShape,isUniform:l.isUniform,isPacked:l.isUniform?!1:l.texData.isPacked,flatOffset:null};return l.texData!=null&&l.texData.slice!=null&&l.texData.slice.flatOffset>0&&(d.flatOffset=l.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=s.map(l=>l.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=dY(s,o,t),c=b2(e.gl,i),u=e.createProgram(c);return q().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:c,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:c,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o},V2(e,t,u))}function V2(e,t,n){let r={},s={},a={},o=[],i,c,u,l=null,p=null;p=e.getUniformLocation(n,"NAN",!1),q().getNumber("WEBGL_VERSION")===1&&(l=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];r[f]=e.getUniformLocation(n,f,d),r[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(s[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),u=e.getUniformLocation(n,"outShapeStrides",d),c=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:r,customUniformLocations:o,infLoc:l,nanLoc:p,inShapesLocations:s,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:c}}function h1(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let s=n.logicalShape,a=t[r],o=a.shape;if(!w.arraysEqual(s,o))throw Error(`Binary was compiled with different shapes than the current args. 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Shape ${i} and ${c} must match`)})}function KY(e,t,n,r,s){t.program.enableShapeUniforms||(h1(t.inShapeInfos,n),h1([t.outShapeInfo],[r]));let a=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),q().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((c,u)=>{let l=t.program.variableNames[u],p=t.uniformLocations[l],d=t.uniformLocations[`offset${l}`],h=t.inShapesLocations[`${l}Shape`],f=t.inTexShapesLocations[`${l}TexShape`];if(h){let{uniformShape:m}=Q0(t.program.packedInputs,c.shape,c.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,c.texData.texShape[0],c.texData.texShape[1]),p!=null){if(c.isUniform){if(w.sizeFromShape(c.shape)<2)e.gl.uniform1f(p,c.uniformValues[0]);else{let m=c.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}c.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,c.texData.slice.flatOffset),e.setInputMatrixTexture(c.texData.texture.texture,p,u)}});let i=t.outShapeLocation;if(i)switch(r.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(r.shape));break;default:break}if(t.outShapeStridesLocation){let c=w.computeStrides(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(c));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(c));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(c));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&s&&t.program.customUniforms.forEach((c,u)=>{let l=t.customUniformLocations[u],p=s[u];if(c.type==="float")e.gl.uniform1fv(l,p);else if(c.type==="vec2")e.gl.uniform2fv(l,p);else if(c.type==="vec3")e.gl.uniform3fv(l,p);else if(c.type==="vec4")e.gl.uniform4fv(l,p);else if(c.type==="int")e.gl.uniform1iv(l,p);else if(c.type==="ivec2")e.gl.uniform2iv(l,p);else if(c.type==="ivec3")e.gl.uniform3iv(l,p);else if(c.type==="ivec4")e.gl.uniform4iv(l,p);else throw Error(`uniform type ${c.type} is not supported yet.`)}),e.executeProgram()}function XY(e,t,n){let r="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let c=o.texData.texShape,{useSqueezeShape:u,uniformShape:l,keptDims:p}=Q0(e.packedInputs,o.shape,c),d="",h="",f="";if(l.length===1&&e.packedInputs){let k=[Math.ceil(c[0]/2),Math.ceil(c[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(l.length===2&&!e.packedInputs)h=`${l[0]>1}_${l[1]>1}`;else if(l.length>2&&!e.packedInputs){let k=w.computeStrides(l);f=`${k[0]===c[1]}_${k[k.length-1]===c[1]}`}let m=o.shape.length,g=l.length===2&&w.arraysEqual(o.shape,c),b=w.sizeFromShape(o.shape)===1,y=N.getBroadcastDims(o.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(c,n.texData.texShape),x=e.packedInputs||l.length>2?"":`${c[0]>1}_${c[1]>1}`;r+=`${m}_${v}_${u?p:""}_${l.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${i}`}else{let c=o.isUniform?"uniform":o.texData.texShape;r+=`${o.shape}_${c}_${i}`}});let s=e.userCode,a=e.constructor.name;return a+="_"+r+"_"+s+`${q().getNumber("WEBGL_VERSION")}`,a}function Dn(e){return q().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var YY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=sd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$n();this.outputShape=e,this.enableShapeUniforms=Dn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Xm(["r","c","d"],e):Ii(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

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

        vec4 result = vec4(0.);

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

        ${t.output} = result;
      }
    `}},ZY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=sd.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=$n();this.outputShape=e,this.enableShapeUniforms=Dn(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?Xm(["r","c","d"],e):Ii(["r","c","d"],e)}
        return ivec3(r, c, d);
      }

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

        vec4 result = vec4(0.);

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

        ${t.output} = result;
      }
    `}},JY=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ur.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
      ${L2}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `}},QY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ur.DOWNLOAD;let t=$n();this.outputShape=e,this.userCode=`
      ${L2}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `}},e9={R:0,G:1,B:2,A:3},f1=class{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=$n();this.outputShape=e,this.enableShapeUniforms=Dn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)");let a="";for(let o=0;o<n.length;o++){let i=n[o];a+=`
          if(offset == ${o}) {
            result = values[${e9[i]}];
          }`}this.userCode=`
      ${this.enableShapeUniforms?J0():Z0(e)}

      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${n.length});

        flatIndex = idiv(flatIndex, ${n.length}, 1.);

        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${r.texture2D}(A, uv);
          ${a}
        }
        ${r.output} = vec4(${s}, 0., 0., 0.);
      }
    `}},t9=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=$n();this.outputShape=e,this.enableShapeUniforms=Dn(this.outputShape.length);let r="",s="result";t&&(s="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;r+=`
          localCoords = coords;
          if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
          localCoords[2] += ${o};
          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
            localCoords[1] += ${a};

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

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

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${n.texture2D}(A, uv);

            if (offset == 0) {
              result[${i}] = values[0];
            } else if (offset == 1) {
              result[${i}] = values[1];
            } else if (offset == 2) {
              result[${i}] = values[2];
            } else {
              result[${i}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?J0():Z0(e)}

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

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

          ${r}

          ${n.output} = ${s};
        }
    `}},U2={};Ae(U2,{bindVertexProgramAttributeStreams:()=>J2,createBufferFromOutputTexture:()=>tE,createFloat16MatrixTexture:()=>K2,createFloat16PackedMatrixTexture:()=>Z2,createFloat32MatrixTexture:()=>j2,createIndexBuffer:()=>q2,createPackedMatrixTexture:()=>Y2,createUnsignedBytesMatrixTexture:()=>X2,createVertexBuffer:()=>H2,createVertexShader:()=>G2,downloadByteEncodedFloatMatrixFromOutputTexture:()=>rE,downloadFloat32MatrixFromBuffer:()=>nE,downloadMatrixFromPackedOutputTexture:()=>aE,downloadPackedMatrixFromBuffer:()=>sE,getInternalFormatForFloat16MatrixTexture:()=>tI,getInternalFormatForFloat16PackedMatrixTexture:()=>sI,getInternalFormatForFloat32MatrixTexture:()=>eI,getInternalFormatForPackedMatrixTexture:()=>rI,getInternalFormatForUnsignedBytesMatrixTexture:()=>nI,uploadDenseMatrixToTexture:()=>Q2,uploadPixelDataToTexture:()=>eE});function G2(e){let t=$n(),n=`${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return g2(e,n)}function H2(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return x2(e,t)}function q2(e){let t=new Uint16Array([0,1,2,2,1,3]);return w2(e,t)}function ip(e,t,n,r,s,a){k2(t,n);let o=I2(e),i=e.TEXTURE_2D;return me(e,()=>e.bindTexture(i,o)),me(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),me(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),me(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),me(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),q().getNumber("WEBGL_VERSION")===1?me(e,()=>e.texImage2D(i,0,r,t,n,0,s,a,null)):me(e,()=>e.texStorage2D(i,1,r,t,n)),me(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function eI(e){return e.internalFormatFloat}function j2(e,t,n,r){let[s,a]=op(t,n);return ip(e,s,a,eI(r),r.textureFormatFloat,e.FLOAT)}function tI(e){return e.internalFormatHalfFloat}function K2(e,t,n,r){let[s,a]=op(t,n);return ip(e,s,a,tI(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function nI(e){return e.downloadTextureFormat}function X2(e,t,n,r){let[s,a]=op(t,n);return ip(e,s,a,nI(r),e.RGBA,e.UNSIGNED_BYTE)}function rI(e){return e.internalFormatPackedFloat}function Y2(e,t,n,r){let[s,a]=Xu(t,n);return ip(e,s,a,rI(r),e.RGBA,e.FLOAT)}function sI(e){return e.internalFormatPackedHalfFloat}function Z2(e,t,n,r){let[s,a]=Xu(t,n);return ip(e,s,a,sI(r),e.RGBA,r.textureTypeHalfFloat)}function J2(e,t,n){return me(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),fv(e,t,"clipSpacePos",n,3,20,0)&&fv(e,t,"uv",n,2,20,12)}function Q2(e,t,n,r,s,a){me(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,c;s instanceof Uint8Array?(o=new Uint8Array(n*r*4),i=e.UNSIGNED_BYTE,c=e.RGBA):(o=new Float32Array(n*r*4),i=e.FLOAT,c=a.internalFormatPackedFloat),o.set(s),q().getNumber("WEBGL_VERSION")===2?me(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,i,o)):me(e,()=>e.texImage2D(e.TEXTURE_2D,0,c,n,r,0,e.RGBA,i,o)),me(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function eE(e,t,n){me(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?q().getNumber("WEBGL_VERSION")===2?me(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):me(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):q().getNumber("WEBGL_VERSION")===2?me(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):me(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),me(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function tE(e,t,n,r){let s=e.createBuffer();me(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,s));let i=4*4*t*n;return me(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),me(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),me(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),s}function nE(e,t,n){let r=e,s=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,s),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),s}function rE(e,t,n,r){let[s,a]=op(t,n),o=4,i=new Uint8Array(Z7(t*n,o));return me(e,()=>e.readPixels(0,0,s,a,r.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function sE(e,t,n,r,s,a,o,i){let c=e,u=new Float32Array(J7(a,o));return c.bindBuffer(c.PIXEL_PACK_BUFFER,t),c.getBufferSubData(c.PIXEL_PACK_BUFFER,0,u),c.bindBuffer(c.PIXEL_PACK_BUFFER,null),u}function aE(e,t,n){let r=new Float32Array(t*n*4);return me(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var Ch=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=q().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,h2(t,e)):this.gl=Kr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),q().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=zl(this.gl,s),lr(this.gl,a))this.textureHalfFloatExtension=zl(this.gl,a);else if(q().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),lr(this.gl,r))this.colorBufferHalfFloatExtension=zl(this.gl,r);else if(q().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",lr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(lr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=H2(this.gl),this.indexBuffer=q2(this.gl),this.framebuffer=S2(this.gl),this.textureConfig=X0(this.gl,this.textureHalfFloatExtension)}get debug(){return q().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;me(e,()=>e.finish()),me(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),me(e,()=>e.deleteFramebuffer(this.framebuffer)),me(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),me(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),me(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),j2(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),K2(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),X2(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),eE(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),Q2(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),Z2(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Y2(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(mv(this.gl,this.framebuffer),this.outputTexture=null),me(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>rE(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,s,a){return sE(this.gl,e,t,n,r,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return nE(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=tE(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(q().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,s=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let 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me(t,()=>t.attachShader(n,this.vertexShader)),me(t,()=>t.attachShader(n,e)),v2(t,n),this.debug&&Ih(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=J2(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&me(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Ih(this.gl,this.program),me(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?C2(this.gl,e,t):N2(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),me(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),_2(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,s]=Xu(t,n);this.setOutputMatrixTextureDriver(e,r,s)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Ih(this.gl,this.program),Bl(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),me(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),me(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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      void main() {
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  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
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      void main() {
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        vec4 y = unaryOperation(x);

        setOutput(y);
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    `}},lZ=fs.whereImpl,dZ=1e-7,pZ=1e-4,bh={};function hZ(e){return e in bh||(bh[e]={}),bh[e]}var fZ=q().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),mZ=600;function gZ(){return q().global.screen==null?1024:q().global.screen.height*q().global.screen.width*window.devicePixelRatio*mZ/1024/1024}var Ym=class extends ld{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!q().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Ch)t=e;else{let n=Kr(q().getNumber("WEBGL_VERSION"),e);t=new Ch(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Kr(q().getNumber("WEBGL_VERSION"));t=new Ch(n),this.binaryCache=hZ(q().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new X9(this.gpgpu),this.numMBBeforeWarning=gZ(),this.texData=new uf(this,Er())}nextDataId(){return Ym.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,r,s,a){let o=this.makeTensorInfo(t,n),i=this.texData.get(o.dataId);i.isPacked=!1,i.texture={texture:e,texShape:[r,s]},i.texShape=[r,s];let c=Wl(t),u=new f1(c,!1,a),l=this.runWebGLProgram(u,[o],n,[[r,s]]);return l.shape=t,i.texture=null,this.disposeIntermediateTensorInfo(o),l.dataId}write(e,t,n){if((q().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||q().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:ur.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,s){if(q().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:ur.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:s,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new Ka(o,sc):p=new Ns(o,sc);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:r}],r),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let c=this.activeTimers!=null,u;c&&(u=w.now());let l;if(r==="complex64"){let p=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=N.mergeRealAndImagArrays(p,d)}else l=this.getValuesFromTexture(e);return c&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:s,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(s!=null){let h;i?h=new Ka(r,sc):h=new Ns(r,sc);let f=this.runWebGLProgram(h,[{dataId:e,shape:r,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(q().getBool("DEBUG")&&!q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&q().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let c=null,u;if(a!=="complex64"&&q().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);c=this.gpgpu.createBufferFromTexture(h.texture.texture,...mh(r))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let l;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];l=N.mergeRealAndImagArrays(f,m)}else if(c==null)l=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(r);l=this.gpgpu.downloadFloat32MatrixFromBuffer(c,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),c!=null){let h=this.gpgpu.gl;me(h,()=>h.deleteBuffer(c))}let p=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Er().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:r,shape:s,slice:a,dtype:o,isPacked:i,texture:c}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new Ka(s,sc):d=new Ns(s,sc);let h=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(c==null)throw r!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),l=Er().makeTensorFromTensorInfo(u),p=this.texData.get(u.dataId);return Object.assign({tensorRef:l},p.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(r=>w.decodeString(r));return Me(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!f2(n))throw q().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),s=w.sizeFromShape(t);if(q().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...mh(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(p),h}let a=q().getBool("WEBGL_PACK")&&r===!0,o=a?Wl(t):t,i=a?new QY(o):new JY(o),c=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(c.dataId),l=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(c),l}timerAvailable(){return q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=w.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=w.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(s);o.kernelMs=w.sum(i),o.getExtraProfileInfo=()=>i.map((c,u)=>({name:a[u],ms:c})).map(c=>`${c.name}: ${c.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(q().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:s,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,c=this.dataRefCount.get(i);c>1?this.dataRefCount.set(i,c-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,s,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=fZ){return q().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return lZ(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ka(e.shape,t),s=this.compileAndRun(r,[e],n);return Er().makeTensorFromTensorInfo(s)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=iE(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(q().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,y1,e.dtype);let t=new Ns(e.shape,y1),n=this.compileAndRun(t,[e]);return Er().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let s=n.map(a=>w.encodeString(a));r=this.write(s,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){return Er().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new uZ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new j9(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ho(e.shape),...fo(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},s=[ho(t),...fo(t)],a=new lE(s,n),o=!0,i=[n],c=this.runWebGLProgram(a,[r],e.dtype,i,o);return{dataId:c.dataId,shape:t,dtype:c.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:r,shape:s,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(s),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=Wl(s),i;r?i=new ZY(o):i=new YY(o);let c=!0,u=[t!=null?t:mh(o)],l=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,u,c,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,n,r,s=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===sd.DENSE){let g=a!=null?a:mh(e.outputShape);i.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),w.sizeFromShape(o.shape)===0)return i.values=w.getTypedArrayFromDType(o.dtype,0),o;let c=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=q().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),c.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!ad(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),c.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(o.dataId);let l={shape:o.shape,texData:i,isUniform:!1},p=XY(e,u,l),d=this.getAndSaveBinary(p,()=>jY(this.gpgpu,e,u,l)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),q().get("ENGINE_COMPILE_ONLY")||KY(this.gpgpu,d,u,l,r),c.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=q().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!q().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,r,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(q().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=O(()=>{if(!q().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=q().getBool("DEBUG");q().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(q().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?dZ:pZ}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:s,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let c=this.activeTimers!=null,u;c&&(u=w.now());let l=t.texShape;if(l==null&&(l=$2(n,i),t.texShape=l),s!=null){let p=Wl(n),d,h=l[1],f=l[0],m=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(i||!m)&&([h,f]=Xu(l[0],l[1])),i?d=new t9(p,m):d=new f1(p,m);let g=m?[f,h]:l,b=this.makeTensorInfo(g,r),y=this.texData.get(b.dataId);m?y.usage=ur.PIXELS:y.usage=ur.UPLOAD,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,s);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],r,v,x),S=this.texData.get(k.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,q().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),c&&(this.uploadWaitMs+=w.now()-u)}else{let p=this.acquireTexture(l,o,r,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=bZ(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(r=>{try{this.checkCompletion_(t),r(!0)}catch(s){throw s}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await Jx(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Y0(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:r,nanLoc:s,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:c,outTexShapeLocation:u}=V2(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=s,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=c,e.outTexShapeLocation=u}}createTensorFromTexture(e,t,n){let{texture:r,height:s,width:a,channels:o}=e,i=Er().backend;if(!i.gpgpu.gl.isTexture(r))throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");let c=i.writeTexture(r,t,n,s,a,o);return Er().makeTensorFromDataId(c,t,n,i)}};Ym.nextDataId=0;function bZ(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var yZ="4.0.0";function dE(){q().set("WEBGL_FORCE_F16_TEXTURES",!0)}$d.isBrowser()&&qf("webgl",()=>new Ym,2);var vZ={forceHalfFloat:dE},oI=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`,$c=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Dn(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}},cp=`
  result.r = isNaN.r ? NAN : result.r;
  result.g = isNaN.g ? NAN : result.g;
  result.b = isNaN.b ? NAN : result.b;
  result.a = isNaN.a ? NAN : result.a;
`,up=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length;this.enableShapeUniforms=Dn(s);let a="";if(r)if(s===0||w.sizeFromShape(this.outputShape)===1)a=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(a=`
          ${mt(s)} coords = getOutputCoords();
        `,s===1)this.enableShapeUniforms?a+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:a+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let i=Tn("coords",s);this.enableShapeUniforms?a+=`
            bool nextRowOutOfBounds =
              (${i[s-2]} + 1) >= outShape[${s} - 2];
            bool nextColOutOfBounds =
              (${i[s-1]} + 1) >= outShape[${s} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:a+=`
            bool nextRowOutOfBounds =
              (${i[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${i[s-1]} + 1) >= ${this.outputShape[s-1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `}this.userCode=`
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

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

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

        setOutput(result);
      }
    `}};function sr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var xZ={kernelName:Oo,backendName:"webgl",kernelFunc:sr};function Ea(e){let{inputs:t,backend:n}=e,{real:r,imag:s}=t,a=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(a.dataId),i=sr({inputs:{x:r},backend:n}),c=sr({inputs:{x:s},backend:n});return o.complexTensorInfos={real:i,imag:c},a}var wZ={kernelName:gf,backendName:"webgl",kernelFunc:Ea},pE="return (a < 0.) ? b * a : a;",hE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function IZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{alpha:a}=r,o=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),i=q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new up(hE,s.shape,o.shape):new $c(pE,s.shape,o.shape),c=n.runWebGLProgram(i,[s,o],"float32");return n.disposeIntermediateTensorInfo(o),c}var kZ={kernelName:Mo,backendName:"webgl",kernelFunc:IZ},fE="return (a < 0.) ? b * a : a;",mE=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function SZ(e){let{inputs:t,backend:n}=e,{x:r,alpha:s}=t,a=q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new up(mE,r.shape,s.shape):new $c(fE,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],"float32")}var TZ={kernelName:Yo,backendName:"webgl",kernelFunc:SZ},tl="if (isnan(x)) return x;";function Ye({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:s,backend:a})=>{let{x:o}=s,i=a,c=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,c);return i.makeTensorInfo(o.shape,c,d)}let u=q().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,l;return u?l=new Ka(o.shape,t):l=new Ns(o.shape,e),i.runWebGLProgram(l,[o],c)}}function dn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:s,dtype:a}){return({inputs:o,backend:i})=>{let{a:c,b:u}=o,l=i;if(r&&c.dtype==="complex64"){let f=l.texData.get(c.dataId),m=l.texData.get(u.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,S={dataId:x.dataId,dtype:x.dtype,shape:c.shape},C={dataId:k.dataId,dtype:k.dtype,shape:u.shape},E=new $c(e,c.shape,u.shape);return l.runWebGLProgram(E,[S,C],hr(x.dtype,k.dtype))}),y=Ea({inputs:{real:g,imag:b},backend:l});return l.disposeIntermediateTensorInfo(g),l.disposeIntermediateTensorInfo(b),y}let p=a||hr(c.dtype,u.dtype);if((c.dtype==="string"||u.dtype==="string"||l.shouldExecuteOnCPU([c,u]))&&s!=null){let f=l.texData.get(c.dataId).values,m=l.texData.get(u.dataId).values,g=c.dtype==="string"?N.fromUint8ToStringArray(f):f,b=c.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,v]=s(c.shape,u.shape,g,b,p),x=l.makeTensorInfo(v,p),k=l.texData.get(x.dataId);return k.values=y,x}let d=q().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new up(t,c.shape,u.shape,n):h=new $c(e,c.shape,u.shape),l.runWebGLProgram(h,[c,u],p)}}function od(e,t=!1){if(e==="linear")return t?sZ:Q9;if(e==="relu")return t?oZ:tZ;if(e==="elu")return t?aZ:eZ;if(e==="relu6")return t?iZ:nZ;if(e==="prelu")return t?mE:fE;if(e==="leakyrelu")return t?hE:pE;if(e==="sigmoid")return t?cZ:rZ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var gE=class{constructor(e,t,n,r=!1,s=!1,a=!1,o=null,i=!1,c=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Dn(this.outputShape.length);let u=r?e[1]:e[2],l=Math.ceil(u/2),p=r?"i * 2, rc.y":"rc.y, i * 2",d=s?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }`:c?m=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }`:m=`vec4 activation(vec4 x) {
          ${o}
        }`,g="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),c&&this.variableNames.push("leakyreluAlpha");let y="rc.x",v="rc.x";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
      ${m}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${l}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${l}; i++) {
          int batchA = ${y};
          int batchB = ${v};
          vec4 a = getMatrixA(batchA, ${p});
          vec4 b = getMatrixB(batchB, ${d});

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

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

        ${b}

        ${g}

        setOutput(result);
      }
    `}},v1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},x1=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}},w1="return a * b;";function iI(e){let{inputs:t,backend:n}=e,{a:r,b:s}=t,a=N.upcastType(r.dtype,s.dtype);if(r.dtype==="complex64"){let i=n.texData.get(r.dataId),c=n.texData.get(s.dataId),u=new x1(v1.REAL,r.shape,s.shape),l=new x1(v1.IMAG,r.shape,s.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:r.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:c.complexTensorInfos.real.dataId,dtype:c.complexTensorInfos.real.dtype,shape:s.shape},{dataId:c.complexTensorInfos.imag.dataId,dtype:c.complexTensorInfos.imag.dtype,shape:s.shape}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(l,p,"float32"),f=Ea({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([r,s])){let i=n.texData.get(r.dataId),c=n.texData.get(s.dataId),[u,l]=k9(r.shape,s.shape,i.values,c.values,a),p=n.makeTensorInfo(l,a),d=n.texData.get(p.dataId);return d.values=u,p}let o;return q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new up(w1,r.shape,s.shape):o=new $c(w1,r.shape,s.shape),n.runWebGLProgram(o,[r,s],a)}var CZ={kernelName:qo,backendName:"webgl",kernelFunc:iI};function NZ(e,t,n){let r=[ho(e.shape),...fo(e.shape)],s={dtype:e.dtype,shape:r,dataId:e.dataId},a=[ho(t),...fo(t)],o=new lE(a,r),i=!0,c=[r],u=n.runWebGLProgram(o,[s],e.dtype,c,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function he(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:a}=r,o=n,i=w.sizeFromShape(s.shape),c=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(c);w.assert(i===u,()=>`The new shape (${c}) has ${u} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let l=o.texData.get(s.dataId);return l.isPacked&&!ad(s.shape,c)&&!(l.texture!==null&&ad(l.shape,c))?NZ(s,c,o):(o.incRef(s.dataId),{dataId:s.dataId,shape:c,dtype:s.dtype})}var _Z={kernelName:xu,backendName:"webgl",kernelFunc:he},I1=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:s,outSize:a}=e;this.outputShape=[r,a];let o=Math.floor(n/4)*4,i=n%4,c="sumValue += dot(values, ones);";if(t!=null){let l=1/t;c=`sumValue += dot(values * ${w.isInt(l)?l.toPrecision(2):l}, ones);`}let u="";s%n>0&&(u=`
        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) {
        ${u}
        return getX(batch, inIdx);
      }

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

        float sumValue = 0.0;

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

          ${c}
        }

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

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

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

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

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

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

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

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

          ${p}
        }

        int inIdx = inOffset + ${u};
        if (${l===1}) {
          ${d} values = ${d}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

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

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

          ${p}
        }
        setOutput(${c});
      }
    `}};function AZ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Si(e,t,n,r){let s=AZ(e.shape),a=e;for(let o=0;o<s.length;o++){let{inSize:i,windowSize:c,outSize:u}=s[o],l,p;n==="mean"?l=o===0?new I1({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:u},i):new I1({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:u}):l=new EZ({windowSize:c,inSize:i,batchSize:e.shape[0],outSize:u},n),p=a,a=r.runWebGLProgram(l,[a],t),p.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(p)}return a}var $Z=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let r=mt(this.rank),s=DZ(t);this.userCode=`
    void main() {
      ${r} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function DZ(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let s=0;s<e.length;s++)r[e[s]]=n[s];return r.join()}var FZ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=mt(this.rank),s=uE("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=s[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${s[this.rank-1]} < ${n[this.rank-1]}`,c=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
    void main() {
      ${r} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${c};
      if(${i}) {
        result[1] = ${c};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${c};
        if(${i}) {
          result[3] = ${c};
        }
      }
      setOutput(result);
    }
    `}};function Zm(e,t,n){let r=q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new FZ(e.shape,t):new $Z(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function RZ(e,t,n,r){let s=t,a=e.shape.length,o=w.parseAxisParam(s,e.shape),i=o,c=N.getAxesPermutation(i,a),u=c!=null,l=e;u&&(l=Zm(e,c,r),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=N.computeOutAndReduceShapes(l.shape,i),h=p;n&&(h=N.expandShapeToKeepDim(p,o));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:l},attrs:{shape:[g,f]},backend:r}),y=Hf(e.dtype),v=Si(b,y,"sum",r),x=he({inputs:{x:v},attrs:{shape:h},backend:r});return r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(v),u&&r.disposeIntermediateTensorInfo(l),x}function Jm(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r;return RZ(s,a,o,n)}var PZ={kernelName:ci,backendName:"webgl",kernelFunc:Jm};function Nn(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{perm:a}=r,o=n,i=s.shape.length,c=new Array(i);for(let l=0;l<c.length;l++)c[l]=s.shape[a[l]];let u;if(o.shouldExecuteOnCPU([s])){let p=o.texData.get(s.dataId).values,d=aI(p,s.shape,s.dtype,a,c);u=o.makeTensorInfo(c,s.dtype);let h=o.texData.get(u.dataId);h.values=d}else u=Zm(s,a,o);return u}var OZ={kernelName:_s,backendName:"webgl",kernelFunc:Nn},bE=1e3;function rf({a:e,b:t,transposeA:n,transposeB:r,backend:s,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:c=null}){let u=e.shape.length,l=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[l-1]:t.shape[l-2],h=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[l-2]:t.shape[l-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Mu.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let k=n?[b,p,h]:[b,h,p],S=r?[y,f,d]:[y,d,f],C=he({inputs:{x:e},backend:s,attrs:{shape:k}}),E=he({inputs:{x:t},backend:s,attrs:{shape:S}}),$=[C,E],F=Math.max(b,y),A=n?C.shape[1]:C.shape[2],R=a!=null,T=o!=null,L=c==="leakyrelu",V=c!=null?od(c,!0):null,G=R||T||L||V!=null,j;if((h===1||f===1)&&A>bE&&G===!1){let Z=C,J=E;n&&(Z=Nn({inputs:{x:C},backend:s,attrs:{perm:[0,2,1]}}),$.push(Z)),r&&(J=Nn({inputs:{x:E},backend:s,attrs:{perm:[0,2,1]}}),$.push(J));let ee=f!==1,re=f===1,te=Z;ee&&(te=he({inputs:{x:Z},backend:s,attrs:{shape:[F,A,1]}}),$.push(te));let ie=f===1?2:1,ne=J;re&&(ne=he({inputs:{x:J},backend:s,attrs:{shape:[F,1,A]}}),$.push(ne));let le=iI({inputs:{a:te,b:ne},backend:s});j=Jm({inputs:{x:le},backend:s,attrs:{axis:ie,keepDims:!0}}),$.push(le)}else{let Z=hr(e.dtype,t.dtype),J=new gE(k,S,[F,h,f],n,r,R,V,T,L),ee=[C,E];if(a!=null&&ee.push(a),T&&ee.push(o),L){let re=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));ee.push(re),$.push(re)}j=s.runWebGLProgram(J,ee,Z)}let H=he({inputs:{x:j},backend:s,attrs:{shape:x}});$.push(j);for(let Z of $)s.disposeIntermediateTensorInfo(Z);return H}function MZ(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:c,transposeB:u,activation:l,leakyreluAlpha:p}=r;return rf({a:s,b:a,transposeA:c,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:l})}var LZ={kernelName:eo,backendName:"webgl",kernelFunc:MZ},k1="return abs(x);";function zZ(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let a=n.texData.get(r.dataId),o=iE(a.values);return n.makeTensorInfo(r.shape,r.dtype,o)}let s;return q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Ka(r.shape,k1):s=new Ns(r.shape,k1),n.runWebGLProgram(s,[r],r.dtype)}var BZ={kernelName:Fc,backendName:"webgl",kernelFunc:zZ},WZ=Or+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,VZ=Ye({opSnippet:WZ}),UZ={kernelName:Rc,backendName:"webgl",kernelFunc:VZ},GZ=Or+`
  if (x < 1.0) return NAN;
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      void main() {
        ${n.join(`
        `)}

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

        vec4 result = ${r};
        setOutput(result);
      }
    `}};function Nh(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return sr({inputs:{x:r[0]},backend:n});if(r.length>q().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(r.length/2),u=Nh({inputs:r.slice(0,c),backend:n}),l=Nh({inputs:r.slice(c),backend:n});return Nh({inputs:[u,l],backend:n})}let s=r.map(c=>c.dtype).reduce((c,u)=>hr(c,u)),a=r.map(c=>c.shape),i=q().getBool("WEBGL_PACK")?new YZ(r[0].shape,a):new XZ(r[0].shape,a);return n.runWebGLProgram(i,r,s)}var ZZ={kernelName:bo,backendName:"webgl",kernelFunc:Nh};function JZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=N.getAxesPermutation(u,i),p=s;l!=null&&(p=Nn({inputs:{x:s},backend:n,attrs:{perm:l}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[d,h]=N.computeOutAndReduceShapes(p.shape,u),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Si(m,m.dtype,"all",n),b;if(o){let y=N.expandShapeToKeepDim(d,c);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(p),b}var QZ={kernelName:Oc,backendName:"webgl",kernelFunc:JZ};function eJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=N.getAxesPermutation(u,i),p=s;l!=null&&(p=Nn({inputs:{x:s},backend:n,attrs:{perm:l}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[d,h]=N.computeOutAndReduceShapes(p.shape,u),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Si(m,m.dtype,"any",n),b;if(o){let y=N.expandShapeToKeepDim(d,c);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(p),b}var tJ={kernelName:Mc,backendName:"webgl",kernelFunc:eJ},nJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${r};

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

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

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

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `}};function yE(e,t,n,r=null){let s=t.shape[0],a=t.shape[1];r!=null&&(s=r.shape[0],a=r.shape[1]);let o=N.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:s,outSize:Math.ceil(a/o)},c=new nJ(i,n,r==null),u=[t];r!=null&&u.push(r);let l=e.runWebGLProgram(c,u,"int32");if(l.shape[1]===1)return l;let p=yE(e,t,n,l);return e.disposeIntermediateTensorInfo(l),p}function vE(e,t,n,r=null){let s=r!=null?r.shape:t.shape,a=s[s.length-1],o=N.computeOptimalWindowSize(a),i=new rJ(s,o,n,r==null),c=r==null?[t]:[t,r],u=e.runWebGLProgram(i,c,"int32");if(u.shape.length===t.shape.length){let l=vE(e,t,n,u);return e.disposeIntermediateTensorInfo(u),l}return u}function xE(e,t,n,r){let s=[n];if(N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),s,t.shape.length),!q().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,c=t;i&&(c=e.unpackTensor(t),a.push(c));let[u,l]=N.computeOutAndReduceShapes(c.shape,s),p=w.sizeFromShape(l),d=he({inputs:{x:c},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=yE(e,d,r);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return vE(e,t,r)}function sJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=Nn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=N.getInnerMostAxes(o.length,c.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],c.shape.length);let l=xE(n,c,o[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),l}var aJ={kernelName:yo,backendName:"webgl",kernelFunc:sJ};function oJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a}=r,o=w.parseAxisParam(a,s.shape),i=N.getAxesPermutation(o,s.shape.length),c=s,u=[];i!=null&&(c=Nn({inputs:{x:s},backend:n,attrs:{perm:i}}),u.push(c),o=N.getInnerMostAxes(o.length,c.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],c.shape.length);let l=xE(n,c,o[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),l}var iJ={kernelName:dd,backendName:"webgl",kernelFunc:oJ},cJ=Or+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,uJ=Ye({opSnippet:cJ}),lJ={kernelName:Lc,backendName:"webgl",kernelFunc:uJ},dJ=Or+"return log(x + sqrt(x * x + 1.0));",pJ=Ye({opSnippet:dJ}),hJ={kernelName:zc,backendName:"webgl",kernelFunc:pJ},fJ=Or+`
  return atan(x);
`,mJ=Ye({opSnippet:fJ}),gJ={kernelName:Bc,backendName:"webgl",kernelFunc:mJ},bJ=oI+`
  return atan(a, b);
`,yJ=`
  vec4 result = atan(a, b);
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+cp+`
  return result;
`,vJ=dn({opSnippet:bJ,packedOpSnippet:yJ}),xJ={kernelName:Vc,backendName:"webgl",kernelFunc:vJ},wJ=Or+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,IJ=Ye({opSnippet:wJ}),kJ={kernelName:Wc,backendName:"webgl",kernelFunc:IJ},id=class{constructor(e,t,n,r=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,l=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
        const ivec2 strides = ivec2(${o}, ${i});
        const ivec2 pads = ivec2(${d}, ${h});

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

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

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

          for (int wR = 0; wR < ${l};
              wR += ${c}) {
            int xR = xRCorner + wR;

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

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

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

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

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

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

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

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

            ${S}
          }

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

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

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

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

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

            for (int wR = 0; wR < ${h};
                wR += ${l}) {
              int xR = xRCorner + wR;

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

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

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

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

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${$} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${r?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 * ${h} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let x="max",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(k="avgValue / count");let S=Math.floor(a/4)*4,C=a%4,E=`
      if (${y}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${x}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${o}, ${i}, ${c});
      const ivec3 pads = ivec3(${m}, ${g}, ${b});
      const float initializationValue = ${v};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

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

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

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

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

        for (int wD = 0; wD < ${d};
            wD += ${u}) {
          int xD = xDCorner + wD;

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

          for (int wR = 0; wR < ${h};
            wR += ${l}) {
            int xR = xRCorner + wR;

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

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

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

              ${E}
            }

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

              ${E}
            } else if (${C===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                initializationValue,
                initializationValue
              );

              ${E}
            } else if (${C===3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${p}, ch),
                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
                initializationValue
              );

              ${E}
            }
          }
          setOutput(${k});
        }
      }
    `}};function SJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Yu(s,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,u=1;w.assert(N.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let l=N.computePool2DInfo(s.shape,a,o,u,i,c);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return sr({inputs:{x:s},backend:n});let p=new id(l,"avg",!1);return n.runWebGLProgram(p,[s],"float32")}var TJ={kernelName:vo,backendName:"webgl",kernelFunc:SJ};function CJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:c,dataFormat:u}=r,l=[1,1,1],p=N.computePool3DInfo(s.shape,a,o,l,i,c,u),d=new cI(p,"avg",!1);return n.runWebGLProgram(d,[s],"float32")}var NJ={kernelName:pd,backendName:"webgl",kernelFunc:CJ},_J=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.top,l=c-1-e.padInfo.left,p=1/(t*n);this.userCode=`
      const ivec2 pads = ivec2(${u}, ${l});
      const float avgMultiplier = float(${p});

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

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

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${i};
            wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${r}.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+= ${o}) {
            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);
      }
    `}},EJ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,c=e.dilationHeight,u=e.dilationWidth,l=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=l-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`
      const ivec3 pads = ivec3(${h}, ${f}, ${m});
      const float avgMultiplier = float(${g});

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

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

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

        for (int wD = 0; wD < ${l};
            wD += ${i}) {
          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 < ${p};
              wR += ${c}) {
            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 < ${d};
                wC += ${u}) {
              float dyC = float(dyCCorner + wC) / ${o}.0;

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

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

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function AJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:c,pad:u,dimRoundingMode:l}=r,p=[1,1,1],d=N.computePool3DInfo(o.shape,i,c,p,u,l),h=new EJ(d);return n.runWebGLProgram(h,[s],o.dtype)}var $J={kernelName:hf,backendName:"webgl",kernelFunc:AJ};function DJ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a;Yu([s,a],"avgPoolGrad");let{filterSize:i,strides:c,pad:u}=r,l=N.computePool2DInfo(o.shape,i,c,1,u),p=new _J(l);return n.runWebGLProgram(p,[s],o.dtype)}var FJ={kernelName:pf,backendName:"webgl",kernelFunc:DJ};function RJ(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a}=t,{transposeA:o,transposeB:i}=r;return rf({a:s,b:a,transposeA:o,transposeB:i,backend:n})}var PJ={kernelName:xo,backendName:"webgl",kernelFunc:RJ},OJ=class{constructor(e,t,n,r,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="0.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${o};
        float scale = ${i};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}},MJ=class{constructor(e,t,n,r,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
      void main() {
        vec4 offset = ${o};
        vec4 scale = ${i};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `}},LJ=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:s,variance:a,offset:o,scale:i}=e;w.assert(s.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(o==null||s.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(i==null||s.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:c}=n;c==null&&(c=.001);let u=[r,s,a],l=null;o!=null&&(l=o.shape,u.push(o));let p=null;i!=null&&(p=i.shape,u.push(i));let d=q().getBool("WEBGL_PACK_NORMALIZATION")?new MJ(r.shape,s.shape,a.shape,l,p,c):new OJ(r.shape,s.shape,a.shape,l,p,c);return t.runWebGLProgram(d,u,u[0].dtype)},zJ={kernelName:Ro,backendName:"webgl",kernelFunc:LJ},BJ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=mt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=WJ(this.rank),r,s=e.map((a,o)=>`sourceLoc.${yv[o]} = start[${o}] + coords.${yv[o]};`);r=`
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      void main() {
        ${r}
        setOutput(getSource(${n}));
      }
    `}},yv=["x","y","z","w","u","v"];function WJ(e){if(e===1)return"sourceLoc";if(e<=6)return yv.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var VJ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=mt(this.rank),n=Tn("coords",this.rank),r=Tn("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,a=`getChannel(getSource(${r.join()}), ${s})`,o=`
      result.x = ${a};
      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
        ++${r[this.rank-1]};
        result.y = ${a};
        --${r[this.rank-1]};
      }
    `,i=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
        ++${r[this.rank-2]};
        result.z = ${a};
        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
          ++${r[this.rank-1]};
          result.w = ${a};
        }
      }
    `,c=this.rank<=4?`sourceLoc = coords +
            ${t}(${e.map((u,l)=>`start[${l}]`).join()});`:e.map((u,l)=>`${r[l]} = ${n[l]} + start[${l}];`).join(`
`);this.userCode=`
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${c}
        vec4 result = vec4(0.);
        ${o}
        ${i}
        setOutput(result);
      }
    `}};function UJ(e,t,n,r){let s=r.texData.get(e.dataId),a=r.makeTensorInfo(n,e.dtype),o=r.texData.get(a.dataId);Object.assign(o,s),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=qt.computeFlatOffset(t,w.computeStrides(e.shape));s.slice&&(i+=s.slice.flatOffset),o.slice={flatOffset:i,origDataId:s.slice&&s.slice.origDataId||e.dataId};let c=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,c+1),a}function nl(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,size:o}=r,[i,c]=qt.parseSliceParams(s,a,o);if(qt.assertParamsValid(s,i,c),w.sizeFromShape(c)===0)return n.makeTensorInfo(c,s.dtype,[]);if(n.shouldExecuteOnCPU([s])||s.dtype==="string"){let p=n.texData.get(s.dataId),d=R9(p.values,i,c,s.shape,s.dtype);return n.makeTensorInfo(c,s.dtype,d)}let{isPacked:u}=n.texData.get(s.dataId),l=qt.isSliceContinous(s.shape,i,c);if(u||!l){let p=q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VJ(c):new BJ(c),d=[i];return n.runWebGLProgram(p,[s],s.dtype,d)}return n.uploadToGPU(s.dataId),UJ(s,i,c,n)}var GJ={kernelName:Su,backendName:"webgl",kernelFunc:nl},HJ=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r;w.assert(s.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,v)=>y*v),c=N.getReshaped(s.shape,a,i),u=N.getPermuted(c.length,a.length),l=N.getReshapedPermuted(s.shape,a,i),p=N.getSliceBeginCoords(o,a.length),d=N.getSliceSize(l,o,a.length),h=[],f=he({inputs:{x:s},backend:n,attrs:{shape:c}}),m=Nn({inputs:{x:f},backend:n,attrs:{perm:u}}),g=he({inputs:{x:m},backend:n,attrs:{shape:l}}),b=nl({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},qJ={kernelName:Uc,backendName:"webgl",kernelFunc:HJ};function jJ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o}=r,i=n.readSync(s.dataId),c=n.readSync(a.dataId),u=oE(i,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var KJ={kernelName:ff,backendName:"webgl",kernelFunc:jJ};function XJ(e){let{inputs:t,backend:n}=e,{s0:r,s1:s}=t,a=n.readSync(r.dataId),o=n.readSync(s.dataId),i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var YJ={kernelName:mf,backendName:"webgl",kernelFunc:XJ},ZJ="return float(a != b);",wE=dn({opSnippet:ZJ,cpuKernelImpl:T9,dtype:"bool"}),JJ={kernelName:hu,backendName:"webgl",kernelFunc:wE};function lp(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return sr({inputs:{x:s.complexTensorInfos.real},backend:n})}var QJ={kernelName:Lf,backendName:"webgl",kernelFunc:lp},eQ="return float(int(x));";function tQ(e,t){let n=new Ns(e.shape,eQ),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function vv(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dtype:a}=r;if(a==="complex64"){if(s.dtype==="complex64")return sr({inputs:{x:s},backend:n});let o=kt(s.shape),i=vv({inputs:{x:s},backend:n,attrs:{dtype:"float32"}}),c=Ea({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),c}if(s.dtype==="complex64"){let o=lp({inputs:{input:s},backend:n}),i=vv({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!w.hasEncodingLoss(s.dtype,a)){let o=sr({inputs:{x:s},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([s])){let o=n.texData.get(s.dataId).values,[i,c,u]=a9(o,s.shape,s.dtype,a);return n.makeTensorInfo(i,c,u)}if(a==="int32")return tQ(s,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),c=wE({inputs:{a:s,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),c}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${a}`)}var nQ={kernelName:wo,backendName:"webgl",kernelFunc:vv},T1="return ceil(x);",rQ=Ye({opSnippet:T1,packedOpSnippet:T1,cpuKernelImpl:o9}),sQ={kernelName:Io,backendName:"webgl",kernelFunc:rQ},aQ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`

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

        setOutput(clamp(value, minVal, maxVal));
      }
    `}},oQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();

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

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function iQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i;q().getBool("WEBGL_PACK_CLIP")?i=new oQ(s.shape):i=new aQ(s.shape);let c=[[a],[o]];return n.runWebGLProgram(i,[s],s.dtype,c)}var cQ={kernelName:ya,backendName:"webgl",kernelFunc:iQ},uQ=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 C1(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function lQ(e){let{inputs:t,backend:n}=e,{x:r}=t,s=n.texData.get(r.dataId),a=new uQ(r.shape),o=[C1(r,s.complexTensorInfos.real),C1(r,s.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var dQ={kernelName:hd,backendName:"webgl",kernelFunc:lQ},pQ=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let r=t.length,s=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

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

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

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

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

        ${a[r-1]} = ${a[r-1]} - 1;
        if (${a[r-2]} < ${n[r-2]} &&
            ${a[r-1]} < ${n[r-1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `}};function yh(e,t,n){let r=e.indexOf(t);return e.map((a,o)=>o===r?`${a} - ${n}`:a).join()}function Qm(e){let{inputs:t,backend:n}=e,{input:r}=t,s=n.texData.get(r.dataId);return sr({inputs:{x:s.complexTensorInfos.imag},backend:n})}var fQ={kernelName:_f,backendName:"webgl",kernelFunc:Qm};function Vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let p=e.map(g=>lp({inputs:{input:g},backend:n})),d=e.map(g=>Qm({inputs:{input:g},backend:n})),h=Vl(p,t,n),f=Vl(d,t,n),m=Ea({inputs:{real:h,imag:f},backend:n});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}let s=n.shouldExecuteOnCPU(e);if(r==="string"&&(s=!0),s){let p=e.map(y=>{let x=[-1,w.sizeFromShape(y.shape.slice(t))];return he({inputs:{x:y},backend:n,attrs:{shape:x}})}),d=p.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),h=N.computeOutShape(p.map(y=>y.shape),1),f=p[0].shape[0]===1,m=i9(d,h,r,f),g=N.computeOutShape(e.map(y=>y.shape),t),b=n.makeTensorInfo(g,r,m);return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}let a=q().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>a){let p=[];for(let h=0;h<e.length;h+=a){let f=e.slice(h,h+a);p.push(Vl(f,t,n))}let d=Vl(p,t,n);for(let h of p)n.disposeIntermediateTensorInfo(h);return d}if(q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let p=new hQ(e.map(d=>d.shape),t);return n.runWebGLProgram(p,e,r)}let{tensors2D:o,outShape:i}=mQ(e,t,n),c=new pQ(o.map(p=>p.shape)),u=n.runWebGLProgram(c,o,r);o.forEach(p=>n.disposeIntermediateTensorInfo(p));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function mQ(e,t,n){let r=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function IE(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,a=w.parseAxisParam(s,t[0].shape)[0],o=t.map(u=>u.shape);N.assertParamsConsistent(o,a);let i=N.computeOutShape(t.map(u=>u.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let c=t.filter(u=>w.sizeFromShape(u.shape)>0);return c.length===1?sr({inputs:{x:c[0]},backend:n}):Vl(c,a,n)}var gQ={kernelName:Gc,backendName:"webgl",kernelFunc:IE},kE=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,c=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(r?v=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?v=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:v=`
          float activation(float x) {
            ${n}
          }
        `,x="result = activation(result);");let k=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${v}

      const ivec2 strides = ivec2(${i}, ${c});
      const ivec2 pads = ivec2(${a}, ${o});

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

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${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 < ${p}; wR++) {
          int xR = xRCorner + wR * ${u};

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

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

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

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

              if (${m}) {
                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 (${f===1}) {

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

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

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

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

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

            }
          }
        }

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

      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 < ${l}; wF++) {
          int xF = xFCorner + wF * ${i};

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

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

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

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

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

              for (int d1 = 0; d1 < ${h}; d1 += 4) {
                vec4 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 (${f===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${h}) *
                  getW(wF, wR, wC, ${h}, d2);
              } else if (${f===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1),
                  getX(batch, xF, xR, xC, ${h} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2),
                  getW(wF, wR, wC, ${h} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},SE=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Dn(this.outputShape.length);let a=e.padInfo.left,o=e.strideWidth,i=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,l=u,p=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m<u;m++)p+=`
           vec4 xTexelC${m*2};
           int xTexelC${m*2}Ready;
           vec4 xTexelC${m*2+1};
           int xTexelC${m*2+1}Ready;
           vec4 xC${m};`;p+=`
     for (int r = 0; r < ${c}; r++) {
      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
       `;for(let m=0;m<u;m++)p+=`
           xTexelC${m*2} = vec4(0.0);
           xTexelC${m*2}Ready = 0;
           xTexelC${m*2+1} = vec4(0.0);
           xTexelC${m*2+1}Ready = 0;
           xC${m} = vec4(0.0);`;p+=`
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;for(let m=0;m<(l+1)/2;m++){let g=m*2;if(p+=`
           xC = xCCorner + ${g*i};
           `,o===1){if(g<u&&(a%2===1?(p+=`
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = getX(batch, xR, xCOffset, d1);

                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }
               `,i===1&&g>0?p+=`
                 xC${g} = vec4(xTexelC${g-2}.zw, xTexelC${g}.xy);
                 `:p+=`
                   xCOffset = xC + 1 - 2;

                   if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);

                     // Need to manually clear unused channels in case
                     // we're reading from recycled texture.
                     if (xCOffset + 1 >= inDims[1]) {
                       previous.zw = vec2(0.0);
                     }

                     xC${g} = vec4(previous.zw, xTexelC${g}.xy);
                   } else {
                     xC${g} = vec4(0.0, 0.0, xTexelC${g}.xy);
                   }
                   `):p+=`
                 if (xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }

                 xC${g} = xTexelC${g};
                 `,g+1<u)){let b=a%2===0?w.nearestLargerEven(i):i;i%2===0&&a%2===1||i%2!==0&&a%2!==1?(p+=`
                   xCOffset = xC + imod(pads[1], 2) + ${b};

                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
                     xTexelC${g+1} = getX(batch, xR, xCOffset, d1);

                     // Need to manually clear unused channels in case
                     // we're reading from recycled texture.
                     if (xCOffset + 1 >= inDims[1]) {
                       xTexelC${g+1}.zw = vec2(0.0);
                     }
                     xTexelC${g+1}Ready = 1;
                   }
                   `,i>1?p+=`
                     xCOffset -= 2;
                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
                      previous = getX(batch, xR, xCOffset, d1);
                      xC${g+1} = vec4(previous.zw, xTexelC${g+1}.xy);
                     } else {
                      xC${g+1} = vec4(0.0, 0.0, xTexelC${g+1}.xy);
                     }
                     `:p+=`
                     xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.xy);
                     `):b===1?p+=`
                     xC${g+1} = xTexelC${g};
                     `:p+=`
                     xCOffset = xC + ${b};

                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
                       xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
                       if (xCOffset + 1 >= inDims[1]) {
                         xTexelC${g+1}.zw = vec2(0.0);
                       }
                       xTexelC${g+1}Ready = 1;
                     }

                     xC${g+1} = xTexelC${g+1};
                     `}}else g<u&&(a%2===1?(p+=`
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = getX(batch, xR, xCOffset, d1);
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }

                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${g+1}Ready == 0) {
                   xTexelC${g+1} = getX(batch, xR, xC + 1, d1);
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xC + 2 >= inDims[1]) {
                     xTexelC${g+1}.zw = vec2(0.0);
                   }
                   xTexelC${g+1}Ready = 1;
                 }

                 xC${g} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw);
               `,g+1<u&&(p+=`
                   final = vec4(0.0);
                   xCOffset = xC + 1 + strides[1];
                   if(xCOffset >= 0 && xCOffset < inDims[1]) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
                   xC${g+1} = vec4(xTexelC${g+1}.xy, final.xy);
                 `)):(p+=`
                 if(xC >= 0 && xC < inDims[1] && xTexelC${g}Ready == 0) {
                   xTexelC${g} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${g}.zw = vec2(0.0);
                   }
                   xTexelC${g}Ready = 1;
                 }

                 xCOffset = xC + strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${g+1}Ready == 0) {
                   xTexelC${g+1} = getX(batch, xR, xCOffset, d1);
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${g+1}.zw = vec2(0.);
                   }
                   xTexelC${g+1}Ready = 1;
                 }

                 xC${g} = vec4(
                   xTexelC${g}.xy, xTexelC${g+1}.xy);
               `,g+1<u&&(p+=`
                   xC${g+1} = vec4(xTexelC${g}.zw, xTexelC${g+1}.zw);
                 `)));g<u&&(p+=`
             wTexel = getW(r, ${g}, d1, d2);
             dotProd += xC${g}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${e.inChannels}) {
               dotProd += xC${g}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `,g+1<u&&(p+=`
               wTexel = getW(r, ${g+1}, d1, d2);
               dotProd += xC${g+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${e.inChannels}) {
                 dotProd += xC${g+1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `))}p+=`
     }
   `,p+=`
     }
   `,p+=`
     }
   `;let d="",h="";n&&(r?d=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:s?d=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
         }`:d=`vec4 activation(vec4 x) {
           ${n}
         }`,h="result = activation(result);");let f=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${d}

       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;

         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
         vec4 dotProd = vec4(0.000000000000001);

         ${p}

         vec4 result = dotProd - vec4(0.000000000000001);
         ${f}
         ${h}
         setOutput(result);
       }
     `}},yQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Dn(this.outputShape.length);let{dataFormat:n}=t,r=$n(),s=n==="channelsLast",a=s?1:2,o=s?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,c="";for(let u=0;u<=1;u++)for(let l=0;l<=1;l++)c+=`
          blockIndex = rc.z + ${l};
          pos = rc.y + ${u};

          ${i}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${a}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);

              if(d1 < inputShape[${o}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${s}) {
                  innerDims = vec2(d1, ch);
                  result[${u*2+l}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${u*2+l}] = getChannel(
                    getA(rc.x, ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;this.userCode=`
      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${c}

        ${r.output} = result;
      }
    `}};function sf(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function TE({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let c=e.shape,u=r.texData.get(e.dataId),l=n.inChannels,p=c[0]*c[1]*c[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(a!=null){let x=sf(a.shape,h);x!=null&&(a=he({inputs:{x:a},backend:r,attrs:{shape:x}}),b.push(a))}if(s!=null){let x=sf(s.shape,h);x!=null&&(s=he({inputs:{x:s},backend:r,attrs:{shape:x}}),b.push(s))}if(!((p===1||d===1)&&l>bE)&&u.isPacked&&h&&u.texture!=null&&c[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),c.slice(-3))){let x=c[0]*c[1]*(c[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},S=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(ad(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let C=he({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(C);let E=rf({a:k,b:C,backend:r,transposeA:f,transposeB:m,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),$=r.texData.get(E.dataId);w.assert($.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=S,$.shape=n.outShape,g=sr({inputs:{x:E},backend:r}),g.shape=n.outShape,b.push(E)}else{let x=n.outHeight*n.outWidth,k=he({inputs:{x:e},backend:r,attrs:{shape:h?[n.batchSize,x,n.inChannels]:[n.batchSize,n.inChannels,x]}}),S=he({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=rf({a:h?k:S,b:h?S:k,transposeA:!h,transposeB:m,backend:r,bias:s,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=he({inputs:{x:C},backend:r,attrs:{shape:n.outShape}}),b.push(k),b.push(S),b.push(C)}for(let x of b)r.disposeIntermediateTensorInfo(x);return g}function CE({x:e,filter:t,convInfo:n,backend:r,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:c,filterHeight:u,inChannels:l,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=c*u*l,g=d*p,b=[n.batchSize,m,g],y=!0,v=!1,x=[];if(a!=null){let H=sf(a.shape,f);H!=null&&(a=he({inputs:{x:a},backend:r,attrs:{shape:H}}),x.push(a))}if(s!=null){let H=sf(s.shape,f);H!=null&&(s=he({inputs:{x:s},backend:r,attrs:{shape:H}}),x.push(s))}let k=he({inputs:{x:t},backend:r,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k);let S=new yQ(b,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=r.runWebGLProgram(S,[e],"float32",C),$=he({inputs:{x:E},backend:r,attrs:{shape:b}});x.push(E),x.push($);let F=s!=null,A=a!=null,R=i==="leakyrelu",T=i?od(i,!0):null,L=new gE(f?$.shape:k.shape,f?k.shape:$.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],y,v,F,T,A,R),V=f?[$,k]:[k,$];if(s&&V.push(s),A&&V.push(a),R){let H=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));V.push(H),x.push(H)}let G=r.runWebGLProgram(L,V,"float32"),j=he({inputs:{x:G},backend:r,attrs:{shape:n.outShape}});x.push(G);for(let H of x)r.disposeIntermediateTensorInfo(H);return j}function vQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dataFormat:c,dilations:u,dimRoundingMode:l}=r,p=N.convertConv2DDataFormat(c),d=N.computeConv2DInfo(s.shape,a.shape,o,u,i,l,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=TE({x:s,filter:a,convInfo:d,backend:n});else if(d.strideWidth<=2&&p==="channelsLast"&&q().getBool("WEBGL_EXP_CONV")){let m=new SE(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=n.runWebGLProgram(m,[s,a],"float32",g)}else if(q().getBool("WEBGL_CONV_IM2COL"))h=CE({x:s,filter:a,convInfo:d,backend:n});else{let m=new kE(d);h=n.runWebGLProgram(m,[s,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var xQ={kernelName:ko,backendName:"webgl",kernelFunc:vQ},wQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=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} - ${r};

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

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

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

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

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

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

        ivec2 dyCorner = ivec2(coords[${c}], coords[${u}]) - 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) / ${r}.0;

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

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

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

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

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

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

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

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

        float dotProd = 0.0;

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

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

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

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

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

                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);
      }
    `}},SQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,s=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,c=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${i}, ${c}, ${u});

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


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

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

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

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

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

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

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

            for (int wC = 0; wC < ${r}; 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);

              int wCPerm = ${r} - 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 TQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,dataFormat:c,dimRoundingMode:u,filterShape:l}=r,p=N.convertConv2DDataFormat(c),d=N.computeConv2DInfo(s.shape,l,o,1,i,u,!1,p),h=new wQ(d);return n.runWebGLProgram(h,[s,a],"float32")}var CQ={kernelName:bf,backendName:"webgl",kernelFunc:TQ};function NQ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{inputShape:o,strides:i,pad:c,dataFormat:u,dimRoundingMode:l}=r,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(o,a.shape,i,1,c,l,!1,p),h=new IQ(d);return n.runWebGLProgram(h,[s,a],"float32")}var _Q={kernelName:So,backendName:"webgl",kernelFunc:NQ};function EQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r,u=N.computeConv3DInfo(s.shape,a.shape,o,c,i),l=new bQ(u);return n.runWebGLProgram(l,[s,a],"float32")}var AQ={kernelName:fd,backendName:"webgl",kernelFunc:EQ};function $Q(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,pad:i,filterShape:c}=r,u=N.computeConv3DInfo(s.shape,c,o,1,i),l=new kQ(u);return n.runWebGLProgram(l,[s,a],"float32")}var DQ={kernelName:yf,backendName:"webgl",kernelFunc:$Q};function FQ(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{pad:o,strides:i,inputShape:c}=r,u=N.computeConv3DInfo(c,a.shape,i,1,o),l=new SQ(u);return n.runWebGLProgram(l,[s,a],"float32")}var RQ={kernelName:vf,backendName:"webgl",kernelFunc:FQ},PQ=tl+`
  return cos(x);
`,OQ=Ye({opSnippet:PQ}),MQ={kernelName:To,backendName:"webgl",kernelFunc:OQ},LQ=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,zQ=Ye({opSnippet:LQ}),BQ={kernelName:Co,backendName:"webgl",kernelFunc:zQ},WQ=class{constructor(e,t,n,r,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,c]=e,[u]=t,[l,p]=n;this.outputShape=[u,l,p,c];let d=r==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,b]=l>1?[`${(o-1)/(l-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
      const float height_ratio = float(${m});
      const float width_ratio = float(${y});
      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 = ${g};
        float width_scale = ${v};

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

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

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

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}},VQ=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:a,boxInd:o}=t,{cropSize:i,method:c,extrapolationValue:u}=r,l=new WQ(s.shape,a.shape,i,c,u);return n.runWebGLProgram(l,[s,a,o],"float32")},UQ={kernelName:qc,backendName:"webgl",kernelFunc:VQ},cd;(function(e){e.Prod="*",e.Sum="+"})(cd||(cd={}));var N1=class{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,a=this.op===cd.Prod?"1.0":"0.0",o=n?a:`getX(${_1(s,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],c="",u="";n?(c=r?`end != ${i-1}`:"end != 0",u=r?"end + 1":"end - 1"):(c=r?`end + pow2 < ${i}`:"end >= pow2",u=r?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${mt(s)} coords = getOutputCoords();
        int end = ${E1(s,"coords",this.op)};
        float val = ${o};
        int pow2 = int(pow(2.0, index));
        if (${c}) {
          int idx = ${u};
          ${E1(s,"coords",this.op)} = idx;
          val ${this.op}= getX(${_1(s,"coords",this.op)});
        }
        setOutput(val);
      }
    `}};function _1(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function E1(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function NE(e,t,n,r,s,a){let o=t.shape.length,i=N.getAxesPermutation([r],o),c=t;i!=null&&(c=Nn({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=N.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);let l=c.shape[u],p=sr({inputs:{x:c},backend:n});for(let d=0;d<=Math.ceil(Math.log2(l))-1;d++){let h=new N1(e,c.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(s){let d=new N1(e,c.shape,s,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=N.getUndoAxesPermutation(i),h=Nn({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}return p}function GQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return NE(cd.Prod,s,n,a,o,i)}var HQ={kernelName:Hc,backendName:"webgl",kernelFunc:GQ};function qQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r;return NE(cd.Sum,s,n,a,o,i)}var jQ={kernelName:No,backendName:"webgl",kernelFunc:qQ};function KQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,weights:a}=t,{size:o,binaryOutput:i}=r;if(s.shape.length===1){let c=n.readSync(s.dataId),u=n.readSync(a.dataId),l=oE(c,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,l)}else if(s.shape.length===2){let c=n.bufferSync(s),u=n.bufferSync(a),l=s9(c,u,o,i);return n.makeTensorInfo(l.shape,a.dtype,l.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${s.shape.length}.`)}var XQ={kernelName:xf,backendName:"webgl",kernelFunc:KQ},YQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

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

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ZQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:a,dataFormat:o}=r,i=s.shape[0],c=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],l=o==="NHWC"?s.shape[3]:s.shape[1],p=c*a,d=u*a,h=l/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new YQ(f,a,o);return n.runWebGLProgram(m,[s],s.dtype)}var JQ={kernelName:jc,backendName:"webgl",kernelFunc:ZQ},_E=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Dn(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,c="",u="";n&&(r?c=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?c=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:c=`
          float activation(float x) {
            ${n}
          }
        `,u="result = activation(result);");let l=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${c}

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

        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 < ${a}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${o}; wC++) {
            int xC = xCCorner + wC * dilations[1];

            if (xC < 0 || xC >= inDims[1]) {
              continue;
            }

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

        float result = dotProd;
        ${l}
        ${u}
        setOutput(result);
      }
    `}},EE=class{constructor(e,t=!1,n=null,r=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Dn(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,c=e.dilationWidth,u=e.filterHeight,l=e.filterWidth,p=l,d=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<l;g++)d+=`
          vec4 xTexelC${g*2};
          int xTexelC${g*2}Ready;
          vec4 xTexelC${g*2+1};
          int xTexelC${g*2+1}Ready;
          vec4 xC${g};`;d+=`
    for (int r = 0; r < ${u}; r++) {
      `;for(let g=0;g<l;g++)d+=`
          xTexelC${g*2} = vec4(0.0);
          xTexelC${g*2}Ready = 0;
          xTexelC${g*2+1} = vec4(0.0);
          xTexelC${g*2+1}Ready = 0;
          xC${g} = vec4(0.0);`;d+=`
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`
          xC = xCCorner + ${b*c};
          `,i===1){if(b<l&&(o%2===1?(d+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
              `,c===1&&b>0?d+=`
                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
                `:d+=`
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      previous.zw = vec2(0.0);
                    }

                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);
                  } else {
                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
                  }
                  `):d+=`
                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }

                xC${b} = xTexelC${b};
                `,b+1<l)){let y=o%2===0?w.nearestLargerEven(c):c;c%2===0&&o%2===1||c%2!==0&&o%2!==1?(d+=`
                  xCOffset = xC + imod(pads[1], 2) + ${y};

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${b+1}.zw = vec2(0.0);
                    }
                    xTexelC${b+1}Ready = 1;
                  }
                  `,c>1?d+=`
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);
                     xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);
                    } else {
                     xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);
                    }
                    `:d+=`
                    xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
                    `):y===1?d+=`
                    xC${b+1} = xTexelC${b};
                    `:d+=`
                    xCOffset = xC + ${y};

                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${b+1}.zw = vec2(0.0);
                      }
                      xTexelC${b+1}Ready = 1;
                    }

                    xC${b+1} = xTexelC${b+1};
                    `}}else b<l&&(o%2===1?(d+=`
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {
                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= inDims[1]) {
                    xTexelC${b+1}.zw = vec2(0.0);
                  }
                  xTexelC${b+1}Ready = 1;
                }

                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
              `,b+1<l&&(d+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
                `)):(d+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }

                xCOffset = xC + strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {
                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b+1}.zw = vec2(0.);
                  }
                  xTexelC${b+1}Ready = 1;
                }

                xC${b} = vec4(
                  xTexelC${b}.xy, xTexelC${b+1}.xy);
              `,b+1<l&&(d+=`
                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
                `)));b<l&&(d+=`
            wTexel = getW(r, ${b}, d1, q);
            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
          `,b+1<l&&(d+=`
              wTexel = getW(r, ${b+1}, d1, q);
              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
            `))}d+=`
    }
  `,d+=`
      }
    `;let h="",f="";n&&(r?h=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?h=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:h=`vec4 activation(vec4 x) {
          ${n}
        }`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${h}

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

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${d}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${m}
        ${f}
        setOutput(result);
      }
    `}};function QQ(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c,dimRoundingMode:u}=r,l=c;l==null&&(l=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(o,l),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${l}'`);let p=N.computeConv2DInfo(s.shape,a.shape,o,l,i,u,!0),d;q().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new EE(p):d=new _E(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[s,a],"float32",h)}var eee={kernelName:_o,backendName:"webgl",kernelFunc:QQ},tee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=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} - ${r};

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

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

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

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

      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) / ${r}.0;

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

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

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

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${i}; dm++) {
              int d2 = d1 * ${i} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function ree(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,dy:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:u,filterShape:l}=r,p=N.computeConv2DInfo(s.shape,l,o,i,c,u,!0),d=new tee(p);return n.runWebGLProgram(d,[s,a],"float32")}var see={kernelName:wf,backendName:"webgl",kernelFunc:ree};function aee(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,filter:a}=t,{strides:o,dilations:i,pad:c,dimRoundingMode:u,inputShape:l}=r,p=N.computeConv2DInfo(l,a.shape,o,i,c,u,!0),d=new nee(p);return n.runWebGLProgram(d,[s,a],"float32")}var oee={kernelName:If,backendName:"webgl",kernelFunc:aee},iee=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function cee(e){let{inputs:t,backend:n}=e,{x:r}=t,s=[...r.shape,...r.shape],a=w.sizeFromShape(r.shape),o=he({inputs:{x:r},backend:n,attrs:{shape:[a]}}),i=new iee(a),c=n.runWebGLProgram(i,[o],o.dtype),u=he({inputs:{x:c},backend:n,attrs:{shape:s}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),u}var uee={kernelName:kf,backendName:"webgl",kernelFunc:cee},lee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:s,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:c,dilationWidth:u}=e,{top:l,left:p}=r;this.userCode=`
      const ivec2 strides = ivec2(${s}, ${a});
      const ivec2 pads = ivec2(${l}, ${p});
      const float neg_infinity = -3.4e38;

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

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

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

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

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

        float result = curVal;
        setOutput(result);
      }
    `}};function dee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a}=t,{strides:o,pad:i,dilations:c}=r,u=N.computeDilation2DInfo(s.shape,a.shape,o,i,"NHWC",c),l,p=new lee(u);l=n.runWebGLProgram(p,[s,a],"float32");let d=he({inputs:{x:l},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(l),d}var pee={kernelName:md,backendName:"webgl",kernelFunc:dee};function hee(e){let{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,a=t,{allDims:o,summedDims:i,idDims:c}=N.decodeEinsumEquation(s,a.length);N.checkEinsumDimSizes(o.length,c,a);let{path:u,steps:l}=N.getEinsumComputePath(i,c),p=l.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of l[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,c[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=Nn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=iI({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=Jm({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var fee={kernelName:Sf,backendName:"webgl",kernelFunc:hee},mee="return (x >= 0.0) ? x : (exp(x) - 1.0);",gee=`
  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;
`,bee=Ye({opSnippet:mee,packedOpSnippet:gee}),yee={kernelName:Ao,backendName:"webgl",kernelFunc:bee},vee="return (b >= 1.0) ? a : a * (b + 1.0);",xee=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,wee=e=>{let{inputs:t,backend:n}=e,{dy:r,y:s}=t,a=q().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new up(xee,r.shape,s.shape):new $c(vee,r.shape,s.shape);return n.runWebGLProgram(a,[r,s],r.dtype)},Iee={kernelName:Tf,backendName:"webgl",kernelFunc:wee},kee=`
  return vec4(equal(a, b));
`,See="return float(a == b);",Tee=dn({opSnippet:See,packedOpSnippet:kee,dtype:"bool",cpuKernelImpl:c9}),Cee={kernelName:Xc,backendName:"webgl",kernelFunc:Tee},Nee=`
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${N.ERF_P};
  float a1 = ${N.ERF_A1};
  float a2 = ${N.ERF_A2};
  float a3 = ${N.ERF_A3};
  float a4 = ${N.ERF_A4};
  float a5 = ${N.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));
`,_ee=Ye({opSnippet:Nee}),Eee={kernelName:Kc,backendName:"webgl",kernelFunc:_ee},Aee=tl+`
  return exp(x);
`,$ee=`
  vec4 result = exp(x);
  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;
`,AE=Ye({opSnippet:Aee,packedOpSnippet:$ee,cpuKernelImpl:u9,dtype:"float32"}),Dee={kernelName:$o,backendName:"webgl",kernelFunc:AE};function xv(e){let{inputs:t,attrs:n,backend:r}=e,{dim:s}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),c=s;return s<0&&(w.assert(-(o+1)<=s,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),c=o+s+1),i.splice(c,0,1),he({inputs:{x:a},backend:r,attrs:{shape:i}})}var Fee={kernelName:Yc,backendName:"webgl",kernelFunc:xv},A1="return exp(x) - 1.0;",Ree=Ye({opSnippet:A1,packedOpSnippet:A1,cpuKernelImpl:l9}),Pee={kernelName:Zc,backendName:"webgl",kernelFunc:Ree},$1=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${r}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="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) {
        ${o}
      }

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

        float result = 0.0;

        for (int i = 0; i < ${r}; 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 $E(e,t,n){let r=n.texData.get(e.dataId),s=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=s/a,i=he({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),c=i.shape,u=new $1("real",c,t),l=new $1("imag",c,t),p=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:c},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:c}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(l,p,"float32"),f=Ea({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function Oee(e){let{inputs:t,backend:n}=e,{input:r}=t;return $E(r,!1,n)}var Mee={kernelName:Cf,backendName:"webgl",kernelFunc:Oee},Lee=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function dp(e){let{backend:t,attrs:n}=e,{shape:r,value:s}=n,{dtype:a}=n;if(a=a||w.inferDtype(s),a==="string"){let o=w.getArrayFromDType(a,w.sizeFromShape(r));return o.fill(s),t.makeTensorInfo(r,a,o)}else{let o=new Lee(r,s),i=[[s]];return t.runWebGLProgram(o,[],a,i)}}var zee={kernelName:gd,backendName:"webgl",kernelFunc:dp},Bee=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 - 1;
          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);
        }
    `}},Wee={kernelName:Jc,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,s=new Bee(n.shape);return r.runWebGLProgram(s,[n],n.dtype)}},D1="return floor(x);",Vee=Ye({opSnippet:D1,packedOpSnippet:D1,cpuKernelImpl:d9}),Uee={kernelName:Do,backendName:"webgl",kernelFunc:Vee},Gee=`
  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;
  }
`,Hee=`
  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);
`,qee=dn({opSnippet:Gee,packedOpSnippet:Hee,dtype:"int32"}),jee={kernelName:Fo,backendName:"webgl",kernelFunc:qee},Kee=class{constructor(e){this.variableNames=["A"];let t=$n(),[n,r]=e;this.outputShape=e,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);

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

        setOutput(floor(value * 255.0 + 0.5));
      }
    `}},Xee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=$n(),[n,r]=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(${r}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

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

        ${t.output} = result;
      }
    `}},Yee={kernelName:Fh,backendName:"webgl",kernelFunc:Zee},ac,Ty=q().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Zee(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:s}=t,{numChannels:a}=r,o=typeof HTMLVideoElement!="undefined"&&s instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&s instanceof HTMLImageElement,[c,u]=o?[s.videoWidth,s.videoHeight]:[s.width,s.height],l=[u,c],p=[u,c,a];if(i||o){let m=q().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(ac==null||m!==Ty)&&(Ty=m,ac=document.createElement("canvas").getContext("2d",{willReadFrequently:Ty})),ac.canvas.width=c,ac.canvas.height=u,ac.drawImage(s,0,0,c,u),s=ac.canvas}let d=n.makeTensorInfo(l,"int32");n.texData.get(d.dataId).usage=ur.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),s);let h=q().getBool("WEBGL_PACK")?new Xee(p):new Kee(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function Jee(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:u,dataFormat:l,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=r,m=N.convertConv2DDataFormat(l),g=N.computeConv2DInfo(s.shape,a.shape,c,p,u,d,!1,m),b,y=[],v=o!=null,x=i!=null,k=h==="leakyrelu",S=()=>{let E=[s,a],$=(F,A)=>{if(A==="NCHW"&&F.shape.length===1&&F.shape[0]!==1){let R=he({inputs:{x:F},backend:n,attrs:{shape:[F.shape[0],1,1]}});return y.push(R),R}return F};if(v&&E.push($(o,l)),x&&E.push($(i,l)),k){let F=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));E.push(F),y.push(F)}return E};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"))b=TE({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&q().getBool("WEBGL_EXP_CONV")){let E=h?od(h,!0):null,$=new SE(g,v,E,x,k),F=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],A=S();b=n.runWebGLProgram($,A,"float32",F)}else if(q().getBool("WEBGL_CONV_IM2COL"))b=CE({x:s,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let E=h?od(h,!1):null,$=new kE(g,v,E,x,k),F=S();b=n.runWebGLProgram($,F,"float32")}let C=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var Qee={kernelName:to,backendName:"webgl",kernelFunc:Jee};function ete(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:u,dilations:l,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=r,f=[],m=l;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(c,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${c} and dilations '${m}'`);let g=N.computeConv2DInfo(s.shape,a.shape,c,m,u,p,!0),b=q().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?od(d,b):null,v=[s,a],x=o!=null,k=i!=null,S=d==="leakyrelu";if(x&&v.push(o),k&&v.push(i),S){let F=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(F),f.push(F)}let C;b?C=new EE(g,x,y,k,S):C=new _E(g,x,y,k,S);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],$=n.runWebGLProgram(C,v,"float32",E);return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),$}var tte={kernelName:no,backendName:"webgl",kernelFunc:ete},nte=class{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;let s=mt(n.length),a=`
    int index;`;for(let o=0;o<this.sliceDim;o++)a+=`
          index = round(getIndices(coords[0], ${o}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[o]};
          flattenIndex += index * ${this.strides[o]};`;this.userCode=`
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;

          ${a}

          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `}};function rte(e){let{inputs:t,backend:n}=e,{params:r,indices:s}=t,a=s.shape,o=a[a.length-1],i=w.sizeFromShape(r.shape),[c,u,l,p]=N.prepareAndValidate(r,s),d=he({inputs:{x:s},backend:n,attrs:{shape:[u,o]}}),h=he({inputs:{x:r},backend:n,attrs:{shape:[w.sizeFromShape(r.shape)/l,l]}});if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let b=n.readSync(s.dataId),y=n.bufferSync(r),v=p9(b,y,r.dtype,u,o,l,p,r.shape,i);return n.makeTensorInfo(c,r.dtype,v.values)}let f=new nte(o,p,[u,l],r.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var ste={kernelName:eu,backendName:"webgl",kernelFunc:rte},ate=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=mt(this.rank),r=ote(e,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${r}));
      }
    `}};function ote(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let s=0;s<e.length;s++)s===2?r.push("index"):r.push(`${n[s]}`);return r.join()}function DE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,indices:a}=t,{axis:o,batchDims:i}=r,c=w.parseAxisParam(o,s.shape)[0];if(q().get("DEBUG")){let y=n.readSync(a.dataId),v=s.shape[c];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(s,a,c,i),l=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,l/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,l/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([s,a])||s.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=h9(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(u.outputShape,x.dtype,x.values)}let m=new ate(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var ite={kernelName:Qc,backendName:"webgl",kernelFunc:DE},cte="return float(a > b);",ute=`
  return vec4(greaterThan(a, b));
`,lte=dn({opSnippet:cte,packedOpSnippet:ute,cpuKernelImpl:f9,dtype:"bool"}),dte={kernelName:tu,backendName:"webgl",kernelFunc:lte},pte="return float(a >= b);",hte=`
  return vec4(greaterThanEqual(a, b));
`,fte=dn({opSnippet:pte,packedOpSnippet:hte,dtype:"bool",cpuKernelImpl:m9}),mte={kernelName:Po,backendName:"webgl",kernelFunc:fte};function gte(e){let{inputs:t,backend:n}=e,{input:r}=t;return $E(r,!0,n)}var bte={kernelName:Nf,backendName:"webgl",kernelFunc:gte},yte="return float(!isnan(x) && !isinf(x));",vte=Ye({opSnippet:yte,dtype:"bool"}),xte={kernelName:nu,backendName:"webgl",kernelFunc:vte},wte="return float(isinf(x));",Ite=Ye({opSnippet:wte,dtype:"bool"}),kte={kernelName:ru,backendName:"webgl",kernelFunc:Ite},Ste="return float(isnan(x));",Tte=Ye({opSnippet:Ste,dtype:"bool"}),Cte={kernelName:su,backendName:"webgl",kernelFunc:Tte},Nte="return float(a < b);",_te=`
  return vec4(lessThan(a, b));
`,Ete=dn({opSnippet:Nte,packedOpSnippet:_te,cpuKernelImpl:g9,dtype:"bool"}),Ate={kernelName:au,backendName:"webgl",kernelFunc:Ete},$te="return float(a <= b);",Dte=`
  return vec4(lessThanEqual(a, b));
`,Fte=dn({opSnippet:$te,packedOpSnippet:Dte,cpuKernelImpl:b9,dtype:"bool"}),Rte={kernelName:ou,backendName:"webgl",kernelFunc:Fte};function Pte(e){let{backend:t,attrs:n}=e,{start:r,stop:s,num:a}=n,o=y9(r,s,a);return t.makeTensorInfo([o.length],"float32",o)}var Ote={kernelName:Ef,backendName:"webgl",kernelFunc:Pte},Mte=tl+`
  return x < 0.0 ? 0./0. : log(x);
`,Lte=`
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`,zte=Ye({opSnippet:Mte,packedOpSnippet:Lte,cpuKernelImpl:v9}),Bte={kernelName:Lo,backendName:"webgl",kernelFunc:zte},Wte=tl+`
  return log(1.0 + x);
`,Vte=Ye({opSnippet:Wte}),Ute={kernelName:iu,backendName:"webgl",kernelFunc:Vte},Gte="return float(a >= 1.0 && b >= 1.0);",Hte=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,qte=dn({opSnippet:Gte,packedOpSnippet:Hte,dtype:"bool"}),jte={kernelName:cu,backendName:"webgl",kernelFunc:qte},Kte="return float(!(x >= 1.0));",Xte=Ye({opSnippet:Kte}),Yte={kernelName:uu,backendName:"webgl",kernelFunc:Xte},Zte="return float(a >= 1.0 || b >= 1.0);",Jte=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,Qte=dn({opSnippet:Zte,packedOpSnippet:Jte,dtype:"bool"}),ene={kernelName:lu,backendName:"webgl",kernelFunc:Qte},tne=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,c=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${c})`:s===1?i=`1.0/(${c})`:i=`exp(log(${c}) * 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 <=  ${o}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${i};
        setOutput(val);
      }
    `}},nne=class{constructor(e,t,n,r,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,c=`float(${n}) + float(${r}) * sum`;s===.5?i=`inversesqrt(${c})`:s===1?i=`1.0/(${c})`:i=`exp(log(${c}) * 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 <= ${a}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));

          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 * ${i};
        setOutput(result);
      }
    `}},rne=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{depthRadius:a,bias:o,alpha:i,beta:c}=r,u=q().getBool("WEBGL_PACK_NORMALIZATION")?new nne(s.shape,a,o,i,c):new tne(s.shape,a,o,i,c);return n.runWebGLProgram(u,[s],s.dtype)},sne={kernelName:bd,backendName:"webgl",kernelFunc:rne},ane=class{constructor(e,t,n,r,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,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(${r}) * norm + float(${n});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${r})
                * float(${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);
      }
    `}},one=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s,y:a,dy:o}=t,{depthRadius:i,bias:c,alpha:u,beta:l}=r,p=new ane(s.shape,i,c,u,l);return n.runWebGLProgram(p,[s,a,o],s.dtype)},ine={kernelName:Af,backendName:"webgl",kernelFunc:one};function cne(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=he({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),c=Si(i,e.dtype,"max",r),u=he({inputs:{x:c},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(c),u}function FE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reductionIndices:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=N.getAxesPermutation(u,i),p=l!=null,d=n.shouldExecuteOnCPU([s]),h=s;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(i);for(let C=0;C<x.length;C++)x[C]=s.shape[l[C]];let k=aI(v,s.shape,s.dtype,l,x);h=n.makeTensorInfo(x,s.dtype);let S=n.texData.get(h.dataId);S.values=k}else h=Zm(s,l,n);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[f,m]=N.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=N.expandShapeToKeepDim(f,c));let b;if(d){let v=n.texData.get(h.dataId).values,x=x9(v,w.sizeFromShape(m),g,s.dtype);b=n.makeTensorInfo(g,s.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=cne(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var une={kernelName:zo,backendName:"webgl",kernelFunc:FE},lne=oI+`
  return max(a, b);
`,dne=`
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+cp+`
  return result;
`,pne=dn({opSnippet:lne,packedOpSnippet:dne,cpuKernelImpl:w9}),hne={kernelName:Bo,backendName:"webgl",kernelFunc:pne};function fne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t;Yu(s,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:c}=r,u=1;w.assert(N.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let l=N.computePool2DInfo(s.shape,a,o,u,i,c);if(l.filterWidth===1&&l.filterHeight===1&&w.arraysEqual(l.inShape,l.outShape))return sr({inputs:{x:s},backend:n});let p=new id(l,"max",!1);return n.runWebGLProgram(p,[s],s.dtype)}var mne={kernelName:Wo,backendName:"webgl",kernelFunc:fne};function gne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{filterSize:a,strides:o,pad:i,dataFormat:c,dimRoundingMode:u}=r,l=[1,1,1],p=N.computePool3DInfo(s.shape,a,o,l,i,u,c),d=new cI(p,"max",!1);return n.runWebGLProgram(d,[s],s.dtype)}var bne={kernelName:yd,backendName:"webgl",kernelFunc:gne},yne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=s-1-e.padInfo.top,i=a-1-e.padInfo.left,c=s*a-1;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${i});

      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 += ${r}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

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

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

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

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${c} - 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);
      }
    `}},vne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,c=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.front,p=c-1-e.padInfo.top,d=u-1-e.padInfo.left,h=i*c*u-1;this.userCode=`
      const ivec3 pads = ivec3(${l}, ${p}, ${d});

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

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

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

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

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

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

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function xne(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a}=t,o=a,{filterSize:i,strides:c,pad:u,dimRoundingMode:l}=r,p=[1,1,1],d=N.computePool3DInfo(o.shape,i,c,p,u,l),h=new cI(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new vne(d),g=n.runWebGLProgram(m,[s,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var wne={kernelName:Df,backendName:"webgl",kernelFunc:xne};function Ine(e){let{inputs:t,backend:n,attrs:r}=e,{dy:s,input:a,output:o}=t,i=a;Yu([a,o],"maxPoolGrad");let{filterSize:c,strides:u,pad:l,dimRoundingMode:p}=r,d=N.computePool2DInfo(i.shape,c,u,1,l,p),h=!0,f=new id(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new yne(d),b=n.runWebGLProgram(g,[s,m],i.dtype);return n.disposeIntermediateTensorInfo(m),b}var kne={kernelName:$f,backendName:"webgl",kernelFunc:Ine};function Sne(e,t,n,r){let s=new id(n,"max",!1),a=r.runWebGLProgram(s,[e],"float32");s=new id(n,"max",!0,!0,t);let o=r.runWebGLProgram(s,[e],"float32");return[a,o]}var Tne={kernelName:Ff,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:s,strides:a,pad:o,includeBatchInIndex:i}=t,c=n;w.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let l=N.computePool2DInfo(r.shape,s,a,u,o),[p,d]=Sne(r,i,l,c);return[p,d]}};function Cne(e,t,n,r){let s=w.sizeFromShape(t),o=w.sizeFromShape(e.shape)/s,i=he({inputs:{x:e},attrs:{shape:[o,s]},backend:r}),c=Si(i,"float32","mean",r),u=he({inputs:{x:c},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(c),u}var Nne={kernelName:Vo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:s,axis:a}=t,o=n,i=r.shape.length,c=w.parseAxisParam(a,r.shape),u=c,l=N.getAxesPermutation(u,i),p=l!=null,d=o.shouldExecuteOnCPU([r]),h=[],f=r;if(p){if(d){let x=o.texData.get(f.dataId).values,k=new Array(i);for(let E=0;E<k.length;E++)k[E]=r.shape[l[E]];let S=aI(x,r.shape,r.dtype,l,k);f=o.makeTensorInfo(k,r.dtype);let C=o.texData.get(f.dataId);C.values=S}else f=Zm(r,l,o);h.push(f),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),b=m;s&&(b=N.expandShapeToKeepDim(m,c));let y=Cne(f,g,b,o);for(let v of h)o.disposeIntermediateTensorInfo(v);return y}};function _ne(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=w.parseAxisParam(a,s.shape),u=c,l=N.getAxesPermutation(u,i),p=s;l!=null&&(p=Nn({inputs:{x:s},backend:n,attrs:{perm:l}}),u=N.getInnerMostAxes(u.length,s.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[d,h]=N.computeOutAndReduceShapes(p.shape,u),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Si(m,m.dtype,"min",n),b;if(o){let y=N.expandShapeToKeepDim(d,c);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),l!=null&&n.disposeIntermediateTensorInfo(p),b}var Ene={kernelName:Uo,backendName:"webgl",kernelFunc:_ne},Ane=oI+`
  return min(a, b);
`,$ne=`
  vec4 result = vec4(min(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  `+cp+`
  return result;
`,Dne=dn({opSnippet:Ane,packedOpSnippet:$ne,cpuKernelImpl:I9}),Fne={kernelName:Go,backendName:"webgl",kernelFunc:Dne},Rne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let r=e.length,s=mt(r),a=t.map(u=>u[0]).join(","),o=t.map((u,l)=>u[0]+e[l]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),c=n==="reflect"?0:1;if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

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

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}},One=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:s,mode:a}=n,o=q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Pne(r.shape,s,a):new Rne(r.shape,s,a);return t.runWebGLProgram(o,[r],r.dtype)},Mne={kernelName:Ho,backendName:"webgl",kernelFunc:One},Lne=`if (b == 0.0) return NAN;
  return mod(a, b);`,zne=`
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  `+cp+`
  return result;
`,Bne=dn({opSnippet:Lne,packedOpSnippet:zne}),Wne={kernelName:du,backendName:"webgl",kernelFunc:Bne},Vne=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
      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}));
      }
    `}},Une=`
if (a == b) {
  return 1.0;
};
return a / b;`,Gne=`
  // 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;
`,RE=dn({opSnippet:Une,packedOpSnippet:Gne,checkOutOfBounds:!0}),Hne={kernelName:Eo,backendName:"webgl",kernelFunc:RE},F1="return a - b;",PE=dn({opSnippet:F1,packedOpSnippet:F1,supportsComplex:!0,cpuKernelImpl:V9}),qne={kernelName:di,backendName:"webgl",kernelFunc:PE};function OE(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:a}=r,o=w.parseAxisParam([a],s.shape),i=FE({inputs:{x:s},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),c=N.expandShapeToKeepDim(i.shape,o),u=he({inputs:{x:i},backend:n,attrs:{shape:c}}),l=PE({inputs:{a:s,b:u},backend:n}),p=AE({inputs:{x:l},backend:n}),d=Jm({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=he({inputs:{x:d},backend:n,attrs:{shape:c}}),f=RE({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var jne={kernelName:ui,backendName:"webgl",kernelFunc:OE};function Kne(e){let{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{numSamples:a,seed:o,normalized:i}=r,c=i?s:OE({inputs:{logits:s},backend:n,attrs:{dim:s.shape.length-1}}),u=c.shape[0],l=c.shape[1],p=new Vne(u,l,a),d=[[o]],h=n.runWebGLProgram(p,[c],"int32",d);return i||n.disposeIntermediateTensorInfo(c),h}var Xne={kernelName:Rf,backendName:"webgl",kernelFunc:Kne},Yne=Or+`
  return -x;
`,Zne=`
  vec4 result = -x;
  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;
`;function Jne(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let a=n.texData.get(r.dataId),[o,i]=S9(a.values,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,o)}let s;return q().getBool("WEBGL_PACK_UNARY_OPERATIONS")?s=new Ka(r.shape,Zne):s=new Ns(r.shape,Yne),n.runWebGLProgram(s,[r],r.dtype)}var Qne={kernelName:pu,backendName:"webgl",kernelFunc:Jne},ere=fs.nonMaxSuppressionV3Impl;function tre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c}=r,u=n.readSync(s.dataId),l=n.readSync(a.dataId),{selectedIndices:p}=ere(u,l,o,i,c);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var nre={kernelName:fu,backendName:"webgl",kernelFunc:tre},rre=fs.nonMaxSuppressionV4Impl;function sre(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,padToMaxOutputSize:u}=r,l=n.readSync(s.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=rre(l,p,o,i,c,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var are={kernelName:mu,backendName:"webgl",kernelFunc:sre},ore=fs.nonMaxSuppressionV5Impl;function ire(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:s,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:c,softNmsSigma:u}=r,l=n.readSync(s.dataId),p=n.readSync(a.dataId),d=o,h=i,f=c,m=u,{selectedIndices:g,selectedScores:b}=ore(l,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var cre={kernelName:gu,backendName:"webgl",kernelFunc:ire},ure=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${r}), float(${n}),
                      float(index == coords.y)));
      }
    `}},lre=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:s}=t,{dtype:a,depth:o,onValue:i,offValue:c}=r,u=w.sizeFromShape(s.shape),l=new ure(u,o,i,c),p=he({inputs:{x:s},backend:n,attrs:{shape:[u]}}),d=n.runWebGLProgram(l,[p],a);n.disposeIntermediateTensorInfo(p);let h=[...s.shape,o],f=he({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),f},dre={kernelName:jo,backendName:"webgl",kernelFunc:lre};function af(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let s=lp({inputs:{input:r},backend:n}),a=af({inputs:{x:s},backend:n}),o=Qm({inputs:{input:r},backend:n}),i=af({inputs:{x:o},backend:n}),c=Ea({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return dp({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var pre={kernelName:Pu,backendName:"webgl",kernelFunc:af};function ME(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let s=lp({inputs:{input:r},backend:n}),a=ME({inputs:{x:s},backend:n}),o=Qm({inputs:{input:r},backend:n}),i=af({inputs:{x:o},backend:n}),c=Ea({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),c}else return dp({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var hre={kernelName:bu,backendName:"webgl",kernelFunc:ME};function fre(e){let{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(t.length===1)return xv({inputs:{input:t[0]},backend:n,attrs:{dim:s}});let a=t[0].shape,o=t[0].dtype;t.forEach(l=>{w.assertShapesMatch(a,l.shape,"All tensors passed to stack must have matching shapes"),w.assert(o===l.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],c=t.map(l=>{let p=xv({inputs:{input:l},backend:n,attrs:{dim:s}});return i.push(p),p}),u=IE({inputs:c,backend:n,attrs:{axis:s}});return i.forEach(l=>n.disposeIntermediateTensorInfo(l)),u}var mre={kernelName:yu,backendName:"webgl",kernelFunc:fre},gre=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,s=mt(r),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
        int start = ${a};
        int end = ${o};

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${i}));
        }
      }
    `}},bre=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,s=mt(r),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Tn("rc",r),c=Tn("source",r),u=`${i[r-1]} < ${this.outputShape[r-1]}`,l=r===1?"source":`vec2(${c.slice(-2).join()})`,p=[`${s} rc = outputLoc;`,`${i[r-1]} += 1;
       if(${u}) {
      `,r===1?"":`}
       rc = outputLoc;
       ${i[r-2]} += 1;
       if(${i[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":`  ${i[r-1]} += 1;
         if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=r===1?2:4;f<m;f++)h+=`
        ${p[f]}
        if (${d}) {
          result[${f}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${f}] = getChannel(getX(${c.join()}), ${l});
        }
      `;h+=r===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${o});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `}},LE=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{paddings:a,constantValue:o}=r;if(w.sizeFromShape(s.shape)===0){let u=a.map((l,p)=>l[0]+s.shape[p]+l[1]);return dp({backend:n,attrs:{shape:u,value:o,dtype:s.dtype}})}let i=q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bre(s.shape,a,o):new gre(s.shape,a,o),c=[[o]];return n.runWebGLProgram(i,[s],s.dtype,c)},yre={kernelName:Ko,backendName:"webgl",kernelFunc:LE},vre=`
  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);
`,xre=`
  // 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;

  bvec4 isNaN1 = lessThan(a, vec4(0.0));
  bvec4 isNaN2 = lessThan(floor(b), b);
  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
  `+cp+`
  return result;
`,wre=dn({opSnippet:vre,packedOpSnippet:xre}),Ire={kernelName:Xo,backendName:"webgl",kernelFunc:wre};function kre(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,keepDims:o}=r,i=s.shape.length,c=[],u=w.parseAxisParam(a,s.shape),l=u,p=N.getAxesPermutation(l,i),d=s;p!=null&&(d=Nn({inputs:{x:s},backend:n,attrs:{perm:p}}),l=N.getInnerMostAxes(l.length,i),c.push(d)),N.assertAxesAreInnerMostDims("prod",l,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=C9(d.shape,d.dtype,f,l);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=N.computeOutAndReduceShapes(d.shape,l),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=Hf(s.dtype),v=Si(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),c.push(b),c.push(v)}if(o){c.push(h);let f=N.expandShapeToKeepDim(h.shape,u);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Sre={kernelName:Zo,backendName:"webgl",kernelFunc:kre};function Tre(e){let{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:s,paramsDenseValues:a,indices:o}=t,{outputRaggedRank:i}=r,c=s.map(b=>n.readSync(b.dataId)),u=s.map(b=>b.shape),l=n.readSync(a.dataId),p=n.readSync(o.dataId),[d,h,f]=N9(c,u,l,a.shape,a.dtype,p,o.shape,i),m=d.map(b=>n.makeTensorInfo([b.length],"int32",b)),g=n.makeTensorInfo(f,a.dtype,h);return m.concat([g])}var Cre={kernelName:Pf,backendName:"webgl",kernelFunc:Tre};function Nre(e){let{inputs:t,backend:n}=e,{starts:r,limits:s,deltas:a}=t,o=n.readSync(r.dataId),i=n.readSync(s.dataId),c=n.readSync(a.dataId),[u,l]=_9(o,r.shape,r.dtype,i,s.shape,c,a.shape),p=n.makeTensorInfo([u.length],"int32",u),d=n.makeTensorInfo([l.length],r.dtype,l);return[p,d]}var _re={kernelName:Of,backendName:"webgl",kernelFunc:Nre};function Ere(e){let{inputs:t,backend:n,attrs:r}=e,{shape:s,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:c}=r,u=n.readSync(s.dataId),l=n.readSync(a.dataId),p=n.readSync(o.dataId),d=i.map(g=>n.readSync(g.dataId)),h=i.map(g=>g.shape),[f,m]=E9(u,s.shape,l,a.shape,a.dtype,p,o.shape,d,h,c);return n.makeTensorInfo(f,a.dtype,m)}var Are={kernelName:Mf,backendName:"webgl",kernelFunc:Ere},zE=e=>{let{backend:t,attrs:n}=e,{start:r,stop:s,step:a,dtype:o}=n,i=A9(r,s,a,o);return t.makeTensorInfo([i.length],o,i)},$re={kernelName:vd,backendName:"webgl",kernelFunc:zE},Dre="return 1.0 / x;",Fre=Ye({opSnippet:Dre}),Rre={kernelName:vu,backendName:"webgl",kernelFunc:Fre},Pre=Or+`
  return (x < 0.0) ? 0.0 : x;
`,Ore=`
  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;
`,Mre=Ye({opSnippet:Pre,packedOpSnippet:Ore}),Lre={kernelName:Jo,backendName:"webgl",kernelFunc:Mre},zre=Or+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Bre=`
  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;
`,Wre=Ye({opSnippet:zre,packedOpSnippet:Bre}),Vre={kernelName:ti,backendName:"webgl",kernelFunc:Wre},Ure=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p;s?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${u[0]/l[0]},
          ${u[1]/l[1]});
      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);

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

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

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

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

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

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

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

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

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

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

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

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

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

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

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

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

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

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

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

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${l});

        const float invHeightScale = float(${p});
        const float invWidthScale = float(${d});

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

        // 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 >= ${o}) {
              continue;
            }

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

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

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

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

        setOutput(newValue);
      }
    `}},Zre=class{constructor(e,t,n,r,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,c]=e;this.outputShape=[a,t,n,c];let u=[r&&t>1?o-1:o,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],p=r?"0.5":"0.0",d;s?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${u[0]/l[0]},
          ${u[1]/l[1]},
          ${u[1]/l[1]});
      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
                                     ${i}.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 = ${d};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));

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

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

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

        float accumulator = 0.0;

        const float heightScale = float(${u});
        const float widthScale = float(${l});

        const float invHeightScale = float(${p});
        const float invWidthScale = float(${d});

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

        // 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 >= ${o}) {
              continue;
            }

            float sourceFracRow =
              float(${i[0]}) *
                (float(dyR) / float(${c[0]}));

            float sourceFracCol =
                float(${i[1]}) *
                  (float(dyC) / float(${c[1]}));

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

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

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

        setOutput(accumulator);
      }
    `}};function tse(e){let{inputs:t,backend:n,attrs:r}=e,{images:s,dy:a}=t,{alignCorners:o}=r,i=new ese(a.shape,s.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var nse={kernelName:zf,backendName:"webgl",kernelFunc:tse},rse=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;return}let r=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,s=e.map((o,i)=>r(i)).join(","),a=mt(n);this.userCode=`
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}},sse=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=Tn("rc",n),s=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=mt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${o} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${i(r.slice())};
          if(${s}){
            result.g = ${c(r.slice())};
          }
          if(${a}) {
            result.b = ${u(r.slice())};
            if(${s}) {
              result.a = ${l(r.slice())};
            }
          }
          setOutput(result);
        }
    `;function i(h){return p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function ase(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{dims:a}=r,o=s.shape.length,i=w.parseAxisParam(a,s.shape);if(o===0)return sr({inputs:{x:s},backend:n});let c=q().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sse(s.shape,i):new rse(s.shape,i);return n.runWebGLProgram(c,[s],s.dtype)}var ose={kernelName:ni,backendName:"webgl",kernelFunc:ase},ise=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],r=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
        vec3 fill = vec3(${t.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) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${s}
          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}},cse={kernelName:Ou,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:s,fillValue:a,center:o}=t,i=n,c=new ise(r.shape,a),[u,l]=N.getImageCenter(o,r.shape[1],r.shape[2]),p=[[u,l,Math.sin(s),Math.cos(s)]];return i.runWebGLProgram(c,[r],r.dtype,p)}},use=`
  // 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;
    }
  }
`,lse=Ye({opSnippet:use}),dse={kernelName:ri,backendName:"webgl",kernelFunc:lse},pse="return inversesqrt(x);",hse=Ye({opSnippet:pse,cpuKernelImpl:$9}),fse={kernelName:si,backendName:"webgl",kernelFunc:hse},BE=class{constructor(e,t,n,r,s,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=mt(s.length),c=mt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let l=`getIndices(${u})`,p="";r===1?p="i":r===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
        ${i} strides = ${i}(${s});

        void main() {
          ${c} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t}; j++) {
              int index = round(${l});
              flattenedIndex += index * ${h};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `}};function mse(e){let{inputs:t,backend:n,attrs:r}=e,{indices:s,updates:a}=t,{shape:o}=r,{sliceRank:i,numUpdates:c,sliceSize:u,strides:l,outputSize:p}=N.calculateShapes(a,s,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,s.dtype);let h=he({inputs:{x:s},backend:n,attrs:{shape:[c,i]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[c,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new BE(c,i,h.shape.length,f.shape.length,l,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var gse={kernelName:wu,backendName:"webgl",kernelFunc:mse},bse=class{constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let s="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=q().getNumber("WEBGL_VERSION")===2?s:a,i=r==="left"?"<":"<=";this.userCode=`
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${o}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${i} value) {
             left = mid + 1;
           } else {
             right = mid;
           }
         }
         return right;
       }

       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int valueIndex = coords[1];

         float value = getValues(batch, valueIndex);

         setOutput(float(findBound(batch, value)));
       }
     `}};function yse(e){let{inputs:t,backend:n,attrs:r}=e,{sortedSequence:s,values:a}=t,{side:o}=r,i=new bse(s.shape[0],s.shape[1],a.shape[1],o),c=[[s.shape[1]]];return n.runWebGLProgram(i,[s,a],"int32",c)}var vse={kernelName:Wf,backendName:"webgl",kernelFunc:yse},xse=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",r="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],c=[];for(let u=0;u<t.length;u++)c.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);r=i.join(),s=c.join()}let a=mt(n);this.userCode=`
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${r});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function wse(e){let{inputs:t,backend:n}=e,{condition:r,t:s,e:a}=t,o=new xse(r.shape.length,s.shape,s.shape.length);return n.runWebGLProgram(o,[r,s,a],hr(s.dtype,a.dtype))}var Ise={kernelName:Iu,backendName:"webgl",kernelFunc:wse},kse=`
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${N.SELU_SCALEALPHA};
  float scale = ${N.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Sse=Ye({opSnippet:kse}),Tse={kernelName:ku,backendName:"webgl",kernelFunc:Sse},Cse=tl+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,Nse=`
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  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;
`,_se=Ye({opSnippet:Cse,packedOpSnippet:Nse,cpuKernelImpl:F9}),Ese={kernelName:oi,backendName:"webgl",kernelFunc:_se},Ase=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,$se=Ye({opSnippet:Ase}),Dse={kernelName:Cu,backendName:"webgl",kernelFunc:$se},Fse=tl+`
  return sin(x);
`,Rse=Ye({opSnippet:Fse}),Pse={kernelName:ai,backendName:"webgl",kernelFunc:Rse},Ose=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,Mse=Ye({opSnippet:Ose}),Lse={kernelName:Tu,backendName:"webgl",kernelFunc:Mse},zse=`
  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;
`,Bse=Ye({opSnippet:zse}),Wse={kernelName:Nu,backendName:"webgl",kernelFunc:Bse},Vse=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,paddings:o}=r;w.assert(s.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((b,y)=>b*y),c=[[0,0]];c.push(...o);for(let b=1+a.length;b<s.shape.length;++b)c.push([0,0]);let u=[],l=LE({inputs:{x:s},backend:n,attrs:{paddings:c,constantValue:0}}),p=N.getReshaped(l.shape,a,i,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(l.shape,a,i,!1),f=he({inputs:{x:l},backend:n,attrs:{shape:p}}),m=Nn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(l),u.push(f),u.push(m),u.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},Use={kernelName:_u,backendName:"webgl",kernelFunc:Vse};function Gse(e){let{inputs:t,backend:n}=e,{indices:r,values:s,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);if(r.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${r.shape}`);if(s.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${s.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${o.shape}`);let i=n.readSync(r.dataId),c=n.readSync(s.dataId),u=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[p,d,h,f,m]=P9(i,r.shape,r.dtype,c,s.dtype,u,l);return[n.makeTensorInfo(d,r.dtype,p),n.makeTensorInfo([d[0]],s.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}var Hse={kernelName:xd,backendName:"webgl",kernelFunc:Gse};function qse(e){let{inputs:t,backend:n}=e,{inputIndices:r,inputShape:s,newShape:a}=t;if(r.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${s.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(s.dataId)),i=n.readSync(r.dataId),c=Array.from(n.readSync(a.dataId)),[u,l,p]=O9(i,r.shape,r.dtype,o,c);return[n.makeTensorInfo(l,r.dtype,u),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var jse={kernelName:Au,backendName:"webgl",kernelFunc:qse};function Kse(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),c=n.readSync(a.dataId),[u,l]=cE(o,r.shape,r.dtype,i,c,!0);return n.makeTensorInfo(l,r.dtype,u)}var Xse={kernelName:wd,backendName:"webgl",kernelFunc:Kse};function Yse(e){let{inputs:t,backend:n}=e,{data:r,indices:s,segmentIds:a}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(s.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${s.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${a.shape}`);let o=n.readSync(r.dataId),i=n.readSync(s.dataId),c=n.readSync(a.dataId),[u,l]=cE(o,r.shape,r.dtype,i,c);return n.makeTensorInfo(l,r.dtype,u)}var Zse={kernelName:Id,backendName:"webgl",kernelFunc:Yse};function Jse(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:s,sparseValues:a,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:c,numUpdates:u,sliceSize:l,strides:p,outputSize:d}=N.calculateShapes(a,s,i),h=!1;if(a.dtype==="string"){let b=n.bufferSync(s),y=n.bufferSync(a),v=w.decodeString(n.readSync(o.dataId)[0]),x=D9(b,y,i,d,l,u,c,p,v,h);return n.makeTensorInfo(i,x.dtype,x.values)}let f=new BE(u,c,s.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,s,o],a.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var Qse={kernelName:Vf,backendName:"webgl",kernelFunc:Jse};function eae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{numOrSizeSplits:a,axis:o}=r,i=w.parseAxisParam(o,s.shape)[0],c=N.prepareSplitSize(s,a,i),u=s.shape.length,l=new Array(u).fill(0),p=s.shape.slice();return c.map(d=>{let h=[...p];h[i]=d;let f=nl({inputs:{x:s},backend:n,attrs:{begin:l,size:h}});return l[i]+=d,f})}var tae={kernelName:Eu,backendName:"webgl",kernelFunc:eae},R1="return sqrt(x);",nae=Ye({opSnippet:R1,packedOpSnippet:R1,cpuKernelImpl:M9}),rae={kernelName:ii,backendName:"webgl",kernelFunc:nae},sae="return x * x;",aae=Ye({opSnippet:sae}),oae={kernelName:kd,backendName:"webgl",kernelFunc:aae},P1="return (a - b) * (a - b);",iae=dn({opSnippet:P1,packedOpSnippet:P1}),cae={kernelName:li,backendName:"webgl",kernelFunc:iae};function uae({inputs:e,attrs:t,backend:n}){let{x:r}=e,s=Or+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,a=new Ns(r.shape,s);return n.runWebGLProgram(a,[r],r.dtype)}var lae={kernelName:xa,backendName:"webgl",kernelFunc:uae},dae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,s=mt(n.length),a=mt(n.length),o="";if(r===1)o="coords * strides + begin";else{let i=0;o=n.map((c,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${e});
      ${s} strides = ${s}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${o}));
      }
    `}};function pae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{begin:a,end:o,strides:i,beginMask:c,endMask:u,ellipsisMask:l,newAxisMask:p,shrinkAxisMask:d}=r,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=qt.sliceInfo(s.shape,a,o,i,c,u,l,p,d),k;if(m)k=he({inputs:{x:s},backend:n,attrs:{shape:f}});else if(g||b){w.assert(s.shape.length>=1,()=>`Input must have rank at least 1, got: ${s.shape.length}`);let C=qt.computeOutShape(y,v,x),E=nl({inputs:{x:s},backend:n,attrs:{begin:y,size:C}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([s])){let E=n.readSync(s.dataId),$=Me(s.shape,s.dtype,E),F=L9(h,$,x,y);k=n.makeTensorInfo(f,s.dtype,F.values)}else{let E=new dae(y,x,h);k=n.runWebGLProgram(E,[s],s.dtype)}let S=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),S}var hae={kernelName:$u,backendName:"webgl",kernelFunc:pae};function fae(e){let{inputs:t,backend:n,attrs:r}=e,{separator:s,nGramWidths:a,leftPad:o,rightPad:i,padWidth:c,preserveShortSequences:u}=r,{data:l,dataSplits:p}=t,d=n.readSync(l.dataId),h=n.readSync(p.dataId),[f,m]=z9(d,h,s,a,o,i,c,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var mae={kernelName:Sd,backendName:"webgl",kernelFunc:fae};function gae(e){let{inputs:t,backend:n,attrs:r}=e,{skipEmpty:s}=r,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[u,l,p]=B9(i,c,s),d=l.length;return[n.makeTensorInfo([d,2],"int32",u),n.makeTensorInfo([d],"string",l),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var bae={kernelName:Td,backendName:"webgl",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:r}=e,{numBuckets:s}=r,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(s<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=W9(o,s);return n.makeTensorInfo(a.shape,"int32",i)}var vae={kernelName:Cd,backendName:"webgl",kernelFunc:yae},xae="return tan(x);",wae=Ye({opSnippet:xae}),Iae={kernelName:pi,backendName:"webgl",kernelFunc:wae},kae=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Sae=Ye({opSnippet:kae}),Tae={kernelName:hi,backendName:"webgl",kernelFunc:Sae},Cae=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let r=mt(this.rank),s=Nae(e);this.userCode=`
      void main() {
        ${r} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function Nae(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let s=0;s<e.length;s++)r.push(`imod(${n[s]}, ${e[s]})`);return r.join()}function WE(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{reps:a}=r;if(s.dtype==="string"||s.shape.length>5){let c=n.readSync(s.dataId),u=s.dtype==="string"?c.map(d=>w.decodeString(d)):c,l=Me(s.shape,s.dtype,u),p=U9(l,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Cae(s.shape,a);return n.runWebGLProgram(o,[s],s.dtype)}var _ae={kernelName:va,backendName:"webgl",kernelFunc:WE},Eae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.

         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;

         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;

         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `}},Aae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.

         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));

         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;

         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `}};function za(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function O1(e){let t=1;for(;t<e;)t*=2;return t}function $ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{k:a,sorted:o}=r,i=q().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),c=q().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=s.shape,l=u[u.length-1];if(n.shouldExecuteOnCPU([s])||l<i||a>c){let F=n.readSync(s.dataId),[A,R]=G9(F,u,s.dtype,a,o);return[n.makeTensorInfo(A.shape,A.dtype,A.values),n.makeTensorInfo(R.shape,R.dtype,R.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,s.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(l===1)return[s,dp({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(s.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(s):s,m=w.sizeFromShape(u)/l,g=he({inputs:{x:h},attrs:{shape:[m,l]},backend:n});d&&za(n,h);let b=O1(a),y=O1(l),v=null,x=()=>v===null?[g,g]:[g,v],k=(F,A,R)=>{let T=x(),L=new Eae(R),G=[[l],[v===null?1:0],[Number.NEGATIVE_INFINITY],[F],[A]],j=v;v=n.runWebGLProgram(L,T,"int32",G),za(n,j)};for(let F=1;F<b;F*=2){let A=F*2;for(let R=F;R>=1;R/=2)k(A,R,[m,y])}for(let F=y;F>b;F/=2){let A=x(),R=new Aae([m,F/2]),L=[[l],[v===null?1:0],[b]],V=v;v=n.runWebGLProgram(R,A,"int32",L),za(n,V);let G=b/2,j=G*2;for(let H=G;H>=1;H/=2)k(j,H,v.shape)}let S=v;v=nl({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),za(n,S);let C=DE({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});za(n,g);let E=u.slice(0,-1);E.push(a),S=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),za(n,S);let $=C;return C=he({inputs:{x:C},attrs:{shape:E},backend:n}),za(n,$),[C,v]}var Dae={kernelName:Du,backendName:"webgl",kernelFunc:$ae},Fae=class{constructor(e,t,n,r,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(r){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${i} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${i} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${s});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${s});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${o} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `}};function Rae(e){let{inputs:t,backend:n,attrs:r}=e,{image:s,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:c,outputShape:u}=r,[l,p,d,h]=s.shape,[f,m]=u!=null?u:[p,d],g=[l,f,m,h],b=new Fae(p,d,o,i,c,g);return n.runWebGLProgram(b,[s,a],"float32")}var Pae={kernelName:Fu,backendName:"webgl",kernelFunc:Rae};function Oae(e){let{inputs:t,attrs:n,backend:r}=e,{axis:s}=n,{x:a}=t;Yu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=r.readSync(a.dataId),{outputValues:i,outputShape:c,indices:u}=H9(o,s,a.shape,a.dtype);return[r.makeTensorInfo(c,a.dtype,i),r.makeTensorInfo([u.length],"int32",u)]}var Mae={kernelName:Uf,backendName:"webgl",kernelFunc:Oae};function Lae(e){let{inputs:t,backend:n,attrs:r}=e,{value:s}=t,{axis:a}=r;a<0&&(a+=s.shape.length);let o=s,i=o.shape.length,c=s.shape[a],u=new Array(i-1),l=0;for(let m=0;m<i;m++)m!==a&&(u[l++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(c);for(let m=0;m<f.length;m++){d[a]=m;let g=nl({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var zae={kernelName:Ru,backendName:"webgl",kernelFunc:Lae},Bae=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,s=e.inSize,a=e.numSegments,o=a*Math.ceil(s/n);this.outputShape=[r,o];let i="0.0",c="sumValue",u=Math.floor(n/4)*4,l=n%4,p=`
        sumValue += dot(values, segFilter);
    `,d="";s%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);let h="";s%n>0&&(h=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${i};

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

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

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

        float sumValue = 0.0;

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

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

          ${p}
        }

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

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

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

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

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

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

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

          ${p}
        }
        setOutput(${c});
      }
    `}};function Wae(e){let{inputs:t,backend:n,attrs:r}=e,{x:s,segmentIds:a}=t,{numSegments:o}=r,i=s.shape.length,c=[],u=0,l=N.getAxesPermutation([u],i),p=s;l!=null&&(p=Nn({inputs:{x:s},backend:n,attrs:{perm:l}}),c.push(p),u=N.getInnerMostAxes(1,i)[0]);let d=N.segment_util.computeOutShape(p.shape,u,o),h=w.sizeFromShape([p.shape[u]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});c.push(f);let m=Hf(s.dtype),g=(x,k,S,C,E)=>{let $=x.shape[0],F=x.shape[1],A=N.segment_util.segOpComputeOptimalWindowSize(F,E),R={windowSize:A,inSize:F,batchSize:$,numSegments:E},T=new Bae(R,k),L=n.compileAndRun(T,[x,S],C);if(c.push(L),L.shape[1]===E)return L;let V=zE({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),G=WE({inputs:{x:V},backend:n,attrs:{reps:[F/A]}});return c.push(V),c.push(G),g(L,k,G,C,E)},b=g(f,"unsortedSegmentSum",a,m,o),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(l!=null){c.push(y);let x=N.getUndoAxesPermutation(l);v=Nn({inputs:{x:v},backend:n,attrs:{perm:x}})}return c.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var Vae={kernelName:Nd,backendName:"webgl",kernelFunc:Wae},Uae=[LZ,BZ,UZ,qZ,KZ,ZZ,QZ,tJ,aJ,iJ,lJ,hJ,gJ,xJ,kJ,TJ,NJ,$J,FJ,PJ,zJ,qJ,KJ,YJ,nQ,sQ,cQ,wZ,dQ,gQ,xQ,CQ,_Q,AQ,DQ,RQ,MQ,BQ,UQ,HQ,jQ,XQ,JQ,eee,see,oee,uee,pee,fee,yee,Iee,Cee,Eee,Dee,Fee,Pee,Mee,zee,Wee,Uee,jee,Yee,Qee,tte,ste,ite,dte,mte,xZ,bte,fQ,xte,kte,Cte,kZ,Ate,Rte,Ote,Bte,Ute,jte,Yte,ene,sne,ine,une,hne,mne,bne,wne,kne,Tne,Nne,Ene,Fne,Mne,Wne,Xne,CZ,Qne,nre,are,cre,JJ,dre,hre,mre,yre,Ire,TZ,Sre,Cre,_re,Are,$re,QJ,Hne,Rre,Lre,Vre,_Z,qre,Xre,Qre,nse,ose,cse,dse,fse,gse,vse,Ise,Tse,Ese,Dse,Pse,Lse,GJ,jne,Wse,Use,Hse,jse,Xse,Zse,Qse,tae,rae,oae,cae,lae,hae,mae,bae,vae,qne,PZ,Iae,Tae,_ae,Dae,Pae,OZ,Mae,zae,Vae,pre];for(let e of Uae)_d(e);var Et;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Et||(Et={}));var ud;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(ud||(ud={}));var VE;function Gae(e){VE=e.wasm.cwrap(eo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Hae(e){let{inputs:t,backend:n,attrs:r}=e,{a:s,b:a,bias:o,preluActivationWeights:i}=t;if(s.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:c,transposeB:u,activation:l,leakyreluAlpha:p}=r,d=n.dataIdMap.get(s.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=ud[l];if(g==null)throw new Error(`${l} activation not yet supported for FusedConv2D in the wasm backend.`);let b=c?s.shape[2]:s.shape[1],y=u?a.shape[1]:a.shape[2],v=Mu.assertAndGetBroadcastShape(s.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],s.dtype),k=n.dataIdMap.get(x.dataId).id,S=new Uint8Array(new Int32Array(s.shape).buffer),C=new Uint8Array(new Int32Array(a.shape).buffer);return VE(d,S,s.shape.length,h,C,a.shape.length,c,u,g,f,m,p||0,k),x}var qae={kernelName:eo,backendName:"wasm",setupFunc:Gae,kernelFunc:Hae};function rn(e,t){let n;function r(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function s(a){let{backend:o,inputs:{x:i}}=a,c=o.dataIdMap.get(i.dataId).id,u=o.makeOutput(i.shape,t||i.dtype),l=o.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||n(c,Et[i.dtype],l),u}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var jae=rn(Fc);function pn(e,t,n){let r;function s(o){r=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:c}=o,{a:u,b:l}=c,p=i.dataIdMap.get(u.dataId).id,d=i.dataIdMap.get(l.dataId).id,h=n!=null?n:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,l.shape),m=i.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),b=new Uint8Array(new Int32Array(l.shape).buffer),y=i.dataIdMap.get(m.dataId).id;return(()=>r(p,g,u.shape.length,d,b,l.shape.length,Et[u.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:a}}var Kae=!0,Xae=pn(ba,Kae),UE;function Yae(e){UE=e.wasm.cwrap(bo,null,["array","number","number","number"])}function Zae(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(r.shape)===0)return r;let s=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(s).buffer),o=n.dataIdMap.get(r.dataId).id;return UE(a,s.length,Et[r.dtype],o),r}var Jae={kernelName:bo,backendName:"wasm",setupFunc:Yae,kernelFunc:Zae};function eg(e){let{inputs:{x:t},backend:n}=e;if(t.dtype==="string")return Cn(n.readSync(t.dataId),t.shape,t.dtype);let r=n.makeOutput(t.shape,t.dtype),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(s),r}var Qae={kernelName:Oo,backendName:"wasm",kernelFunc:eg},GE;function eoe(e){GE=e.wasm.cwrap(_s,null,["number","array","number","number","number","array","number"])}function fa(e){let{inputs:t,backend:n,attrs:r}=e,[s,a]=noe(t.x.shape,r.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=toe(t.x.shape,r.perm),c={dataId:t.x.dataId,shape:s,dtype:t.x.dtype};if(o){let f=eg({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,c.dtype),l=n.dataIdMap.get(c.dataId).id,p=n.dataIdMap.get(u.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(c.shape).buffer);return GE(l,h,c.shape.length,Et[c.dtype],p,d,a.length),u}function toe(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function noe(e,t){let n=[],r=[];for(let s=0;s<e.length;++s)e[s]!==1&&n.push(e[s]),e[t[s]]!==1&&r.push(t[s]);for(let s=0;s<r.length;++s){let a=-1;for(let o=0;o<r.length;++o)r[o]>=s&&(a===-1||r[a]>r[o])&&(a=o);r[a]=s}return[n,r]}var roe={kernelName:_s,backendName:"wasm",kernelFunc:fa,setupFunc:eoe};function Aa(e,t,n){let r=e.shape,s=e.shape.length,a=w.parseAxisParam(t,r),o=a,i=N.getAxesPermutation(o,s),c=null,u=!1;if(i!=null){let l=new Array(s);for(let h=0;h<l.length;h++)l[h]=r[i[h]];o=N.getInnerMostAxes(o.length,s),c=fa({inputs:{x:e},attrs:{perm:i},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(c.dataId).id!==p&&(u=!0)}return{transposed:c,originalAxes:a,axes:o,inputWasTransposed:u}}var HE;function soe(e){HE=e.wasm.cwrap(Oc,null,["number, number, number"])}function aoe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,c=t.dataIdMap.get(o.dataId).id,u=o,{transposed:l,axes:p,originalAxes:d,inputWasTransposed:h}=Aa(o,s,t);if(h){let v=t.dataIdMap.get(l.dataId).id;u=l,c=v}let f=u.shape.length;N.assertAxesAreInnerMostDims("all",p,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;HE(c,b,v)}if(h&&t.disposeData(l.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var ooe={kernelName:Oc,backendName:"wasm",setupFunc:soe,kernelFunc:aoe},qE;function ioe(e){qE=e.wasm.cwrap(Mc,null,["number, number, number"])}function coe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s,keepDims:a}=r,{x:o}=n,c=t.dataIdMap.get(o.dataId).id,u=o,{transposed:l,axes:p,originalAxes:d,inputWasTransposed:h}=Aa(o,s,t);if(h){let v=t.dataIdMap.get(l.dataId).id;u=l,c=v}let f=u.shape.length;N.assertAxesAreInnerMostDims("any",p,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;qE(c,b,v)}if(h&&t.disposeData(l.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var uoe={kernelName:Mc,backendName:"wasm",setupFunc:ioe,kernelFunc:coe},jE;function loe(e){jE=e.wasm.cwrap(yo,null,["number","number","number","number","number"])}function doe(e){let{backend:t,inputs:n,attrs:r}=e,{axis:s}=r,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,c=a,{transposed:u,axes:l,inputWasTransposed:p}=Aa(a,s,t);if(p){let b=t.dataIdMap.get(u.dataId).id;b!==o&&(c=u,i=b)}let d=c.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=c.shape[l[0]];return jE(i,Et[c.dtype],m,g,f),p&&t.disposeData(u.dataId),h}var poe={kernelName:yo,backendName:"wasm",kernelFunc:doe,setupFunc:loe},KE;function hoe(e){KE=e.wasm.cwrap(vo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function foe(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id,{filterSize:o,strides:i,pad:c,dimRoundingMode:u}=n,l=N.computePool2DInfo(s.shape,o,i,1,c,u),p=l.filterHeight,d=l.filterWidth,h=l.padInfo.top,f=l.padInfo.right,m=l.padInfo.bottom,g=l.padInfo.left,b=l.strideHeight,y=l.strideWidth,v=l.inChannels;if(l.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${l.dataFormat}'. 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t.dtype==="string"?p.stringBytes=c.slice(f,f+w.sizeFromShape(o)):s.typedArrayFromHeap(u).set(c.subarray(f,f+w.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Qh(c,a,o,t.shape,t.dtype);return p.stringBytes=f,u}let d=s.typedArrayFromHeap(u),h=t.shape.length;if(h===2)xoe(c,l[0],d,a,o);else if(h===3)woe(c,l[0],l[1],d,a,o);else if(h===4)Ioe(c,l[0],l[1],l[2],d,a,o);else{let f=Qh(c,a,o,t.shape,t.dtype);d.set(f)}return u}function xoe(e,t,n,r,s){let a=0,o=r[0],i=r[1],c=o+s[0];for(let u=o;u<c;u++){let l=u*t+i;n.set(e.subarray(l,l+s[1]),a),a+=s[1]}}function woe(e,t,n,r,s,a){let o=0,i=s[0],c=s[1],u=s[2],l=i+a[0],p=c+a[1];for(let d=i;d<l;d++)for(let h=c;h<p;h++){let f=d*t+h*n+u;r.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Ioe(e,t,n,r,s,a,o){let i=0,c=a[0],u=a[1],l=a[2],p=c+o[0],d=u+o[1],h=l+o[2],f=a[3];for(let m=c;m<p;m++)for(let g=u;g<d;g++)for(let b=l;b<h;b++){let y=m*t+g*n+b*r+f;s.set(e.subarray(y,y+o[3]),i),i+=o[3]}}var koe={kernelName:Su,backendName:"wasm",kernelFunc:mo};function Soe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockShape:a,crops:o}=r,i=a.reduce((b,y)=>b*y),c=N.getReshaped(s.shape,a,i),u=N.getPermuted(c.length,a.length),l=N.getReshapedPermuted(s.shape,a,i),p=N.getSliceBeginCoords(o,a.length),d=N.getSliceSize(l,o,a.length),h=Bn({inputs:{x:s},backend:n,attrs:{shape:c}}),f=fa({inputs:{x:h},backend:n,attrs:{perm:u}}),m=Bn({inputs:{x:f},backend:n,attrs:{shape:l}}),g=mo({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Toe={kernelName:Uc,backendName:"wasm",kernelFunc:Soe};function rl(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,s=r.makeOutput(t.shape,n),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(s).set(a),s}var Coe={kernelName:wo,backendName:"wasm",kernelFunc:rl},Noe=rn(Io),YE;function _oe(e){YE=e.wasm.cwrap(ya,null,["number","number","number","number"])}function Eoe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{clipValueMin:a,clipValueMax:o}=r,i=n.dataIdMap.get(s.dataId).id,c=n.makeOutput(s.shape,s.dtype),u=n.dataIdMap.get(c.dataId).id;return YE(i,a,o,u),c}var Aoe={kernelName:ya,backendName:"wasm",setupFunc:_oe,kernelFunc:Eoe};function ZE(e){let{inputs:t,backend:n}=e,r=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],s=t.map(h=>h.shape);N.assertParamsConsistent(s,r);let a=N.computeOutShape(t.map(h=>h.shape),r),o=t.filter(h=>w.sizeFromShape(h.shape)>0);if(o.length===1)return eg({inputs:{x:o[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(w.sizeFromShape(a)===0)return i;if(o[0].dtype==="string"){let h=o.map(v=>{let k=[-1,w.sizeFromShape(v.shape.slice(r))];return Bn({inputs:{x:v},backend:n,attrs:{shape:k}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));a=N.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=M0(f,a,t[0].dtype,m),b=N.computeOutShape(o.map(v=>v.shape),r);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let c=w.sizeFromShape(o[0].shape.slice(0,r)),u=0,l=o.map(h=>{let f=w.sizeFromShape(h.shape.slice(r));return u+=f,f}),p=o.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<c;h++){let f=h*u;for(let m=0;m<p.length;m++){let g=l[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var $oe={kernelName:Gc,backendName:"wasm",kernelFunc:ZE},JE;function Doe(e){JE=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Foe(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:c,dilations:u,pad:l,dimRoundingMode:p,dataFormat:d}=n,h=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(s.shape,a.shape,c,u,l,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,S=f.dilationWidth,C=f.strideHeight,E=f.strideWidth,$=f.inChannels,F=f.outChannels,A=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let R=r.makeOutput(f.outShape,"float32"),T=r.dataIdMap.get(R.dataId).id;return JE(o,s.shape[0],s.shape[1],s.shape[2],i,m,g,b,y,v,x,A,k,S,C,E,$,F,T),R}var Roe={kernelName:ko,backendName:"wasm",setupFunc:Doe,kernelFunc:Foe},QE;function Poe(e){QE=e.wasm.cwrap(So,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ooe(e){let{backend:t,inputs:n,attrs:r}=e,{dy:s,filter:a}=n,{strides:o,pad:i,dataFormat:c,dimRoundingMode:u,inputShape:l}=r,p=1,d=N.convertConv2DDataFormat(c),h=N.computeConv2DInfo(l,a.shape,o,p,i,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:S,strideHeight:C,strideWidth:E}=h,$=m-1-h.padInfo.top,F=g-1-h.padInfo.left,A=h.dataFormat==="channelsLast",R=w.computeStrides(h.inShape),T=w.computeStrides(s.shape),[L,V,G]=w.computeStrides(a.shape),j=R[0],H=A?R[1]:R[2],Z=A?R[2]:1,J=A?1:R[1],ee=T[0],re=A?T[1]:T[2],te=A?T[2]:1,ie=A?1:T[1],ne=t.makeOutput(h.inShape,"float32"),le=t.dataIdMap.get(ne.dataId).id,ue=t.dataIdMap.get(s.dataId).id,ye=t.dataIdMap.get(a.dataId).id;return QE(ue,ye,f,m,g,y,v,b,k,S,x,C,E,$,F,L,V,G,j,H,Z,J,ee,re,te,ie,le),ne}var Moe={kernelName:So,backendName:"wasm",setupFunc:Poe,kernelFunc:Ooe},Loe=rn(To),zoe=rn(Co),wv;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(wv||(wv={}));var eA;function Boe(e){eA=e.wasm.cwrap(qc,null,["number","number","number","number","array","number","number","number","number","number"])}function Woe(e){let{backend:t,inputs:n,attrs:r}=e,{method:s,extrapolationValue:a,cropSize:o}=r,{image:i,boxes:c,boxInd:u}=n,l=c.shape[0],[p,d]=o,h=[l,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=rl({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(u.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(i.shape).buffer);return eA(g,b,y,l,k,p,d,wv[s],a,x),m!=null&&t.disposeData(m.dataId),v}var Voe={kernelName:qc,backendName:"wasm",setupFunc:Boe,kernelFunc:Woe},tA;function Uoe(e){tA=e.wasm.cwrap(Hc,null,["number","number","number","number","number","number"])}function Goe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,c=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumprod does not support ${s.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([a],c),l=s;u!==null&&(l=fa({inputs:{x:s},attrs:{perm:u},backend:n}));let p=N.getInnerMostAxes(1,c)[0];N.assertAxesAreInnerMostDims("cumprod",[p],c);let d=n.makeOutput(l.shape,l.dtype),h=l.shape[p],f=n.dataIdMap.get(l.dataId).id,m=n.dataIdMap.get(d.dataId).id;tA(f,o?1:0,i?1:0,h,m,Et[s.dtype]);let g=d;if(u!==null){let b=N.getUndoAxesPermutation(u);g=fa({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(l.dataId),n.disposeData(d.dataId)}return g}var Hoe={kernelName:Hc,backendName:"wasm",setupFunc:Uoe,kernelFunc:Goe},nA;function qoe(e){nA=e.wasm.cwrap(No,null,["number","number","number","number","number","number"])}function joe(e){let{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:a,exclusive:o,reverse:i}=r,c=s.shape.length;w.assert(s.dtype==="float32"||s.dtype==="int32",()=>`cumsum does not support ${s.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([a],c),l=s;u!==null&&(l=fa({inputs:{x:s},attrs:{perm:u},backend:n}));let p=N.getInnerMostAxes(1,c)[0];N.assertAxesAreInnerMostDims("cumsum",[p],c);let d=n.makeOutput(l.shape,l.dtype),h=l.shape[p],f=n.dataIdMap.get(l.dataId).id,m=n.dataIdMap.get(d.dataId).id;nA(f,o?1:0,i?1:0,h,m,Et[s.dtype]);let g=d;if(u!==null){let b=N.getUndoAxesPermutation(u);g=fa({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(l.dataId),n.disposeData(d.dataId)}return g}var Koe={kernelName:No,backendName:"wasm",setupFunc:qoe,kernelFunc:joe},rA;function Xoe(e){rA=e.wasm.cwrap(jc,null,["number","number","number","array","number","array","array","number","number"])}function Yoe(e){let{backend:t,inputs:n,attrs:r}=e,{x:s}=n,{blockSize:a,dataFormat:o}=r,i=s.shape[0],c=o==="NHWC"?s.shape[1]:s.shape[2],u=o==="NHWC"?s.shape[2]:s.shape[3],l=o==="NHWC"?s.shape[3]:s.shape[1],p=c*a,d=u*a,h=l/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(s.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(s.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return rA(b,a,o==="NHWC"?1:0,y,s.shape.length-1,v,x,f.length,k),m}var Zoe={kernelName:jc,backendName:"wasm",setupFunc:Xoe,kernelFunc:Yoe},sA;function Joe(e){sA=e.wasm.cwrap(_o,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qoe(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a}=t,o=r.dataIdMap.get(s.dataId).id,i=r.dataIdMap.get(a.dataId).id,{strides:c,dilations:u,pad:l,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=N.computeConv2DInfo(s.shape,a.shape,c,d,l,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,S=h.strideHeight,C=h.strideWidth,E=h.inChannels,$=h.outChannels,F=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let A=r.makeOutput(h.outShape,"float32"),R=r.dataIdMap.get(A.dataId).id;return sA(o,s.shape[0],s.shape[1],s.shape[2],i,f,m,g,b,y,v,F,x,k,S,C,E,$,R),A}var eie={kernelName:_o,backendName:"wasm",setupFunc:Joe,kernelFunc:Qoe},tie=rn(Ao),nie=!1,rie=pn(Xc,nie,"bool"),sie=rn($o,"float32");function Iv(e){let{inputs:t,attrs:n,backend:r}=e,{input:s}=t,{dim:a}=n,o=s.shape.length,i=s.shape.slice(),c=a;return a<0&&(w.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),c=o+a+1),i.splice(c,0,1),Bn({inputs:{x:s},backend:r,attrs:{shape:i}})}var aie={kernelName:Yc,backendName:"wasm",kernelFunc:Iv};function aA(e){let{attrs:{shape:t,value:n,dtype:r},backend:s}=e,a=s.makeOutput(t,r);return s.typedArrayFromHeap(a).fill(n),a}var oie={kernelName:gd,backendName:"wasm",kernelFunc:aA},oA;function iie(e){oA=e.wasm.cwrap(Jc,null,["number","number","number","number","number","number"])}function cie(e){let{inputs:t,backend:n}=e,{image:r}=t,s=n.makeOutput(r.shape,r.dtype),a=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(s.dataId).id,[i,c,u,l]=r.shape;return oA(a,i,c,u,l,o),s}var uie={kernelName:Jc,backendName:"wasm",kernelFunc:cie,setupFunc:iie},lie=rn(Do),die=!1,pie=pn(Fo,die),iA;function hie(e){iA=e.wasm.cwrap(Ro,null,["number","number","number","number","number","number","number"])}function fie(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:s}=r,{x:a,mean:o,variance:i,offset:c,scale:u}=n,l=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(i.dataId).id,h=c!=null?t.dataIdMap.get(c.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return iA(l,p,d,h,f,s,g),m}var mie={kernelName:Ro,backendName:"wasm",setupFunc:hie,kernelFunc:fie},cA;function gie(e){cA=e.wasm.cwrap(to,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bie(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:u,dilations:l,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(s.shape,a.shape,c,l,u,d),g=ud[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=r.dataIdMap.get(s.dataId).id,y=r.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=r.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,S=m.filterWidth,C=m.padInfo.top,E=m.padInfo.right,$=m.padInfo.bottom,F=m.padInfo.left,A=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,V=m.inChannels,G=m.padInfo.type==="SAME"?1:0,j=m.batchSize,H=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let J=r.makeOutput(m.outShape,"float32"),ee=r.dataIdMap.get(J.dataId).id,re=i==null?0:r.dataIdMap.get(i.dataId).id;return cA(b,j,H,Z,y,k,S,x,C,E,$,F,G,A,R,T,L,V,v,g,re,f||0,ee),J}var yie={kernelName:to,backendName:"wasm",setupFunc:gie,kernelFunc:bie},uA;function vie(e){uA=e.wasm.cwrap(no,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xie(e){let{inputs:t,attrs:n,backend:r}=e,{x:s,filter:a,bias:o,preluActivationWeights:i}=t,{strides:c,pad:u,dilations:l,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(s.shape,a.shape,c,l,u,d,!0),g=ud[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=r.dataIdMap.get(s.dataId).id,y=r.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(o!=null){let te=r.dataIdMap.get(o.dataId);if(te.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${te.shape.length}.`);if(te.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${te.shape}) does not match the number of output channels (${v})`);x=te.id}let k=m.filterHeight,S=m.filterWidth,C=m.padInfo.top,E=m.padInfo.right,$=m.padInfo.bottom,F=m.padInfo.left,A=m.dilationHeight,R=m.dilationWidth,T=m.strideHeight,L=m.strideWidth,V=m.inChannels,G=m.padInfo.type==="SAME"?1:0,j=m.batchSize,H=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let J=r.makeOutput(m.outShape,"float32"),ee=r.dataIdMap.get(J.dataId).id,re=i==null?0:r.dataIdMap.get(i.dataId).id;return uA(b,j,H,Z,y,k,S,x,C,E,$,F,G,A,R,T,L,V,v,g,re,f||0,ee),J}var wie={kernelName:no,backendName:"wasm",setupFunc:vie,kernelFunc:xie},lA;function Iie(e){lA=e.wasm.cwrap(eu,null,["number","number","number","number","number","number","array","number"])}function kie(e){let{backend:t,inputs:n}=e,{params:r,indices:s}=n,[a,o,i,c]=Uv.prepareAndValidate(r,s),u=t.makeOutput(a,r.dtype);if(o===0)return u;let l=s.shape,p=l[l.length-1],h=t.dataIdMap.get(r.dataId).id,m=t.dataIdMap.get(s.dataId).id,g=new Uint8Array(new Int32Array(c).buffer),b=t.dataIdMap.get(u.dataId).id;return lA(h,Et[r.dtype],m,o,p,i,g,b),u}var Sie={kernelName:eu,backendName:"wasm",setupFunc:Iie,kernelFunc:kie},dA;function Tie(e){dA=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Cie(e){let{backend:t,inputs:n,attrs:r}=e,{x:s,indices:a}=n,{axis:o,batchDims:i}=r,c=w.parseAxisParam(o,s.shape)[0],u=t.readSync(a.dataId),l=s.shape[c];for(let $=0;$<u.length;++$){let F=u[$];w.assert(F<=l-1&&F>=0,()=>`GatherV2: the index value ${F} is not in [0, ${l-1}]`)}let p=N.segment_util.collectGatherOpShapeInfo(s,a,c,i),d=Bn({inputs:{x:s},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=Bn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,s.dtype);if(w.sizeFromShape(s.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,S=t.dataIdMap.get(g.dataId).id,C=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return dA(v,Et[s.dtype],C,b,k,p.batchSize,E,S),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Nie={kernelName:Qc,backendName:"wasm",setupFunc:Tie,kernelFunc:Cie},_ie=!1,Eie=pn(tu,_ie,"bool"),Aie=!1,$ie=pn(Po,Aie,"bool"),pA;function Die(e){pA=e.wasm.cwrap(Mo,null,["number","number","number","number"])}function Fie(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,s=r.dataIdMap.get(t.dataId).id,a=r.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let o=r.dataIdMap.get(a.dataId).id;pA(s,Et[t.dtype],n,o)}return a}var Rie={kernelName:Mo,backendName:"wasm",setupFunc:Die,kernelFunc:Fie},Pie=!1,Oie=pn(au,Pie,"bool"),Mie=!1,Lie=pn(ou,Mie,"bool"),zie=rn(Lo),Bie=!1,Wie=pn(cu,Bie,"bool"),Vie=rn(uu),Uie=!1,Gie=pn(lu,Uie,"bool"),Hie=!1,qie=pn(sS,Hie,"bool"),hA;function jie(e){hA=e.wasm.cwrap(zo,null,["number","number","number","number"])}function Kie(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:s,keepDims:a}=r,{x:o}=n,c=t.dataIdMap.get(o.dataId).id,u=o,{transposed:l,axes:p,originalAxes:d,inputWasTransposed:h}=Aa(o,s,t);if(h){let v=t.dataIdMap.get(l.dataId).id;u=l,c=v}let f=u.shape.length;N.assertAxesAreInnerMostDims("max",p,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,o.dtype);if(w.sizeFromShape(u.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;hA(c,Et[o.dtype],b,v)}if(h&&t.disposeData(l.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Xie={kernelName:zo,backendName:"wasm",setupFunc:jie,kernelFunc:Kie},Yie=!1,Zie=pn(Bo,Yie),fA;function Jie(e){fA=e.wasm.cwrap(Wo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qie(e){let{inputs:t,attrs:n,backend:r}=e,s=t.x,a=r.dataIdMap.get(s.dataId).id;w.assert(s.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${s.dtype}.`);let{filterSize:o,strides:i,pad:c,dimRoundingMode:u}=n,l=N.computePool2DInfo(s.shape,o,i,1,c,u),p=l.filterHeight,d=l.filterWidth,h=l.padInfo.top,f=l.padInfo.right,m=l.padInfo.bottom,g=l.padInfo.left,b=l.dilationHeight,y=l.dilationWidth,v=l.strideHeight,x=l.strideWidth,k=l.inChannels,S=l.outChannels;if(l.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${l.dataFormat}'. 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Fe(this.left,this.top)}get topRight(){return new Fe(this.right,this.top)}get bottomLeft(){return new Fe(this.left,this.bottom)}get bottomRight(){return new Fe(this.right,this.bottom)}round(){let[t,n,r,s]=[this.x,this.y,this.width,this.height].map(a=>Math.round(a));return new ot({x:t,y:n,width:r,height:s})}floor(){let[t,n,r,s]=[this.x,this.y,this.width,this.height].map(a=>Math.floor(a));return new ot({x:t,y:n,width:r,height:s})}toSquare(){let{x:t,y:n,width:r,height:s}=this,a=Math.abs(r-s);return r<s&&(t-=a/2,r+=a),s<r&&(n-=a/2,s+=a),new ot({x:t,y:n,width:r,height:s})}rescale(t){let n=ng(t)?t.width:t,r=ng(t)?t.height:t;return new ot({x:this.x*n,y:this.y*r,width:this.width*n,height:this.height*r})}pad(t,n){let[r,s,a,o]=[this.x-t/2,this.y-n/2,this.width+t,this.height+n];return new ot({x:r,y:s,width:a,height:o})}clipAtImageBorders(t,n){let{x:r,y:s,right:a,bottom:o}=this,i=Math.max(r,0),c=Math.max(s,0),u=a-i,l=o-c,p=Math.min(u,t-i),d=Math.min(l,n-c);return new ot({x:i,y:c,width:p,height:d}).floor()}shift(t,n){let{width:r,height:s}=this,a=this.x+t,o=this.y+n;return new ot({x:a,y:o,width:r,height:s})}padAtBorders(t,n){let r=this.width+1,s=this.height+1,a=1,o=1,i=r,c=s,u=this.left,l=this.top,p=this.right,d=this.bottom;return p>n&&(i=-p+n+r,p=n),d>t&&(c=-d+t+s,d=t),u<1&&(c=2-u,u=1),l<1&&(c=2-l,l=1),{dy:o,edy:c,dx:a,edx:i,y:l,ey:d,x:u,ex:p,w:r,h:s}}calibrate(t){return new ot({left:this.left+t.left*this.width,top:this.top+t.top*this.height,right:this.right+t.right*this.width,bottom:this.bottom+t.bottom*this.height}).toSquare().round()}};var _i=class extends ot{constructor(t,n,r,s,a=!1){super({left:t,top:n,right:r,bottom:s},a)}};var Ls=class{constructor(t,n,r,s,a){this._imageDims=new yn(a.width,a.height),this._score=t,this._classScore=n,this._className=r,this._box=new ot(s).rescale(this._imageDims)}get score(){return this._score}get classScore(){return this._classScore}get className(){return this._className}get box(){return this._box}get imageDims(){return this._imageDims}get imageWidth(){return this.imageDims.width}get imageHeight(){return this.imageDims.height}get relativeBox(){return new ot(this._box).rescale(this.imageDims.reverse())}forSize(t,n){return new Ls(this.score,this.classScore,this.className,this.relativeBox,{width:t,height:n})}};var gt=class extends Ls{constructor(t,n,r){super(t,t,"",n,r)}forSize(t,n){let{score:r,relativeBox:s,imageDims:a}=super.forSize(t,n);return new gt(r,s,a)}};function mI(e,t,n=!0){let r=Math.max(0,Math.min(e.right,t.right)-Math.max(e.left,t.left)),s=Math.max(0,Math.min(e.bottom,t.bottom)-Math.max(e.top,t.top)),a=r*s;return n?a/(e.area+t.area-a):a/Math.min(e.area,t.area)}function gI(e){let t=e.map(i=>i.x),n=e.map(i=>i.y),r=t.reduce((i,c)=>c<i?c:i,1/0),s=n.reduce((i,c)=>c<i?c:i,1/0),a=t.reduce((i,c)=>i<c?c:i,0),o=n.reduce((i,c)=>i<c?c:i,0);return new _i(r,s,a,o)}function bI(e,t,n,r=!0){let s=t.map((o,i)=>({score:o,boxIndex:i})).sort((o,i)=>o.score-i.score).map(o=>o.boxIndex),a=[];for(;s.length>0;){let o=s.pop();a.push(o);let i=s,c=[];for(let u=0;u<i.length;u++){let l=i[u],p=e[o],d=e[l];c.push(mI(p,d,r))}s=s.filter((u,l)=>c[l]<=n)}return a}function Jr(e,t){return O(()=>{let[n,r,s]=t,a=bn([...e.shape.slice(0,3),1],n,"float32"),o=bn([...e.shape.slice(0,3),1],r,"float32"),i=bn([...e.shape.slice(0,3),1],s,"float32"),c=Je([a,o,i],3);return de(e,c)})}function yI(e,t=!1){return O(()=>{let[n,r]=e.shape.slice(1);if(n===r)return e;let s=Math.abs(n-r),a=Math.round(s*(t?.5:1)),o=n>r?2:1,i=d=>{let h=e.shape.slice();return h[o]=d,bn(h,0,"float32")},c=i(a),u=s-c.shape[o],p=[t&&u?i(u):null,e,c].filter(d=>!!d).map(d=>ce(d,"float32"));return Je(p,o)})}function Mle(e){let t=e.slice();for(let n=t.length-1;n>0;n--){let r=Math.floor(Math.random()*(n+1)),s=t[n];t[n]=t[r],t[r]=s}return t}function pp(e){return 1/(1+Math.exp(-e))}function Lle(e){return Math.log(e/(1-e))}var Ei=class extends ot{constructor(t,n,r,s,a=!1){super({x:t,y:n,width:r,height:s},a)}};var zle=.5,Ble=.43,Wle=.45,ar=class{constructor(t,n,r=new Fe(0,0)){let{width:s,height:a}=n;this._imgDims=new yn(s,a),this._shift=r,this._positions=t.map(o=>o.mul(new Fe(s,a)).add(r))}get shift(){return new Fe(this._shift.x,this._shift.y)}get imageWidth(){return this._imgDims.width}get imageHeight(){return this._imgDims.height}get positions(){return this._positions}get relativePositions(){return this._positions.map(t=>t.sub(this._shift).div(new Fe(this.imageWidth,this.imageHeight)))}forSize(t,n){return new this.constructor(this.relativePositions,{width:t,height:n})}shiftBy(t,n){return new this.constructor(this.relativePositions,this._imgDims,new Fe(t,n))}shiftByPoint(t){return this.shiftBy(t.x,t.y)}align(t,n={}){if(t){let a=t instanceof gt?t.box.floor():new ot(t);return this.shiftBy(a.x,a.y).align(null,n)}let{useDlibAlignment:r,minBoxPadding:s}={useDlibAlignment:!1,minBoxPadding:.2,...n};return r?this.alignDlib():this.alignMinBbox(s)}alignDlib(){let t=this.getRefPointsForAlignment(),[n,r,s]=t,a=p=>s.sub(p).magnitude(),o=(a(n)+a(r))/2,i=Math.floor(o/Wle),c=Ni(t),u=Math.floor(Math.max(0,c.x-zle*i)),l=Math.floor(Math.max(0,c.y-Ble*i));return new Ei(u,l,Math.min(i,this.imageWidth+u),Math.min(i,this.imageHeight+l))}alignMinBbox(t){let n=gI(this.positions);return n.pad(n.width*t,n.height*t)}getRefPointsForAlignment(){throw new Error("getRefPointsForAlignment not implemented by base class")}};var vI=class extends ar{getRefPointsForAlignment(){let t=this.positions;return[t[0],t[1],Ni([t[3],t[4]])]}};var Ai=class extends ar{getJawOutline(){return this.positions.slice(0,17)}getLeftEyeBrow(){return this.positions.slice(17,22)}getRightEyeBrow(){return this.positions.slice(22,27)}getNose(){return this.positions.slice(27,36)}getLeftEye(){return this.positions.slice(36,42)}getRightEye(){return this.positions.slice(42,48)}getMouth(){return this.positions.slice(48,68)}getRefPointsForAlignment(){return[this.getLeftEye(),this.getRightEye(),this.getMouth()].map(Ni)}};var al=class{constructor(t,n){this._label=t,this._distance=n}get label(){return this._label}get distance(){return this._distance}toString(t=!0){return`${this.label}${t?` (${Ci(this.distance)})`:""}`}};var ol=class extends ot{constructor(n,r){super(n);this._label=r}static assertIsValidLabeledBox(n,r){if(ot.assertIsValidBox(n,r),!Zr(n.label))throw new Error(`${r} - expected property label (${n.label}) to be a number`)}get label(){return this._label}};var bs=class{constructor(t,n){if(typeof t!="string")throw new Error("LabeledFaceDescriptors - constructor expected label to be a string");if(!Array.isArray(n)||n.some(r=>!(r instanceof Float32Array)))throw new Error("LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array");this._label=t,this._descriptors=n}get label(){return this._label}get descriptors(){return this._descriptors}toJSON(){return{label:this.label,descriptors:this.descriptors.map(t=>Array.from(t))}}static fromJSON(t){let n=t.descriptors.map(r=>new Float32Array(r));return new bs(t.label,n)}};var xI=class extends ol{constructor(n,r,s,a){super(n,r);this._score=s,this._classScore=a}static assertIsValidPredictedBox(n,r){if(ol.assertIsValidLabeledBox(n,r),!sl(n.score)||!sl(n.classScore))throw new Error(`${r} - expected properties score (${n.score}) and (${n.classScore}) to be a number between [0, 1]`)}get score(){return this._score}get classScore(){return this._classScore}};function ys(e){return e.detection instanceof gt}function $i(e,t){return{...e,...{detection:t}}}function wI(){let e=window.fetch;if(!e)throw new Error("fetch - missing fetch implementation for browser 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kI()?SI(wI()):hp()?SI(II()):null}function Ule(e){if(sn||TI(),!sn)throw new Error("monkeyPatch - environment is not defined, check isNodejs() and isBrowser()");let{Canvas:t=sn.Canvas,Image:n=sn.Image}=e;sn.Canvas=t,sn.Image=n,sn.createCanvasElement=e.createCanvasElement||(()=>new t),sn.createImageElement=e.createImageElement||(()=>new n),sn.ImageData=e.ImageData||sn.ImageData,sn.Video=e.Video||sn.Video,sn.fetch=e.fetch||sn.fetch,sn.readFile=e.readFile||sn.readFile}var Qe={getEnv:Vle,setEnv:SI,initialize:TI,createBrowserEnv:wI,createFileSystem:rg,createNodejsEnv:II,monkeyPatch:Ule,isBrowser:kI,isNodejs:hp};TI();function Di(e){return!Qe.isNodejs()&&typeof e=="string"?document.getElementById(e):e}function Gn(e){let{Canvas:t,CanvasRenderingContext2D:n}=Qe.getEnv();if(e instanceof n)return e;let r=Di(e);if(!(r instanceof t))throw new Error("resolveContext2d - expected canvas to be of instance of Canvas");let s=r.getContext("2d");if(!s)throw new Error("resolveContext2d - canvas 2d context is 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this.text.length*t+2*n}getUpperLeft(t,n){let{anchorPosition:r}=this.options,s=r==="BOTTOM_RIGHT"||r==="TOP_RIGHT",a=r==="BOTTOM_LEFT"||r==="BOTTOM_RIGHT",o=this.measureWidth(t),i=this.measureHeight(),c=s?this.anchor.x-o:this.anchor.x,u=a?this.anchor.y-i:this.anchor.y;if(n){let{width:l,height:p}=n,d=Math.max(Math.min(c,l-o),0),h=Math.max(Math.min(u,p-i),0);return{x:d,y:h}}return{x:c,y:u}}draw(t){let n=Di(t),r=Gn(n),{backgroundColor:s,fontColor:a,fontSize:o,fontStyle:i,padding:c}=this.options;r.font=`${o}px ${i}`;let u=this.measureWidth(r),l=this.measureHeight();r.fillStyle=s;let p=this.getUpperLeft(r,n);r.fillRect(p.x,p.y,u,l),r.fillStyle=a,this.text.forEach((d,h)=>{let f=c+p.x,m=c+p.y+(h+1)*o;r.fillText(d,f,m)})}};var sg=class{constructor(t={}){let{boxColor:n,lineWidth:r,label:s,drawLabelOptions:a}=t;this.boxColor=n||"rgba(0, 0, 255, 1)",this.lineWidth=r||2,this.label=s;let o={anchorPosition:"BOTTOM_LEFT",backgroundColor:this.boxColor};this.drawLabelOptions=new il({...o,...a})}},fp=class{constructor(t,n={}){this.box=new ot(t),this.options=new sg(n)}draw(t){let n=Gn(t),{boxColor:r,lineWidth:s}=this.options,{x:a,y:o,width:i,height:c}=this.box;n.strokeStyle=r,n.lineWidth=s,n.strokeRect(a,o,i,c);let{label:u}=this.options;u&&new zs([u],{x:a-s/2,y:o},this.options.drawLabelOptions).draw(t)}};function Gle(e,t){(Array.isArray(t)?t:[t]).forEach(r=>{let s=r instanceof gt?r.score:ys(r)?r.detection.score:void 0,a=r instanceof gt?r.box:ys(r)?r.detection.box:new ot(r),o=s?`${Ci(s)}`:void 0;new fp(a,{label:o}).draw(e)})}function mp(e){let{Image:t,Video:n}=Qe.getEnv();return e instanceof t&&e.complete||e instanceof n&&e.readyState>=3}function NI(e){return new Promise((t,n)=>{(e instanceof Qe.getEnv().Canvas||mp(e))&&t(null);function r(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),n(a))}function s(a){!a.currentTarget||(a.currentTarget.removeEventListener("load",s),a.currentTarget.removeEventListener("error",r),t(a))}e.addEventListener("load",s),e.addEventListener("error",r)})}function _I(e){return new Promise((t,n)=>{e instanceof Blob||n(new Error("bufferToImage - expected buf to be of type: Blob"));let r=new FileReader;r.onload=()=>{typeof r.result!="string"&&n(new Error("bufferToImage - expected reader.result to be a string, in onload"));let s=Qe.getEnv().createImageElement();s.onload=()=>t(s),s.onerror=n,s.src=r.result},r.onerror=n,r.readAsDataURL(e)})}function Fi(e){let{Image:t,Video:n}=Qe.getEnv();return e instanceof t?new yn(e.naturalWidth,e.naturalHeight):e instanceof n?new yn(e.videoWidth,e.videoHeight):new yn(e.width,e.height)}function Ri({width:e,height:t}){let{createCanvasElement:n}=Qe.getEnv(),r=n();return r.width=e,r.height=t,r}function gp(e,t){let{ImageData:n}=Qe.getEnv();if(!(e instanceof n)&&!mp(e))throw new Error("createCanvasFromMedia - media has not finished 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vs=class{constructor(t,n=!1){this._imageTensors=[];this._canvases=[];this._treatAsBatchInput=!1;this._inputDimensions=[];this._inputSize=0;if(!Array.isArray(t))throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${t}`);this._treatAsBatchInput=n,this._batchSize=t.length,t.forEach((r,s)=>{if(Ms(r)){this._imageTensors[s]=r,this._inputDimensions[s]=r.shape;return}if(yr(r)){let o=r.shape[0];if(o!==1)throw new Error(`NetInput - tf.Tensor4D with batchSize ${o} passed, but not supported in input array`);this._imageTensors[s]=r,this._inputDimensions[s]=r.shape.slice(1);return}let a=r instanceof Qe.getEnv().Canvas?r:gp(r);this._canvases[s]=a,this._inputDimensions[s]=[a.height,a.width,3]})}get imageTensors(){return this._imageTensors}get canvases(){return this._canvases}get isBatchInput(){return this.batchSize>1||this._treatAsBatchInput}get batchSize(){return this._batchSize}get inputDimensions(){return 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an{constructor(n,r){super(n);this._faceFeatureExtractor=r}get faceFeatureExtractor(){return this._faceFeatureExtractor}runNet(n){let{params:r}=this;if(!r)throw new Error(`${this._name} - load model before inference`);return O(()=>{let s=n instanceof vs?this.faceFeatureExtractor.forwardInput(n):n;return vp(s.as2D(s.shape[0],-1),r.fc)})}dispose(n=!0){this.faceFeatureExtractor.dispose(n),super.dispose(n)}loadClassifierParams(n){let{params:r,paramMappings:s}=this.extractClassifierParams(n);this._params=r,this._paramMappings=s}extractClassifierParams(n){return YA(n,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())}extractParamsFromWeightMap(n){let{featureExtractorMap:r,classifierMap:s}=pg(n);return this.faceFeatureExtractor.loadFromWeightMap(r),ZA(s)}extractParams(n){let r=this.getClassifierChannelsIn(),s=this.getClassifierChannelsOut(),a=s*r+s,o=n.slice(0,n.length-a),i=n.slice(n.length-a);return this.faceFeatureExtractor.extractWeights(o),this.extractClassifierParams(i)}};var FI=["neutral","happy","sad","angry","fearful","disgusted","surprised"],Ws=class{constructor(t){this.neutral=0;this.happy=0;this.sad=0;this.angry=0;this.fearful=0;this.disgusted=0;this.surprised=0;if(t.length!==7)throw new Error(`FaceExpressions.constructor - expected probabilities.length to be 7, have: ${t.length}`);FI.forEach((n,r)=>{this[n]=t[r]})}asSortedArray(){return FI.map(t=>({expression:t,probability:this[t]})).sort((t,n)=>n.probability-t.probability)}};var xp=class extends fl{constructor(t=new hl){super("FaceExpressionNet",t)}forwardInput(t){return O(()=>Xr(this.runNet(t)))}async forward(t){return this.forwardInput(await bt(t))}async predictExpressions(t){let n=await bt(t),r=await this.forwardInput(n),s=await Promise.all(lt(r).map(async o=>{let i=o.dataSync();return o.dispose(),i}));r.dispose();let a=s.map(o=>new Ws(o));return n.isBatchInput?a:a[0]}getDefaultModelName(){return"face_expression_model"}getClassifierChannelsIn(){return 256}getClassifierChannelsOut(){return 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p=n(c,`${l}/depthwise_conv`),d=s(c,u,1,`${l}/pointwise_conv`);return{depthwise_conv:p,pointwise_conv:d}}function o(){let c=s(3,32,3,"mobilenetv1/conv_0"),u=a(32,64,"mobilenetv1/conv_1"),l=a(64,128,"mobilenetv1/conv_2"),p=a(128,128,"mobilenetv1/conv_3"),d=a(128,256,"mobilenetv1/conv_4"),h=a(256,256,"mobilenetv1/conv_5"),f=a(256,512,"mobilenetv1/conv_6"),m=a(512,512,"mobilenetv1/conv_7"),g=a(512,512,"mobilenetv1/conv_8"),b=a(512,512,"mobilenetv1/conv_9"),y=a(512,512,"mobilenetv1/conv_10"),v=a(512,512,"mobilenetv1/conv_11"),x=a(512,1024,"mobilenetv1/conv_12"),k=a(1024,1024,"mobilenetv1/conv_13");return{conv_0:c,conv_1:u,conv_2:l,conv_3:p,conv_4:d,conv_5:h,conv_6:f,conv_7:m,conv_8:g,conv_9:b,conv_10:y,conv_11:v,conv_12:x,conv_13:k}}function i(){let 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t=[],{extractMobilenetV1Params:n,extractPredictionLayerParams:r}=ude(e,t),s=e["Output/extra_dim"];if(t.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Ms(s))throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${s}`);let a={mobilenetv1:n(),prediction_layer:r(),output_layer:{extra_dim:s}};return Fn(e,t),{params:a,paramMappings:t}}function Mr(e,t,n){return O(()=>{let r=Dt(e,t.filters,n,"same");return r=Y(r,t.batch_norm_offset),en(r,0,6)})}var lde=.0010000000474974513;function dde(e,t,n){return O(()=>{let r=Sa(e,t.filters,n,"same");return r=ka(r,t.batch_norm_mean,t.batch_norm_variance,t.batch_norm_offset,t.batch_norm_scale,lde),en(r,0,6)})}function pde(e){return[2,4,6,12].some(t=>t===e)?[2,2]:[1,1]}function p$(e,t){return O(()=>{let n,r=Mr(e,t.conv_0,[2,2]);if([t.conv_1,t.conv_2,t.conv_3,t.conv_4,t.conv_5,t.conv_6,t.conv_7,t.conv_8,t.conv_9,t.conv_10,t.conv_11,t.conv_12,t.conv_13].forEach((a,o)=>{let i=o+1,c=pde(i);r=dde(r,a.depthwise_conv,c),r=Mr(r,a.pointwise_conv,[1,1]),i===11&&(n=r)}),n===null)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:r,conv11:n}})}function hde(e,t,n){let r=e.arraySync(),s=Math.min(r[t][0],r[t][2]),a=Math.min(r[t][1],r[t][3]),o=Math.max(r[t][0],r[t][2]),i=Math.max(r[t][1],r[t][3]),c=Math.min(r[n][0],r[n][2]),u=Math.min(r[n][1],r[n][3]),l=Math.max(r[n][0],r[n][2]),p=Math.max(r[n][1],r[n][3]),d=(o-s)*(i-a),h=(l-c)*(p-u);if(d<=0||h<=0)return 0;let f=Math.max(s,c),m=Math.max(a,u),g=Math.min(o,l),b=Math.min(i,p),y=Math.max(g-f,0)*Math.max(b-m,0);return y/(d+h-y)}function h$(e,t,n,r,s){let a=e.shape[0],o=Math.min(n,a),i=t.map((l,p)=>({score:l,boxIndex:p})).filter(l=>l.score>s).sort((l,p)=>p.score-l.score),c=l=>l<=r?1:0,u=[];return i.forEach(l=>{if(u.length>=o)return;let p=l.score;for(let d=u.length-1;d>=0;--d){let h=hde(e,l.boxIndex,u[d]);if(h!==0&&(l.score*=c(h),l.score<=s))break}p===l.score&&u.push(l.boxIndex)}),u}function fde(e){let t=lt(Ee(e,[1,0])),n=[de(t[2],t[0]),de(t[3],t[1])],r=[Y(t[0],fe(n[0],2)),Y(t[1],fe(n[1],2))];return{sizes:n,centers:r}}function mde(e,t){let{sizes:n,centers:r}=fde(e),s=lt(Ee(t,[1,0])),a=fe(B(gn(fe(s[2],5)),n[0]),2),o=Y(B(fe(s[0],10),n[0]),r[0]),i=fe(B(gn(fe(s[3],5)),n[1]),2),c=Y(B(fe(s[1],10),n[1]),r[1]);return Ee(Ft([de(o,a),de(c,i),Y(o,a),Y(c,i)]),[1,0])}function f$(e,t,n){return O(()=>{let r=e.shape[0],s=mde(W(Ln(n.extra_dim,[r,1,1]),[-1,4]),W(e,[-1,4]));s=W(s,[r,s.shape[0]/r,4]);let a=dr(We(t,[0,0,1],[-1,-1,-1])),o=We(a,[0,0,0],[-1,-1,1]);o=W(o,[r,o.shape[1]]);let i=lt(s),c=lt(o);return{boxes:i,scores:c}})}function zi(e,t){return O(()=>{let n=e.shape[0],r=W(Pi(e,t.box_encoding_predictor),[n,-1,1,4]),s=W(Pi(e,t.class_predictor),[n,-1,3]);return{boxPredictionEncoding:r,classPrediction:s}})}function m$(e,t,n){return O(()=>{let 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l=Array.from(u.dataSync()),d=h$(c,l,r,.5,s),h=a.getReshapedInputDimensions(0),f=a.inputSize,m=f/h.width,g=f/h.height,b=c.arraySync(),y=d.map(v=>{let[x,k]=[Math.max(0,b[v][0]),Math.min(1,b[v][2])].map(E=>E*g),[S,C]=[Math.max(0,b[v][1]),Math.min(1,b[v][3])].map(E=>E*m);return new gt(l[v],new Ei(S,x,C-S,k-x),{height:a.getInputHeight(0),width:a.getInputWidth(0)})});return c.dispose(),u.dispose(),y}getDefaultModelName(){return"ssd_mobilenetv1_model"}extractParamsFromWeightMap(t){return d$(t)}extractParams(t){return l$(t)}};function g$(e){let t=new $a;return t.extractWeights(e),t}function gde(e){return g$(e)}var BI=class extends $a{};var b$=.4,y$=[new Fe(.738768,.874946),new Fe(2.42204,2.65704),new Fe(4.30971,7.04493),new Fe(10.246,4.59428),new Fe(12.6868,11.8741)],v$=[new Fe(1.603231,2.094468),new Fe(6.041143,7.080126),new Fe(2.882459,3.518061),new Fe(4.266906,5.178857),new Fe(9.041765,10.66308)],x$=[117.001,114.697,97.404],w$="tiny_yolov2_model",I$="tiny_yolov2_separable_conv_model";var kg=e=>typeof e=="number";function WI(e){if(!e)throw new Error(`invalid config: ${e}`);if(typeof e.withSeparableConvs!="boolean")throw new Error(`config.withSeparableConvs has to be a boolean, have: ${e.withSeparableConvs}`);if(!kg(e.iouThreshold)||e.iouThreshold<0||e.iouThreshold>1)throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${e.iouThreshold}`);if(!Array.isArray(e.classes)||!e.classes.length||!e.classes.every(t=>typeof t=="string"))throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(e.classes)}`);if(!Array.isArray(e.anchors)||!e.anchors.length||!e.anchors.map(t=>t||{}).every(t=>kg(t.x)&&kg(t.y)))throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(e.anchors)}`);if(e.meanRgb&&(!Array.isArray(e.meanRgb)||e.meanRgb.length!==3||!e.meanRgb.every(kg)))throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: 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k$(e,t,n,r){let{extractWeights:s,getRemainingWeights:a}=Rn(e),o=[],{extractConvParams:i,extractConvWithBatchNormParams:c,extractSeparableConvParams:u}=bde(s,o),l;if(t.withSeparableConvs){let[p,d,h,f,m,g,b,y,v]=r,x=t.isFirstLayerConv2d?i(p,d,3,"conv0"):u(p,d,"conv0"),k=u(d,h,"conv1"),S=u(h,f,"conv2"),C=u(f,m,"conv3"),E=u(m,g,"conv4"),$=u(g,b,"conv5"),F=y?u(b,y,"conv6"):void 0,A=v?u(y,v,"conv7"):void 0,R=i(v||y||b,5*n,1,"conv8");l={conv0:x,conv1:k,conv2:S,conv3:C,conv4:E,conv5:$,conv6:F,conv7:A,conv8:R}}else{let[p,d,h,f,m,g,b,y,v]=r,x=c(p,d,"conv0"),k=c(d,h,"conv1"),S=c(h,f,"conv2"),C=c(f,m,"conv3"),E=c(m,g,"conv4"),$=c(g,b,"conv5"),F=c(b,y,"conv6"),A=c(y,v,"conv7"),R=i(v,5*n,1,"conv8");l={conv0:x,conv1:k,conv2:S,conv3:C,conv4:E,conv5:$,conv6:F,conv7:A,conv8:R}}if(a().length!==0)throw new Error(`weights remaing after extract: ${a().length}`);return{params:l,paramMappings:o}}function yde(e,t){let n=or(e,t);function r(i){let c=n(`${i}/sub`,1),u=n(`${i}/truediv`,1);return{sub:c,truediv:u}}function s(i){let c=n(`${i}/filters`,4),u=n(`${i}/bias`,1);return{filters:c,bias:u}}function a(i){let c=s(`${i}/conv`),u=r(`${i}/bn`);return{conv:c,bn:u}}let o=pl(n);return{extractConvParams:s,extractConvWithBatchNormParams:a,extractSeparableConvParams:o}}function S$(e,t){let n=[],{extractConvParams:r,extractConvWithBatchNormParams:s,extractSeparableConvParams:a}=yde(e,n),o;if(t.withSeparableConvs){let i=t.filterSizes&&t.filterSizes.length||9;o={conv0:t.isFirstLayerConv2d?r("conv0"):a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:i>7?a("conv6"):void 0,conv7:i>8?a("conv7"):void 0,conv8:r("conv8")}}else o={conv0:s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:s("conv6"),conv7:s("conv7"),conv8:r("conv8")};return Fn(e,n),{params:o,paramMappings:n}}var es=class{constructor({inputSize:t,scoreThreshold:n}={}){this._name="TinyYolov2Options";if(this._inputSize=t||416,this._scoreThreshold=n||.5,typeof this._inputSize!="number"||this._inputSize%32!==0)throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`);if(typeof this._scoreThreshold!="number"||this._scoreThreshold<=0||this._scoreThreshold>=1)throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)}get inputSize(){return this._inputSize}get scoreThreshold(){return this._scoreThreshold}};var VI=class extends an{constructor(n){super("TinyYolov2");WI(n),this._config=n}get config(){return this._config}get withClassScores(){return this.config.withClassScores||this.config.classes.length>1}get boxEncodingSize(){return 5+(this.withClassScores?this.config.classes.length:0)}runTinyYolov2(n,r){let s=Vs(n,r.conv0);return s=Rt(s,[2,2],[2,2],"same"),s=Vs(s,r.conv1),s=Rt(s,[2,2],[2,2],"same"),s=Vs(s,r.conv2),s=Rt(s,[2,2],[2,2],"same"),s=Vs(s,r.conv3),s=Rt(s,[2,2],[2,2],"same"),s=Vs(s,r.conv4),s=Rt(s,[2,2],[2,2],"same"),s=Vs(s,r.conv5),s=Rt(s,[2,2],[1,1],"same"),s=Vs(s,r.conv6),s=Vs(s,r.conv7),Pi(s,r.conv8,"valid",!1)}runMobilenet(n,r){let s=this.config.isFirstLayerConv2d?bl(Pi(n,r.conv0,"valid",!1)):Us(n,r.conv0);return s=Rt(s,[2,2],[2,2],"same"),s=Us(s,r.conv1),s=Rt(s,[2,2],[2,2],"same"),s=Us(s,r.conv2),s=Rt(s,[2,2],[2,2],"same"),s=Us(s,r.conv3),s=Rt(s,[2,2],[2,2],"same"),s=Us(s,r.conv4),s=Rt(s,[2,2],[2,2],"same"),s=Us(s,r.conv5),s=Rt(s,[2,2],[1,1],"same"),s=r.conv6?Us(s,r.conv6):s,s=r.conv7?Us(s,r.conv7):s,Pi(s,r.conv8,"valid",!1)}forwardInput(n,r){let{params:s}=this;if(!s)throw new Error("TinyYolov2 - load model before inference");return O(()=>{let a=ce(n.toBatchTensor(r,!1),"float32");return a=this.config.meanRgb?Jr(a,this.config.meanRgb):a,a=a.div(255),this.config.withSeparableConvs?this.runMobilenet(a,s):this.runTinyYolov2(a,s)})}async forward(n,r){return this.forwardInput(await bt(n),r)}async detect(n,r={}){let{inputSize:s,scoreThreshold:a}=new es(r),o=await bt(n),i=await this.forwardInput(o,s),c=O(()=>lt(i)[0].expandDims()),u={width:o.getInputWidth(0),height:o.getInputHeight(0)},l=await this.extractBoxes(c,o.getReshapedInputDimensions(0),a);i.dispose(),c.dispose();let p=l.map(b=>b.box),d=l.map(b=>b.score),h=l.map(b=>b.classScore),f=l.map(b=>this.config.classes[b.label]);return bI(p.map(b=>b.rescale(s)),d,this.config.iouThreshold,!0).map(b=>new Ls(d[b],h[b],f[b],p[b],u))}getDefaultModelName(){return""}extractParamsFromWeightMap(n){return S$(n,this.config)}extractParams(n){let r=this.config.filterSizes||VI.DEFAULT_FILTER_SIZES,s=r?r.length:void 0;if(s!==7&&s!==8&&s!==9)throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${s} filterSizes in config`);return k$(n,this.config,this.boxEncodingSize,r)}async extractBoxes(n,r,s){let{width:a,height:o}=r,i=Math.max(a,o),c=i/a,u=i/o,l=n.shape[1],p=this.config.anchors.length,[d,h,f]=O(()=>{let y=n.reshape([l,l,p,this.boxEncodingSize]),v=y.slice([0,0,0,0],[l,l,p,4]),x=y.slice([0,0,0,4],[l,l,p,1]),k=this.withClassScores?Xr(y.slice([0,0,0,5],[l,l,p,this.config.classes.length]),3):xe(0);return[v,x,k]}),m=[],g=await h.array(),b=await d.array();for(let y=0;y<l;y++)for(let v=0;v<l;v++)for(let x=0;x<p;x++){let k=pp(g[y][v][x][0]);if(!s||k>s){let S=(v+pp(b[y][v][x][0]))/l*c,C=(y+pp(b[y][v][x][1]))/l*u,E=Math.exp(b[y][v][x][2])*this.config.anchors[x].x/l*c,$=Math.exp(b[y][v][x][3])*this.config.anchors[x].y/l*u,F=S-E/2,A=C-$/2,R={row:y,col:v,anchor:x},{classScore:T,label:L}=this.withClassScores?await this.extractPredictedClass(f,R):{classScore:1,label:0};m.push({box:new _i(F,A,F+E,A+$),score:k,classScore:k*T,label:L,...R})}}return d.dispose(),h.dispose(),f.dispose(),m}async extractPredictedClass(n,r){let{row:s,col:a,anchor:o}=r,i=await n.array();return Array(this.config.classes.length).fill(0).map((c,u)=>i[s][a][o][u]).map((c,u)=>({classScore:c,label:u})).reduce((c,u)=>c.classScore>u.classScore?c:u)}},Bi=VI;Bi.DEFAULT_FILTER_SIZES=[3,16,32,64,128,256,512,1024,1024];var Wi=class extends Bi{constructor(t=!0){let n={withSeparableConvs:t,iouThreshold:b$,classes:["face"],...t?{anchors:v$,meanRgb:x$}:{anchors:y$,withClassScores:!0}};super(n)}get withSeparableConvs(){return this.config.withSeparableConvs}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new gt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return this.withSeparableConvs?I$:w$}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};function vde(e,t=!0){let n=new Wi(t);return n.extractWeights(e),n}var Sp=class extends es{constructor(){super(...arguments);this._name="TinyFaceDetectorOptions"}};var xr=class{async then(t){return t(await this.run())}async run(){throw new Error("ComposableTask - run is not implemented")}};async function Vi(e,t,n,r,s=({alignedRect:a})=>a){let a=e.map(c=>Oi(c)?s(c):c.detection),o=r||(t instanceof Te?await ul(t,a):await cl(t,a)),i=await n(o);return o.forEach(c=>c instanceof Te&&c.dispose()),i}async function yl(e,t,n,r,s){return Vi([e],t,async a=>n(a[0]),r,s)}var T$=.4,C$=[new Fe(1.603231,2.094468),new Fe(6.041143,7.080126),new Fe(2.882459,3.518061),new Fe(4.266906,5.178857),new Fe(9.041765,10.66308)],N$=[117.001,114.697,97.404];var Ui=class extends Bi{constructor(){let t={withSeparableConvs:!0,iouThreshold:T$,classes:["face"],anchors:C$,meanRgb:N$,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};super(t)}get anchors(){return this.config.anchors}async locateFaces(t,n){return(await this.detect(t,n)).map(s=>new gt(s.score,s.relativeBox,{width:s.imageWidth,height:s.imageHeight}))}getDefaultModelName(){return"tiny_face_detector_model"}extractParamsFromWeightMap(t){return super.extractParamsFromWeightMap(t)}};var et={ssdMobilenetv1:new $a,tinyFaceDetector:new Ui,tinyYolov2:new Wi,faceLandmark68Net:new Mi,faceLandmark68TinyNet:new Ip,faceRecognitionNet:new Li,faceExpressionNet:new xp,ageGenderNet:new wp},_$=(e,t)=>et.ssdMobilenetv1.locateFaces(e,t),xde=(e,t)=>et.tinyFaceDetector.locateFaces(e,t),wde=(e,t)=>et.tinyYolov2.locateFaces(e,t),E$=e=>et.faceLandmark68Net.detectLandmarks(e),Ide=e=>et.faceLandmark68TinyNet.detectLandmarks(e),kde=e=>et.faceRecognitionNet.computeFaceDescriptor(e),Sde=e=>et.faceExpressionNet.predictExpressions(e),Tde=e=>et.ageGenderNet.predictAgeAndGender(e),A$=e=>et.ssdMobilenetv1.load(e),Cde=e=>et.tinyFaceDetector.load(e),Nde=e=>et.tinyYolov2.load(e),_de=e=>et.faceLandmark68Net.load(e),Ede=e=>et.faceLandmark68TinyNet.load(e),Ade=e=>et.faceRecognitionNet.load(e),$de=e=>et.faceExpressionNet.load(e),Dde=e=>et.ageGenderNet.load(e),Fde=A$,Rde=_$,Pde=E$;var Sg=class extends xr{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.extractedFaces=s}},Gi=class extends Sg{async run(){let t=await this.parentTask,n=await Vi(t,this.input,async r=>Promise.all(r.map(s=>et.faceExpressionNet.predictExpressions(s))),this.extractedFaces);return t.map((r,s)=>hg(r,n[s]))}withAgeAndGender(){return new qi(this,this.input)}},Hi=class extends Sg{async run(){let t=await this.parentTask;if(!t)return;let n=await yl(t,this.input,r=>et.faceExpressionNet.predictExpressions(r),this.extractedFaces);return hg(t,n)}withAgeAndGender(){return new ji(this,this.input)}},Da=class extends Gi{withAgeAndGender(){return new Ra(this,this.input)}withFaceDescriptors(){return new Gs(this,this.input)}},Fa=class extends Hi{withAgeAndGender(){return new Pa(this,this.input)}withFaceDescriptor(){return new Hs(this,this.input)}};var Tg=class extends xr{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.extractedFaces=s}},qi=class extends Tg{async run(){let t=await this.parentTask,n=await Vi(t,this.input,async r=>Promise.all(r.map(s=>et.ageGenderNet.predictAgeAndGender(s))),this.extractedFaces);return t.map((r,s)=>{let{age:a,gender:o,genderProbability:i}=n[s];return wg(Ig(r,o,i),a)})}withFaceExpressions(){return new Gi(this,this.input)}},ji=class extends Tg{async run(){let t=await this.parentTask;if(!t)return;let{age:n,gender:r,genderProbability:s}=await yl(t,this.input,a=>et.ageGenderNet.predictAgeAndGender(a),this.extractedFaces);return wg(Ig(t,r,s),n)}withFaceExpressions(){return new Hi(this,this.input)}},Ra=class extends qi{withFaceExpressions(){return new Da(this,this.input)}withFaceDescriptors(){return new Gs(this,this.input)}},Pa=class extends ji{withFaceExpressions(){return new Fa(this,this.input)}withFaceDescriptor(){return new Hs(this,this.input)}};var Tp=class extends xr{constructor(n,r){super();this.parentTask=n;this.input=r}},Gs=class extends Tp{async run(){let t=await this.parentTask;return(await Vi(t,this.input,r=>Promise.all(r.map(s=>et.faceRecognitionNet.computeFaceDescriptor(s))),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}))).map((r,s)=>xg(t[s],r))}withFaceExpressions(){return new Da(this,this.input)}withAgeAndGender(){return new Ra(this,this.input)}},Hs=class extends Tp{async run(){let t=await this.parentTask;if(!t)return;let n=await yl(t,this.input,r=>et.faceRecognitionNet.computeFaceDescriptor(r),null,r=>r.landmarks.align(null,{useDlibAlignment:!0}));return xg(t,n)}withFaceExpressions(){return new Fa(this,this.input)}withAgeAndGender(){return new Pa(this,this.input)}};var Cp=class extends xr{constructor(n,r,s){super();this.parentTask=n;this.input=r;this.useTinyLandmarkNet=s}get landmarkNet(){return this.useTinyLandmarkNet?et.faceLandmark68TinyNet:et.faceLandmark68Net}},Np=class extends Cp{async run(){let t=await this.parentTask,n=t.map(o=>o.detection),r=this.input instanceof Te?await ul(this.input,n):await cl(this.input,n),s=await Promise.all(r.map(o=>this.landmarkNet.detectLandmarks(o)));return r.forEach(o=>o instanceof Te&&o.dispose()),t.filter((o,i)=>s[i]).map((o,i)=>ml(o,s[i]))}withFaceExpressions(){return new Da(this,this.input)}withAgeAndGender(){return new Ra(this,this.input)}withFaceDescriptors(){return new Gs(this,this.input)}},_p=class extends Cp{async run(){let t=await this.parentTask;if(!t)return;let{detection:n}=t,r=this.input instanceof Te?await ul(this.input,[n]):await cl(this.input,[n]),s=await this.landmarkNet.detectLandmarks(r[0]);return r.forEach(a=>a instanceof Te&&a.dispose()),ml(t,s)}withFaceExpressions(){return new Fa(this,this.input)}withAgeAndGender(){return new Pa(this,this.input)}withFaceDescriptor(){return new Hs(this,this.input)}};var Ep=class extends xr{constructor(n,r=new vr){super();this.input=n;this.options=r}},vl=class extends Ep{async run(){let{input:t,options:n}=this,r;if(n instanceof Sp)r=et.tinyFaceDetector.locateFaces(t,n);else if(n instanceof vr)r=et.ssdMobilenetv1.locateFaces(t,n);else if(n instanceof es)r=et.tinyYolov2.locateFaces(t,n);else throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | TinyYolov2Options");return r}runAndExtendWithFaceDetections(){return new Promise((t,n)=>{this.run().then(r=>t(r.map(s=>$i({},s)))).catch(r=>n(r))})}withFaceLandmarks(t=!1){return new Np(this.runAndExtendWithFaceDetections(),this.input,t)}withFaceExpressions(){return new Gi(this.runAndExtendWithFaceDetections(),this.input)}withAgeAndGender(){return new qi(this.runAndExtendWithFaceDetections(),this.input)}},Ap=class extends Ep{async run(){let t=await new vl(this.input,this.options),n=t[0];return t.forEach(r=>{r.score>n.score&&(n=r)}),n}runAndExtendWithFaceDetection(){return new Promise(async t=>{let n=await this.run();t(n?$i({},n):void 0)})}withFaceLandmarks(t=!1){return new _p(this.runAndExtendWithFaceDetection(),this.input,t)}withFaceExpressions(){return new Hi(this.runAndExtendWithFaceDetection(),this.input)}withAgeAndGender(){return new ji(this.runAndExtendWithFaceDetection(),this.input)}};function Ode(e,t=new vr){return new Ap(e,t)}function Cg(e,t=new vr){return new vl(e,t)}async function $$(e,t){return Cg(e,new vr(t?{minConfidence:t}:{})).withFaceLandmarks().withFaceDescriptors()}async function Mde(e,t={}){return Cg(e,new es(t)).withFaceLandmarks().withFaceDescriptors()}var Lde=$$;function UI(e,t){if(e.length!==t.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");let n=Array.from(e),r=Array.from(t);return Math.sqrt(n.map((s,a)=>s-r[a]).reduce((s,a)=>s+a*a,0))}var $p=class{constructor(t,n=.6){this._distanceThreshold=n;let r=Array.isArray(t)?t:[t];if(!r.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");let s=1,a=()=>`person ${s++}`;this._labeledDescriptors=r.map(o=>{if(o instanceof bs)return o;if(o instanceof Float32Array)return new bs(a(),[o]);if(o.descriptor&&o.descriptor instanceof Float32Array)return new bs(a(),[o.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}get labeledDescriptors(){return this._labeledDescriptors}get distanceThreshold(){return this._distanceThreshold}computeMeanDistance(t,n){return n.map(r=>UI(r,t)).reduce((r,s)=>r+s,0)/(n.length||1)}matchDescriptor(t){return this.labeledDescriptors.map(({descriptors:n,label:r})=>new al(r,this.computeMeanDistance(t,n))).reduce((n,r)=>n.distance<r.distance?n:r)}findBestMatch(t){let n=this.matchDescriptor(t);return n.distance<this._distanceThreshold?n:new al("unknown",n.distance)}toJSON(){return{distanceThreshold:this._distanceThreshold,labeledDescriptors:this._labeledDescriptors.map(t=>t.toJSON())}}static fromJSON(t){let n=t.labeledDescriptors.map(r=>bs.fromJSON(r));return new $p(n,t.distanceThreshold)}};function zde(e){let t=new Ui;return t.extractWeights(e),t}function D$(e,t){let{width:n,height:r}=new yn(t.width,t.height);if(n<=0||r<=0)throw new Error(`resizeResults - invalid dimensions: ${JSON.stringify({width:n,height:r})}`);if(Array.isArray(e))return e.map(s=>D$(s,{width:n,height:r}));if(Oi(e)){let s=e.detection.forSize(n,r),a=e.unshiftedLandmarks.forSize(s.box.width,s.box.height);return ml($i(e,s),a)}return ys(e)?$i(e,e.detection.forSize(n,r)):e instanceof ar||e instanceof gt?e.forSize(n,r):e}var Bde=JA;return gD(Wde);})();