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human
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/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var Human=(()=>{var AI=Object.defineProperty;var vm=e=>{if(typeof require!="undefined")return require(e);throw new Error('Dynamic require of "'+e+'" is not supported')};var xa=(e,t)=>{for(var n in t)AI(e,n,{get:t[n],enumerable:!0})};var d5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var on=(e,t,n)=>(d5(e,t,"read from private field"),n?n.call(e):t.get(e)),Jn=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},ba=(e,t,n,a)=>(d5(e,t,"write to private field"),a?a.call(e,n):t.set(e,n),n);var zoe={};xa(zoe,{Human:()=>E9,default:()=>E9});function vt(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`Human: ModelPath Error: ${r} Expecting JSON file`);return r}function de(...e){let t=new 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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new SS(this.backendInstance),!0}setupRegisteredKernels(){dl(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){dl(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof ku)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),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 a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return Ku.nextTensorId++}nextVariableId(){return Ku.nextVariableId++}clone(e){let t=_.runKernel(Rs,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},u={dtype:i};return _.runKernel(As,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(Ac(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 a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Vm(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Vm(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let y=Ac(h,this.backendName);F(y!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let A=this.backend.numDataIds();o=y.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,A,g);let x=g.map(w=>{if(w.rank!=null)return w;let{dataId:b,shape:v,dtype:N}=w;return this.makeTensorFromDataId(b,v,N)});if(a){let w=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(w)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(y=>this.keep(this.clone(y))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let y=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,f,y),y}}let{inputs:l,attrs:d}=e,p=Vm(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(u,l,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=$m(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>s[l]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Cr(e[0])&&(r=e.map(o=>Uu(o)));let s=a.write(r,t,n),i=new We(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),u=Yx(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new We(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new Xu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Tm(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 Xu||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*Tm(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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=$m(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=u=>(u=u.map((l,d)=>{if(l==null){let p=n[d],c=Pp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return l}),a(u.length>1?u:u[0],r,s))),this.state.activeTape.push(i)}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=Bm(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(r instanceof We,()=>"The result y returned by f() must be a tensor.");let s=ES(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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u=se(W(i,this.rho),W(ot(s),1-this.rho)),l=W(fe(en(se(o,this.epsilon)),en(se(i,this.epsilon))),s),d=se(W(o,this.rho),W(ot(l),1-this.rho));i.assign(u),o.assign(d);let p=se(W(l,-this.learningRate),a);a.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ie(this.accumulatedGrads.map(e=>e.variable)),Ie(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};ih.className="Adadelta";Pr(ih);var oh=class extends mr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=_.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:V(()=>kl(a.shape,this.initialAccumulatorValue).variable(i))}}let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let s=this.accumulatedGrads[n].variable;V(()=>{let i=se(s,ot(r));s.assign(i);let o=se(W(fe(r,en(se(i,_.backend.epsilon()))),-this.learningRate),a);a.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ie(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};oh.className="Adagrad";Pr(oh);var lh=class extends mr{constructor(e,t,n,a=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),a==null&&(this.epsilon=_.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ye(1,this.accBeta1),a=ye(1,this.accBeta2);t.forEach((r,s)=>{let i=_.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:V(()=>Ge(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${r}/v`,variable:V(()=>Ge(i).variable(o))});let u=Array.isArray(e)?e[s].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,p=se(W(l,this.beta1),W(u,1-this.beta1)),c=se(W(d,this.beta2),W(ot(u),1-this.beta2)),h=fe(p,n),m=fe(c,a);l.assign(p),d.assign(c);let f=se(W(fe(h,se(en(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(W(this.accBeta1,this.beta1)),this.accBeta2.assign(W(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ie(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ie(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(hr(this.beta1,this.iterations_+1)),this.accBeta2.assign(hr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};lh.className="Adam";Pr(lh);var uh=class extends mr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),a==null&&(this.epsilon=_.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=ye(1,this.accBeta1),a=fe(-this.learningRate,se(W(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=_.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ge(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ge(i).variable(o)});let u=Array.isArray(e)?e[s].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=se(W(l,this.beta1),W(u,1-this.beta1)),c=W(d,this.beta2),h=Lt(u),m=Ua(c,h);l.assign(p),d.assign(m);let f=se(W(fe(a,n),fe(p,se(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(W(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ie(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ie(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)}};uh.className="Adamax";Pr(uh);var fd=class extends mr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=_.registeredVariables[t];V(()=>{let s=se(W(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};fd.className="SGD";Pr(fd);var dh=class extends fd{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=_.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:V(()=>Ge(a).variable(i))}}let r=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&V(()=>{let i,o=se(W(this.m,r),s);this.useNesterov?i=se(W(this.c,se(s,W(o,this.m))),a):i=se(W(this.c,o),a),r.assign(o),a.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ie(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};dh.className="Momentum";Pr(dh);var ph=class extends mr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=_.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=_.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;V(()=>{let u=se(W(i,this.decay),W(ot(s),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,d=se(W(l,this.decay),W(s,1-this.decay)),p=fe(W(s,this.learningRate),en(ye(u,se(ot(d),this.epsilon)))),c=se(W(o,this.momentum),p);i.assign(u),l.assign(d),o.assign(c);let h=ye(a,c);a.assign(h)}else{let l=se(W(i,this.decay),W(ot(s),1-this.decay)),d=se(W(o,this.momentum),fe(W(s,this.learningRate),en(se(l,this.epsilon))));i.assign(l),o.assign(d);let p=ye(a,d);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ie(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ie(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ie(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};ph.className="RMSProp";Pr(ph);var Ii=class{static sgd(e){return new fd(e)}static momentum(e,t,n=!1){return new dh(e,t,n)}static rmsprop(e,t=.9,n=0,a=null,r=!1){return new ph(e,t,n,a,r)}static adam(e=.001,t=.9,n=.999,a=null){return new lh(e,t,n,a)}static adadelta(e=.001,t=.95,n=null){return new ih(e,t,n)}static adamax(e=.002,t=.9,n=.999,a=null,r=0){return new uh(e,t,n,a,r)}static adagrad(e,t=.1){return new oh(e,t)}},Si={sgd:Ii.sgd,momentum:Ii.momentum,adadelta:Ii.adadelta,adagrad:Ii.adagrad,rmsprop:Ii.rmsprop,adamax:Ii.adamax,adam:Ii.adam},LF=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function ch(){return new Promise(e=>LF(()=>e()))}var R={};Fe(R,{ERF_A1:()=>ZF,ERF_A2:()=>YF,ERF_A3:()=>JF,ERF_A4:()=>QF,ERF_A5:()=>e$,ERF_P:()=>KF,PARALLELIZE_THRESHOLD:()=>Q1,SELU_SCALE:()=>q3,SELU_SCALEALPHA:()=>G3,applyActivation:()=>rh,assertAndGetBroadcastShape:()=>ct,assertAxesAreInnerMostDims:()=>hC,assertParamsConsistent:()=>WF,assignToTypedArray:()=>l$,axesAreInnerMostDims:()=>F1,calculateShapes:()=>zb,checkEinsumDimSizes:()=>f$,combineLocations:()=>A3,complexWithEvenIndex:()=>s$,complexWithOddIndex:()=>i$,computeConv2DInfo:()=>ed,computeConv3DInfo:()=>Qb,computeDefaultPad:()=>A1,computeDilation2DInfo:()=>_T,computeOptimalWindowSize:()=>VF,computeOutAndReduceShapes:()=>g3,computeOutShape:()=>BF,computePool2DInfo:()=>Jb,computePool3DInfo:()=>PT,convertConv2DDataFormat:()=>e3,decodeEinsumEquation:()=>c$,eitherStridesOrDilationsAreOne:()=>Va,expandShapeToKeepDim:()=>wi,exponent:()=>d$,exponents:()=>u$,fromStringArrayToUint8:()=>k$,fromUint8ToStringArray:()=>w$,getAxesPermutation:()=>x3,getBroadcastDims:()=>EE,getComplexWithIndex:()=>o$,getEinsumComputePath:()=>m$,getEinsumPermutation:()=>h$,getFusedBiasGradient:()=>ah,getFusedDyActivation:()=>nh,getImageCenter:()=>jF,getInnerMostAxes:()=>fC,getPermuted:()=>HF,getReductionAxes:()=>Wt,getReshaped:()=>UF,getReshapedPermuted:()=>GF,getSliceBeginCoords:()=>qF,getSliceSize:()=>XF,getUndoAxesPermutation:()=>$1,isIdentityPermutation:()=>y$,log:()=>n$,mergeRealAndImagArrays:()=>a$,prepareAndValidate:()=>Db,prepareSplitSize:()=>g$,segment_util:()=>Z3,shouldFuse:()=>sh,slice_util:()=>un,splitRealAndImagArrays:()=>r$,tupleValuesAreOne:()=>Lr,upcastType:()=>da,validateInput:()=>a1,validateUpdateShape:()=>n1,warn:()=>t$});function WF(e,t){let n=e[0].length;e.forEach((r,s)=>{F(r.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let a=e[0];e.forEach((r,s)=>{for(let i=0;i<n;i++)F(i===t||r[i]===a[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${r}) does not match the shape of the rest (${a}) along the non-concatenated axis ${s}.`)})}function BF(e,t){let n=e[0].slice();for(let a=1;a<e.length;a++)n[t]+=e[a][t];return n}var Q1=30;function VF(e){return e<=Q1?e:_p(e,Math.floor(Math.sqrt(e)))}function jF(e,t,n){let a=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[a,r]}function UF(e,t,n,a=!0){let r=[];if(a)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(s+1))}return r}function HF(e,t,n=!0){let a=[];if(n){a.push(t);for(let 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d=R.computePool3DInfo(s.shape,i,o,1,u,l),p=d.strideDepth,c=d.strideHeight,h=d.strideWidth,m=d.filterDepth,f=d.filterHeight,y=d.filterWidth,A=d.dilationDepth,g=d.dilationHeight,x=d.dilationWidth,w=d.effectiveFilterDepth,b=d.effectiveFilterHeight,v=d.effectiveFilterWidth,N=w-1-d.padInfo.front,I=v-1-d.padInfo.left,E=b-1-d.padInfo.top,$=Be(s.shape,"float32"),O=1/(m*f*y),z=n.bufferSync(r);for(let P=0;P<d.batchSize;++P)for(let D=0;D<d.inChannels;++D)for(let U=0;U<d.inDepth;++U)for(let X=0;X<d.inHeight;++X)for(let G=0;G<d.inWidth;++G){let ee=U-N,Y=X-E,re=G-I,ne=0;for(let ie=0;ie<w;ie+=A){let Q=(ee+ie)/p;if(!(Q<0||Q>=d.outDepth||Math.floor(Q)!==Q))for(let pe=0;pe<b;pe+=g){let oe=(Y+pe)/c;if(!(oe<0||oe>=d.outHeight||Math.floor(oe)!==oe))for(let ge=0;ge<v;ge+=x){let he=(re+ge)/h;he<0||he>=d.outWidth||Math.floor(he)!==he||(ne+=z.get(P,Q,oe,he,D))}}}$.set(ne*O,P,U,X,G,D)}return n.makeTensorInfo($.shape,$.dtype,$.values)}var ez={kernelName:Wp,backendName:"cpu",kernelFunc:QD};function tz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ve([r,s],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=a,d=R.computePool2DInfo(i.shape,o,u,1,l),p=d.strideHeight,c=d.strideWidth,h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,y=d.dilationWidth,A=d.effectiveFilterHeight,g=d.effectiveFilterWidth,x=g-1-d.padInfo.left,w=A-1-d.padInfo.top,b=Be(i.shape,"float32"),v=1/(h*m),N=n.data.get(r.dataId).values,I=Be(r.shape,"float32",N);for(let E=0;E<d.batchSize;++E)for(let $=0;$<d.inChannels;++$)for(let O=0;O<d.inHeight;++O)for(let z=0;z<d.inWidth;++z){let P=O-w,D=z-x,U=0;for(let X=0;X<A;X+=f){let G=(P+X)/p;if(!(G<0||G>=d.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=y){let Y=(D+ee)/c;Y<0||Y>=d.outWidth||Math.floor(Y)!==Y||(U+=I.get(E,G,Y,$))}}b.set(U*v,E,O,z,$)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var nz={kernelName:Lp,backendName:"cpu",kernelFunc:tz};function 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n.makeTensorInfo(r.shape,r.dtype,f)}var rz={kernelName:Es,backendName:"cpu",kernelFunc:az};function sz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ve([r],"batchToSpaceND");let o=s.reduce((A,g)=>A*g),u=R.getReshaped(r.shape,s,o),l=R.getPermuted(u.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=ht({inputs:{x:r},backend:n,attrs:{shape:u}}),m=na({inputs:{x:h},backend:n,attrs:{perm:l}}),f=ht({inputs:{x:m},backend:n,attrs:{shape:d}}),y=Ti({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),y}var iz={kernelName:Eu,backendName:"cpu",kernelFunc:sz};function oz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,l=ay(o,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,l)}var lz={kernelName:Bp,backendName:"cpu",kernelFunc:oz},uz=rt(Fr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),dz={kernelName:Fr,backendName:"cpu",kernelFunc:uz},pz=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let d=o[l],p=u[l];a[l]=Math.hypot(d,p)}return n.makeOutput(a,t.shape,"float32")},cz={kernelName:Cu,backendName:"cpu",kernelFunc:pz};function Dl(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var hz={kernelName:nc,backendName:"cpu",kernelFunc:Dl};function zl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=R.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return 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Wz={kernelName:qp,backendName:"cpu",kernelFunc:Lz};function Bz(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:d}=a;ve([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=R.computeConv2DInfo(d,s.shape,i,o,u,l,!0),m=new Pt(h.inShape,"float32"),f=m.values,[y,A,g]=m.strides,x=n.data.get(r.dataId).values,[w,b,v]=p,N=n.data.get(s.dataId).values,[I,E,$]=c,{batchSize:O,filterHeight:z,filterWidth:P,inChannels:D,inHeight:U,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:re,strideWidth:ne}=h,ie=z-1-h.padInfo.top,Q=P-1-h.padInfo.left,pe=G/D;for(let oe=0;oe<O;++oe)for(let ge=0;ge<D;++ge)for(let he=0;he<U;++he){let Ne=he-ie,Te=Math.max(0,Math.ceil(Ne/re)),De=Math.min(ee,(z+Ne)/re);for(let _e=0;_e<X;++_e){let ze=_e-Q,tt=Math.max(0,Math.ceil(ze/ne)),nt=Math.min(Y,(P+ze)/ne),it=0;for(let Ze=Te;Ze<De;++Ze){let pt=Ze*re-Ne;for(let Ue=tt;Ue<nt;++Ue){let 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}
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`,u="",l=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,u=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,l=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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}
`}var gv=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
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if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
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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);
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${Mi(["r","c","d"],e)}
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vec4 values = ${a.texture2D}(A, uv);
float result;
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result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
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${a.output} = vec4(${i}, 0., 0., 0.);
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flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
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result[${d}] = values[0];
} else if(offset == 1) {
result[${d}] = values[1];
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result[${d}] = values[2];
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result[${d}] = values[3];
}
}
}
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ivec3 localCoords;
vec2 uv;
vec4 values;
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a=this.gl;vh(a,e,this.framebuffer),this.debug&&wd(a),this.outputTexture=e,xe(a,()=>a.viewport(0,0,t,n)),xe(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,a))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function zL(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:Ov}=R;function OL(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
`),i=e.map(h=>_L(h,t,a)).join(`
`),o=t.texShape,u=hn(),l=WL(u),d,p,c=jL(u);return t.isPacked?(d=PL(t.logicalShape,o),p=VL(u)):(d=LL(t.logicalShape,o),p=BL(u)),a&&(c+=qL),[c,l,p,s,d,i,n].join(`
`)}function Pl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return sW(e);case 1:return oW(e);case 2:return uW(e);case 3:return pW(e);case 4:return hW(e);case 5:return fW(e);case 6:return mW(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function _v(e){switch(e.shapeInfo.logicalShape.length){case 0:return rW(e);case 1:return iW(e);case 2:return lW(e);case 3:return dW(e);default:return cW(e)}}function _L(e,t,n=!1){let a="";n?a+=_v(e):a+=Pl(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=yW(e,t):a+=AW(e,t)),a}function PL(e,t){switch(e.length){case 0:return Pv();case 1:return XL(e,t);case 2:return nW(e,t);case 3:return ZL(e,t);default:return JL(e,t)}}function LL(e,t){switch(e.length){case 0:return Pv();case 1:return KL(e,t);case 2:return aW(e,t);case 3:return YL(e,t);case 4:return QL(e,t);case 5:return eW(e,t);case 6:return tW(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function WL(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function BL(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function VL(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function jL(e){return`${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);
}
${UL}
${HL}
${GL}
`}var UL=`
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);
}
`,HL=`
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);
}
`,GL=`
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);
}
`,qL=`
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 Pv(){return`
int getOutputCoords() {
return 0;
}
`}function XL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function KL(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function ZL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec3(b, r, c);
}
`}function YL(e,t){let n=Mi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function JL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let u=2;u<e.length-1;u++)s*=e[e.length-u-1],i=`
int b${u} = index / ${s};
index -= b${u} * ${s};
`+i,o=`b${u}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${r};
index -= b * ${r};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec${e.length}(${o});
}
`}function QL(e,t){let n=Mi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function eW(e,t){let n=Mi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function tW(e,t){let n=Mi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function nW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let a=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec2(r, c);
}
`}function aW(e,t){return k.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Fi(e){return`offset${e}`}function rW(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=hn();return`
vec4 ${n}() {
return ${a.texture2D}(${t}, halfCR);
}
`}function sW(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=Fi(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function iW(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=hn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${r[0]}, ${r[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function oW(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Ll(e)}
}
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=Fi(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
return sampleTexture(${t}, uv);
}
`:r===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function lW(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=hn();if(r!=null&&k.arraysEqual(t,r))return`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let u=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],l=Math.ceil(t[1]/2);return`
vec4 ${a}(int row, int col) {
vec2 uv = packedUVfrom2D(${l}, ${u[0]}, ${u[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function uW(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],c=r[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=Wl(e,o),c=["row","col"];return`
${Pl(p)}
float ${a}(int row, int col) {
return ${a}(${Bl(c,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${Ll(e)}
}
`;let u=r[0],l=r[1],d=Fi(n);return l===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${n}, uv);
}
`:u===1?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${d};
vec2 uv = uvFromFlat(${u}, ${l}, index);
return sampleTexture(${n}, uv);
}
`}function dW(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),c=[1,2],h=Wl(e,p),m=["b","row","col"];return`
${_v(h)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${Bl(m,c)});
}
`}let i=s[0],o=s[1],u=Math.ceil(t[2]/2),l=u*Math.ceil(t[1]/2),d=hn();return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${l}, ${u}, b, row, col);
return ${d.texture2D}(${n}, uv);
}
`}function pW(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),u=i;if(u.length<t.length){let m=Wl(e,u),f=["row","col","depth"];return`
${Pl(m)}
float ${a}(int row, int col, int depth) {
return ${a}(${Bl(f,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${r}, ${s}, 1)));
${Ll(e)}
}
`;let l=e.shapeInfo.texShape,d=l[0],p=l[1],c=e.shapeInfo.flatOffset;if(p===r&&c==null)return`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(p===s&&c==null)return`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let h=Fi(n);return`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r} + col * ${s} + depth + ${h};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function cW(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],u=i[1],l=Math.ceil(t[n-1]/2),d=l*Math.ceil(t[n-2]/2),p="int b, int row, int col",c=`b * ${d} + (row / 2) * ${l} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,d*=t[n-m-1],c=`b${m} * ${d} + `+c;let h=hn();return`
vec4 ${r}(${p}) {
int index = ${c};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${o});
return ${h.texture2D}(${a}, uv);
}
`}function hW(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:u}=k.squeezeShape(t);if(o.length<t.length){let m=Wl(e,o),f=["row","col","depth","depth2"];return`
${Pl(m)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${Bl(f,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${r}, 1)));
${Ll(e)}
}
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],c=d[1];if(c===i&&l==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(c===r&&l==null)return`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let h=Fi(n);return`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${r} + depth2;
vec2 uv = uvFromFlat(${p}, ${c}, index + ${h});
return sampleTexture(${n}, uv);
}
`}function fW(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:u,keptDims:l}=k.squeezeShape(t);if(u.length<t.length){let f=Wl(e,u),y=["row","col","depth","depth2","depth3"];return`
${Pl(f)}
float ${a}(int row, int col, int depth, int depth2, int depth3) {
return ${a}(${Bl(y,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${r})) +
depth3;
${Ll(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&d==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&d==null)return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;let m=Fi(n);return`
float ${a}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
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`}},AB=Ga.whereImpl,gB=1e-7,xB=1e-4,Ey={};function bB(e){return e in Ey||(Ey[e]={}),Ey[e]}var vB=128,wB=600;function kB(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*wB/1024/1024}var jl=class extends ku{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Xa(J().getNumber("WEBGL_VERSION"));this.binaryCache=bB(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Nh(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new nB(this.gpgpu),this.numMBBeforeWarning=kB(),this.texData=new zp(this,dr())}nextDataId(){return jl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().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 a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:aa.UPLOAD,refCount:1}),a}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,a,r){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:aa.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new Vl(i,Th):p=new Gr(i,Th);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let u=this.activeTimers!=null,l;u&&(l=k.now());let d;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);d=R.mergeRealAndImagArrays(p,c)}else d=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=k.now()-l),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Vl(a,Th):h=new Gr(a,Th);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture,...bd(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=R.mergeRealAndImagArrays(m,f)}else if(u==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}l!=null&&this.disposeIntermediateTensorInfo(l);let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&dr().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!X7(n))throw J().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...bd(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=J().getBool("WEBGL_PACK")&&a===!0,i=s?wh(t):t,o=s?new FL(i):new ML(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),d}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:s[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(J().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:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=vB){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return AB(e.shape,t)}packedUnaryOp(e,t,n){let a=new Vl(e.shape,t),r=this.compileAndRun(a,[e],n);return dr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=Bv(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,qv,e.dtype);let t=new Gr(e.shape,qv),n=this.compileAndRun(t,[e]);return dr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return dr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new yB(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ZW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ci(e.shape),...Ri(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Ci(t),...Ri(t)],s=new jv(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=wh(a),i;n?i=new RL(s):i=new CL(s);let o=!0,u=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:u.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===gd.DENSE){let f=bd(e.outputShape);i.texShape=f.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],u=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(f.dataId);if(y.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=f.shape)}else if(!!y.isPacked!=!!e.packedInputs)f=y.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),y=this.texData.get(f.dataId);else if(y.isPacked&&!kd(y.shape,f.shape)){let A=f,g=f.shape;f.shape=y.shape,f=this.packedReshape(f,g),o.push(f),y=this.texData.get(f.dataId),A.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:y,isUniform:!1}});this.uploadToGPU(s.dataId);let l={shape:s.shape,texData:i,isUniform:!1},d=bW(e,u,l),p=this.getAndSaveBinary(d,()=>gW(this.gpgpu,e,u,l)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),xW(this.gpgpu,p,u,l,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?gB:xB}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let u=this.activeTimers!=null,l;u&&(l=k.now());let d=t.texShape;if(d==null&&(d=pv(n,o),t.texShape=d),r!=null){let p=wh(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=Ol(d[0],d[1]),c=new DL(p,[m,h],f)):c=new $L(p,[m,h],f);let y=this.makeTensorInfo([m,h],a);f?this.texData.get(y.dataId).usage=aa.PIXELS:this.texData.get(y.dataId).usage=aa.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,m,r);let A=!0,g=this.runWebGLProgram(c,[y],a,null,A),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(g.dataId),t.values=null,u&&(this.uploadWaitMs+=k.now()-l)}else{let p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=IB(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};jl.nextDataId=0;function IB(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 a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var Xv="3.6.0";function Kv(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Zu.isBrowser()&&Al("webgl",()=>new jl,2);var SB={forceHalfFloat:Kv},Zv=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Ul=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Eh=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`,Sd=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${ut(r)} coords = getOutputCoords();
`,r===1)s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=fn("coords",r);s+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= ${this.outputShape[r-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);
${s}
setOutput(result);
}
`}};function Vn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var NB={kernelName:Rs,backendName:"webgl",kernelFunc:Vn};function qr(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Vn({inputs:{x:a},backend:n}),u=Vn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},s}var TB={kernelName:Vp,backendName:"webgl",kernelFunc:qr},Yv="return (a < 0.) ? b * a : a;",Jv=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function EB(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(Jv,r.shape,i.shape):new Ul(Yv,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),u}var CB={kernelName:Ms,backendName:"webgl",kernelFunc:EB},Qv="return (a < 0.) ? b * a : a;",ew=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function RB(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(ew,a.shape,r.shape):new Ul(Qv,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var MB={kernelName:Us,backendName:"webgl",kernelFunc:RB},tw="if (isnan(x)) return x;",FB=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,$B=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,u=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,u);return o.makeTensorInfo(i.shape,u,c)}let l=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return l?d=new Vl(i.shape,t):d=new Gr(i.shape,e),o.runWebGLProgram(d,[i],u)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,d=o;if(a&&u.dtype==="complex64"){let m=d.texData.get(u.dataId),f=d.texData.get(l.dataId),[y,A]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[w,b]=x,v={dataId:w.dataId,dtype:w.dtype,shape:u.shape},N={dataId:b.dataId,dtype:b.dtype,shape:l.shape},I=new Ul(e,u.shape,l.shape);return d.runWebGLProgram(I,[v,N],da(w.dtype,b.dtype))}),g=qr({inputs:{real:y,imag:A},backend:d});return d.disposeIntermediateTensorInfo(y),d.disposeIntermediateTensorInfo(A),g}let p=s||da(u.dtype,l.dtype);if(d.shouldExecuteOnCPU([u,l])&&r!=null){let m=d.texData.get(u.dataId),f=d.texData.get(l.dataId),[y,A]=r(u.shape,l.shape,m.values,f.values,p),g=d.makeTensorInfo(A,p),x=d.texData.get(g.dataId);return x.values=y,g}let c=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new Sd(t,u.shape,l.shape,n):h=new Ul(e,u.shape,l.shape),d.runWebGLProgram(h,[u,l],p)}}function Ch(e,t=!1){if(e==="linear")return t?pB:iB;if(e==="relu")return t?hB:lB;if(e==="elu")return t?cB:oB;if(e==="relu6")return t?fB:uB;if(e==="prelu")return t?ew:Qv;if(e==="leakyrelu")return t?Jv:Yv;if(e==="sigmoid")return t?mB:dB;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var nw=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let l=a?e[1]:e[2],d=Math.ceil(l/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",y="";i&&(o?f=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:u?f=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:f=`vec4 activation(vec4 x) {
${i}
}`,y="result = activation(result);");let A=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${f}
const float sharedDimension = ${d}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${d}; i++) {
int batchA = ${g};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${p});
vec4 b = getMatrixB(batchB, ${c});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${m[0]});
result += (${h[1]} * ${m[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${A}
${y}
setOutput(result);
}
`}},aw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},rw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},sw="return a * b;";function Cy(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=R.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),u=n.texData.get(r.dataId),l=new rw(aw.REAL,a.shape,r.shape),d=new rw(aw.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=qr({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),u=n.texData.get(r.dataId),[l,d]=OW(a.shape,r.shape,o.values,u.values,s),p=n.makeTensorInfo(d,s),c=n.texData.get(p.dataId);return c.values=l,p}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Sd(sw,a.shape,r.shape):i=new Ul(sw,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var DB={kernelName:Ws,backendName:"webgl",kernelFunc:Cy};function zB(e,t,n){let a=[Ci(e.shape),...Ri(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[Ci(t),...Ri(t)],i=new jv(s,a),o=!0,u=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),u=k.inferFromImplicitShape(s,o),l=k.sizeFromShape(u);k.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(r.dataId);return d.isPacked&&!kd(r.shape,u)&&!(d.texture!==null&&kd(d.shape,u))?zB(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var OB={kernelName:Xo,backendName:"webgl",kernelFunc:Ae},iw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let d=1/t;u=`sumValue += dot(values * ${k.isInt(d)?d.toPrecision(2):d}, ones);`}let l="";r%n>0&&(l=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${l}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${u}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${u}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${u}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${u}
}
setOutput(sumValue);
}
`}},_B=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,d=n%4,p=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
}
`,c="vec4";t==="all"?(i="1.0",p=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,c="bvec4"):t==="any"&&(i="0.0",p=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,c="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${p}
}
int inIdx = inOffset + ${l};
if (${d===1}) {
${c} values = ${c}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${p}
} else if (${d===2}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${p}
} else if (${d===3}) {
${c} values = ${c}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${p}
}
setOutput(${u});
}
`}};function PB(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function $i(e,t,n,a){let r=PB(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],d,p;n==="mean"?d=i===0?new iw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new iw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):d=new _B({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=s,s=a.runWebGLProgram(d,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var LB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=WB(t);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function WB(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var BB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ut(this.rank),r=Vv("rc",this.rank),s=new Array(this.rank);for(let l=0;l<t.length;l++)s[t[l]]=r[l];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${a} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${u};
if(${o}) {
result[1] = ${u};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${u};
if(${o}) {
result[3] = ${u};
}
}
setOutput(result);
}
`}};function Rh(e,t,n){let a=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new BB(e.shape,t):new LB(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function VB(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,u=R.getAxesPermutation(o,s),l=u!=null,d=e;l&&(d=Rh(e,u,a),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=R.computeOutAndReduceShapes(d.shape,o),h=p;n&&(h=R.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,y=Ae({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),A=vc(e.dtype),g=$i(y,A,"sum",a),x=Ae({inputs:{x:g},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(y),a.disposeIntermediateTensorInfo(g),l&&a.disposeIntermediateTensorInfo(d),x}function Mh(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return VB(r,s,i,n)}var jB={kernelName:ei,backendName:"webgl",kernelFunc:Mh};function mn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,u=new Array(o);for(let d=0;d<u.length;d++)u[d]=r.shape[s[d]];let l;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=Ty(d,r.shape,r.dtype,s,u);l=i.makeTensorInfo(u,r.dtype);let c=i.texData.get(l.dataId);c.values=p}else l=Rh(r,s,i);return l}var UB={kernelName:ii,backendName:"webgl",kernelFunc:mn},ow=1e3;function Fh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,d=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],c=a?t.shape[d-1]:t.shape[d-2],h=n?e.shape[l-1]:e.shape[l-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),y=t.shape.slice(0,-2),A=k.sizeFromShape(f),g=k.sizeFromShape(y),x=A===g||A===1||g===1;k.assert(l>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${y}).`);let w=(A>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[A,p,h]:[A,h,p],v=a?[g,m,c]:[g,c,m],N=Ae({inputs:{x:e},backend:r,attrs:{shape:b}}),I=Ae({inputs:{x:t},backend:r,attrs:{shape:v}}),E=[N,I],$=Math.max(A,g),O=n?N.shape[1]:N.shape[2],z=s!=null,P=i!=null,D=u==="leakyrelu",U=u!=null?Ch(u,!0):null,X=z||P||D||U!=null,G;if((h===1||m===1)&&O>ow&&X===!1){let Y=N,re=I;n&&(Y=mn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(Y)),a&&(re=mn({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),E.push(re));let ne=m!==1,ie=m===1,Q=Y;ne&&(Q=Ae({inputs:{x:Y},backend:r,attrs:{shape:[$,O,1]}}),E.push(Q));let pe=m===1?2:1,oe=re;ie&&(oe=Ae({inputs:{x:re},backend:r,attrs:{shape:[$,1,O]}}),E.push(oe));let ge=Cy({inputs:{a:Q,b:oe},backend:r});G=Mh({inputs:{x:ge},backend:r,attrs:{axis:pe,keepDims:!0}}),E.push(ge)}else{let Y=da(e.dtype,t.dtype),re=new nw(b,v,[$,h,m],n,a,z,U,P,D),ne=[N,I];if(s!=null&&ne.push(s),P&&ne.push(i),D){let ie=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));ne.push(ie),E.push(ie)}G=r.runWebGLProgram(re,ne,Y)}let ee=Ae({inputs:{x:G},backend:r,attrs:{shape:w}});E.push(G);for(let Y of E)r.disposeIntermediateTensorInfo(Y);return ee}function HB(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a;return Fh({a:r,b:s,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var GB={kernelName:oi,backendName:"webgl",kernelFunc:HB},lw="return abs(x);";function qB(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=Bv(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vl(a.shape,lw):r=new Gr(a.shape,lw),n.runWebGLProgram(r,[a],a.dtype)}var XB={kernelName:oo,backendName:"webgl",kernelFunc:qB},KB=Ia+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,ZB=Ke({opSnippet:KB}),YB={kernelName:lo,backendName:"webgl",kernelFunc:ZB},JB=Ia+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,QB=Ke({opSnippet:JB}),eV={kernelName:uo,backendName:"webgl",kernelFunc:QB},uw="return a + b;",tV=nn({opSnippet:uw,packedOpSnippet:uw,supportsComplex:!0,cpuKernelImpl:vW}),nV={kernelName:Mr,backendName:"webgl",kernelFunc:tV},aV=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${a};
setOutput(result);
}
`}},rV=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${a};
setOutput(result);
}
`}};function $h(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Vn({inputs:{x:a[0]},backend:n});if(a.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),u=$h({inputs:a.slice(0,o),backend:n}),l=$h({inputs:a.slice(o),backend:n});return $h({inputs:[u,l],backend:n})}let r=a.map(o=>o.dtype).reduce((o,u)=>da(o,u)),s=a.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new rV(a[0].shape,s):new aV(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var sV={kernelName:hs,backendName:"webgl",kernelFunc:$h};function iV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=R.getAxesPermutation(l,o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=R.getInnerMostAxes(l.length,o)),R.assertAxesAreInnerMostDims("all",l,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),y=$i(f,f.dtype,"all",n),A;if(i){let g=R.expandShapeToKeepDim(c,u);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),d!=null&&n.disposeIntermediateTensorInfo(p),A}var oV={kernelName:po,backendName:"webgl",kernelFunc:iV};function lV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=R.getAxesPermutation(l,o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=R.getInnerMostAxes(l.length,o)),R.assertAxesAreInnerMostDims("any",l,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),y=$i(f,f.dtype,"any",n),A;if(i){let g=R.expandShapeToKeepDim(c,u);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),d!=null&&n.disposeIntermediateTensorInfo(p),A}var uV={kernelName:co,backendName:"webgl",kernelFunc:lV},dV=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${a};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${a}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},pV=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=ut(o),l=fn("coords",o),d,p;if(s===1){p=o+1;let N=ut(p);d=`
${N} sourceLocR = ${N}(${l.join()}, 0);
++${l[o-1]};
${N} sourceLocG = ${N}(${l.join()}, 0);
++${l[o-2]};
${N} sourceLocA = ${N}(${l.join()}, 0);
--${l[o-1]};
${N} sourceLocB = ${N}(${l.join()}, 0);
--${l[o-2]};`}else p=o,d=`
${u} sourceLocR = coords;
++${l[o-1]};
${u} sourceLocG = coords;
++${l[o-2]};
${u} sourceLocA = coords;
--${l[o-1]};
${u} sourceLocB = coords;
--${l[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(N=>"int "+N),f=fn("sourceLocR",p-1).concat("inIdx.r"),y=fn("sourceLocG",p-1).concat("inIdx.g"),A=fn("sourceLocB",p-1).concat("inIdx.b"),g=fn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",w=a?"":`
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${g.join()})));`,b=`vec4(
getAChannel(${f.join()}),
hasNextCol ? getAChannel(${y.join()}) : 0.,
hasNextRow ? getAChannel(${A.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,v=a?"":`
float getBestIndicesAChannel(${m.join()}) {
return getChannel(getBestIndicesA(${c.join()}),
vec2(${c.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${m.join()}) {
return getChannel(getA(${c.join()}),
vec2(${c.slice(-2).join()}));
}
${v}
void main() {
${u} coords = getOutputCoords();
bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};
${d}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${b};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${w}
vec4 candidate = ${b};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function dw(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},u=new dV(o,n,a==null),l=[t];a!=null&&l.push(a);let d=e.runWebGLProgram(u,l,"int32");if(d.shape[1]===1)return d;let p=dw(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function pw(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=R.computeOptimalWindowSize(s),o=new pV(r,i,n,a==null),u=a==null?[t]:[t,a],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let d=pw(e,t,n,l);return e.disposeIntermediateTensorInfo(l),d}return l}function cw(e,t,n,a){let r=[n];if(R.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,r),u=k.sizeFromShape(o),l=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,u]}});s.push(l);let d=dw(e,l,a);s.push(d);let p=Ae({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return pw(e,t,a)}function cV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=mn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=R.getInnerMostAxes(i.length,u.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let d=cw(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var hV={kernelName:fs,backendName:"webgl",kernelFunc:cV};function fV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=R.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=mn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=R.getInnerMostAxes(i.length,u.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let d=cw(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var mV={kernelName:Nu,backendName:"webgl",kernelFunc:fV},yV=Ia+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,AV=Ke({opSnippet:yV}),gV={kernelName:ho,backendName:"webgl",kernelFunc:AV},xV=Ia+"return log(x + sqrt(x * x + 1.0));",bV=Ke({opSnippet:xV}),vV={kernelName:fo,backendName:"webgl",kernelFunc:bV},wV=Ia+`
return atan(x);
`,kV=Ke({opSnippet:wV}),IV={kernelName:mo,backendName:"webgl",kernelFunc:kV},SV=FB+`
return atan(a, b);
`,NV=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+$B+`
return result;
`,TV=nn({opSnippet:SV,packedOpSnippet:NV}),EV={kernelName:Ao,backendName:"webgl",kernelFunc:TV},CV=Ia+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,RV=Ke({opSnippet:CV}),MV={kernelName:yo,backendName:"webgl",kernelFunc:RV},Nd=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(m||(A="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${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 < ${d};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p};
wC += ${l}) {
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 ${N} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?f:y:`wR * ${p} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let w=Math.floor(s/4)*4,b=s%4,v=`
if (${m}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${g}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${c}, ${h});
const float initializationValue = ${A};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${A});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${d};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${w}; wC += 4) {
int xC = xCCorner + wC * ${l};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
getValue(batch, xR, xC + 3 * ${l}, d)
);
${v}
}
int xC = xCCorner + ${w};
if (${b===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${v}
} else if (${b===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
initializationValue,
initializationValue
);
${v}
} else if (${b===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${l}, d),
getValue(batch, xR, xC + 2 * ${l}, d),
initializationValue
);
${v}
}
}
setOutput(${x});
}
`}},Ry=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,d=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,y=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${u});
const ivec3 pads = ivec3(${f}, ${y}, ${A});
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 < ${c};
wD += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${m};
wC += ${p}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${E} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
wR * ${m} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let w="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let v=Math.floor(s/4)*4,N=s%4,I=`
if (${g}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${w}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${u});
const ivec3 pads = ivec3(${f}, ${y}, ${A});
const float initializationValue = ${x};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${c};
wD += ${l}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${v}; 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)
);
${I}
}
int xC = xCCorner + ${v};
if (${N===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${I}
} else if (${N===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${p}, ch),
initializationValue,
initializationValue
);
${I}
} else if (${N===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
);
${I}
}
}
setOutput(${b});
}
}
`}};function FV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;_l(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(R.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=R.computePool2DInfo(r.shape,s,i,l,o,u);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Vn({inputs:{x:r},backend:n});let p=new Nd(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var $V={kernelName:ms,backendName:"webgl",kernelFunc:FV};function DV(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new Ry(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var zV={kernelName:Tu,backendName:"webgl",kernelFunc:DV},OV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,d=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${l}, ${d});
const float avgMultiplier = float(${p});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},_V=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,y=1/(t*n*a);this.userCode=`
const ivec3 pads = ivec3(${h}, ${m}, ${f});
const float avgMultiplier = float(${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${d};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${p};
wR += ${u}) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${l}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function PV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:u,pad:l,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,u,p,l,d),h=new _V(c);return n.runWebGLProgram(h,[r],i.dtype)}var LV={kernelName:Wp,backendName:"webgl",kernelFunc:PV};function WV(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;_l([r,s],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=a,d=R.computePool2DInfo(i.shape,o,u,1,l),p=new OV(d);return n.runWebGLProgram(p,[r],i.dtype)}var BV={kernelName:Lp,backendName:"webgl",kernelFunc:WV};function VV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return Fh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var jV={kernelName:ys,backendName:"webgl",kernelFunc:VV},UV=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},HV=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},GV=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[a,r,s],d=null;i!=null&&(d=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let c=J().getBool("WEBGL_PACK_NORMALIZATION")?new HV(a.shape,r.shape,s.shape,d,p,u):new UV(a.shape,r.shape,s.shape,d,p,u);return t.runWebGLProgram(c,l,l[0].dtype)},qV={kernelName:Es,backendName:"webgl",kernelFunc:GV},XV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,a=KV(this.rank),r,s=e.map((i,o)=>`sourceLoc.${My[o]} = start[${o}] + coords.${My[o]};`);r=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${r}
setOutput(getSource(${a}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},My=["x","y","z","w","u","v"];function KV(e){if(e===1)return"sourceLoc";if(e<=6)return My.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var ZV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=fn("coords",this.rank),a=fn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.y = ${s};
--${a[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${a[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${a[this.rank-1]};
result.w = ${s};
}
}
`,u=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((l,d)=>`start[${d}]`).join()});`:e.map((l,d)=>`${a[d]} = ${n[d]} + start[${d}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${u}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function YV(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,u+1),s}function Td(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,u]=un.parseSliceParams(r,s,i);if(un.assertParamsValid(r,o,u),k.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=BW(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,c)}let{isPacked:l}=n.texData.get(r.dataId),d=un.isSliceContinous(r.shape,o,u);if(l||!d){let p=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ZV(u):new XV(u),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),YV(r,o,u,n)}var JV={kernelName:Jo,backendName:"webgl",kernelFunc:Td},QV=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),u=R.getReshaped(r.shape,s,o),l=R.getPermuted(u.length,s.length),d=R.getReshapedPermuted(r.shape,s,o),p=R.getSliceBeginCoords(i,s.length),c=R.getSliceSize(d,i,s.length),h=[],m=Ae({inputs:{x:r},backend:n,attrs:{shape:u}}),f=mn({inputs:{x:m},backend:n,attrs:{perm:l}}),y=Ae({inputs:{x:f},backend:n,attrs:{shape:d}}),A=Td({inputs:{x:y},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(y),h.forEach(g=>n.disposeIntermediateTensorInfo(g)),A},ej={kernelName:Eu,backendName:"webgl",kernelFunc:QV};function tj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),u=n.readSync(s.dataId),l=Wv(o,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,l)}var nj={kernelName:Bp,backendName:"webgl",kernelFunc:tj},aj="return float(a != b);",hw=nn({opSnippet:aj,dtype:"bool"}),rj={kernelName:Wo,backendName:"webgl",kernelFunc:hw};function Ed(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Vn({inputs:{x:r.complexTensorInfos.real},backend:n})}var sj={kernelName:uc,backendName:"webgl",kernelFunc:Ed},ij="return float(int(x));";function oj(e,t){let n=new Gr(e.shape,ij),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function Fy(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Vn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Fy({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=qr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Ed({inputs:{input:r},backend:n}),o=Fy({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Vn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return oj(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=hw({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var lj={kernelName:As,backendName:"webgl",kernelFunc:Fy},fw="return ceil(x);",uj=Ke({opSnippet:fw,packedOpSnippet:fw,cpuKernelImpl:kW}),dj={kernelName:gs,backendName:"webgl",kernelFunc:uj},pj=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},cj=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function hj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;J().getBool("WEBGL_PACK_CLIP")?o=new cj(r.shape):o=new pj(r.shape);let u=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,u)}var fj={kernelName:Fr,backendName:"webgl",kernelFunc:hj},mj=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 mw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function yj(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new mj(a.shape),i=[mw(a,r.complexTensorInfos.real),mw(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var Aj={kernelName:Cu,backendName:"webgl",kernelFunc:yj},gj=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},xj=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ut(a),s=fn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let u=i[t],l=i.slice(-2),d=i.join(),p=`if (${u} < ${o[0]}) {
return getChannel(
getT0(${d}), vec2(${l.join()}));
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
if (${u} < ${o[m]} && ${u} >= ${o[m-1]}) {
return getChannel(
getT${m}(${Dh(i,u,f)}),
vec2(${Dh(l,u,f)}));
}`}let c=o.length,h=o[o.length-1];p+=`
return getChannel(
getT${c}(${Dh(i,u,h)}),
vec2(${Dh(l,u,h)}));`,this.userCode=`
float getValue(${i.map(m=>"int "+m)}) {
${p}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[a-1]} = ${s[a-1]} + 1;
if (${s[a-1]} < ${n[a-1]}) {
result.g = getValue(${s});
}
${s[a-2]} = ${s[a-2]} + 1;
if (${s[a-2]} < ${n[a-2]}) {
result.a = getValue(${s});
}
${s[a-1]} = ${s[a-1]} - 1;
if (${s[a-2]} < ${n[a-2]} &&
${s[a-1]} < ${n[a-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function Dh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function zh(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Vn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var bj={kernelName:nc,backendName:"webgl",kernelFunc:zh};function Hl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>Ed({inputs:{input:f},backend:n})),p=e.map(f=>zh({inputs:{input:f},backend:n})),c=Hl(d,t,n),h=Hl(p,t,n),m=qr({inputs:{real:c,imag:h},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(A=>{let g=k.sizeFromShape(A.shape.slice(t));return Ae({inputs:{x:A},backend:n,attrs:{shape:[-1,g]}})}),p=d.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),c=R.computeOutShape(d.map(A=>A.shape),1),h=d[0].shape[0]===1,m=IW(p,c,a,h),f=R.computeOutShape(e.map(A=>A.shape),t),y=n.makeTensorInfo(f,a,m);return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),p=Hl(e.slice(0,d),t,n),c=Hl(e.slice(d),t,n),h=Hl([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new xj(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=vj(e,t,n),o=new gj(s.map(d=>d.shape)),u=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let l=Ae({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function vj(e,t,n){let a=R.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>Ae({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function yw(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=R.computeOutShape(t.map(l=>l.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>k.sizeFromShape(l.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return R.assertParamsConsistent(u,s),Hl(o,s,n)}var wj={kernelName:go,backendName:"webgl",kernelFunc:yw},Aw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",y=f?1:2,A=f?2:3,g=f?3:1,x="",w="";n&&(a?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,w="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${u});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${g}];
ivec2 xRCCorner =
ivec2(coords[${y}], coords[${A}]) * 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 * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${c}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${f}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${m===1}) {
if (${f}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${m===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${f}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${m===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${f}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${b}
${w}
setOutput(result);
}
`}},kj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${a});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${d}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${c}; wC++) {
int xC = xCCorner + wC * ${l};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${m===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${m===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${m===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},Ij=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:u,dilationWidth:l,dilationHeight:d,dataFormat:p}=n,{left:c,top:h}=o,m=r*a,f=hn(),y=p==="channelsLast",A=y?0:1,g=y?1:2,x="";for(let w=0;w<=1;w++)for(let b=0;b<=1;b++)x+=`
blockIndex = rc.y + ${b};
pos = rc.x + ${w};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${u})) * ${i} - ${h};
d0 = offsetY + ${d} * (pos / ${m});
if(d0 < ${t[A]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${u}.) * ${s}. - ${c}.);
d1 = offsetX + ${l} * (int(mod(float(pos), ${m}.) / ${r}.));
if(d1 < ${t[g]} && d1 >= 0) {
ch = int(mod(float(pos), ${r}.));
if (${y}) {
innerDims = vec2(d1, ch);
result[${w*2+b}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${w*2+b}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${x}
${f.output} = result;
}
`}};function gw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=a.texData.get(e.dataId),d=n.inChannels,p=u[0]*u[1]*u[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,y,A=[],g=(p===1||c===1)&&d>ow,x=u[2]%2!=0&&!!l.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let w=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],b=Ae({inputs:{x:e},backend:a,attrs:{shape:[1,w,n.inChannels]}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Fh({a:b,b:v,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});y=Ae({inputs:{x:N},backend:a,attrs:{shape:n.outShape}}),A.push(b),A.push(v),A.push(N)}else{let w=h?u[0]*u[1]*(u[2]+1):u[0]*u[2]*(u[3]+1),b={dataId:e.dataId,shape:[1,w,n.inChannels],dtype:e.dtype},v=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,k.assert(kd(l.shape,b.shape),()=>`packed reshape ${l.shape} to ${b.shape} isn't free`);let N=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(N);let I=Fh({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=a.texData.get(I.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=v,E.shape=n.outShape,y=Vn({inputs:{x:I},backend:a}),y.shape=n.outShape,A.push(I)}for(let w of A)a.disposeIntermediateTensorInfo(w);return y}function xw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=u*l*d,y=c*p,A=[f,y],g=!0,x=!1,w=[],b=Ae({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),v=Ae({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});w.push(b),w.push(v);let N=new Ij(A,b.shape,n),I=a.runWebGLProgram(N,[b],"float32"),E=Ae({inputs:{x:I},backend:a,attrs:{shape:[1,A[0],A[1]]}});w.push(I),w.push(E);let $=r!=null,O=s!=null,z=o==="leakyrelu",P=o?Ch(o,!0):null,D=new nw(E.shape,v.shape,[1,y,n.outChannels],g,x,$,P,O,z),U=[E,v];if(r&&U.push(r),O&&U.push(s),z){let Y=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));U.push(Y),w.push(Y)}let X=a.runWebGLProgram(D,U,"float32"),G=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],ee=Ae({inputs:{x:X},backend:a,attrs:{shape:G}});w.push(X);for(let Y of w)a.disposeIntermediateTensorInfo(Y);return ee}function Sj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(u),c=R.computeConv2DInfo(r.shape,s.shape,i,l,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=gw({x:r,filter:s,convInfo:c,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=xw({x:r,filter:s,convInfo:c,backend:n});else{let f=new Aw(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=Ae({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var Nj={kernelName:xs,backendName:"webgl",kernelFunc:Sj},Tj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Ej=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=s?1:2,l=s?2:3,d=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${d}];
ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},Cj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${a} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Rj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=a-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${u}, ${l});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${a} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function Mj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:d}=a,p=R.convertConv2DDataFormat(u),c=R.computeConv2DInfo(r.shape,d,i,1,o,l,!1,p),h=new Tj(c);return n.runWebGLProgram(h,[r,s],"float32")}var Fj={kernelName:jp,backendName:"webgl",kernelFunc:Mj};function $j(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:d}=a,p=R.convertConv2DDataFormat(l),c=R.computeConv2DInfo(i,s.shape,o,1,u,d,!1,p),h=new Ej(c);return n.runWebGLProgram(h,[r,s],"float32")}var Dj={kernelName:bs,backendName:"webgl",kernelFunc:$j};function zj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a,l=R.computeConv3DInfo(r.shape,s.shape,i,u,o),d=new kj(l);return n.runWebGLProgram(d,[r,s],"float32")}var Oj={kernelName:Ru,backendName:"webgl",kernelFunc:zj};function _j(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:u}=a,l=R.computeConv3DInfo(r.shape,u,i,1,o),d=new Cj(l);return n.runWebGLProgram(d,[r,s],"float32")}var Pj={kernelName:Up,backendName:"webgl",kernelFunc:_j};function Lj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:u}=a,l=R.computeConv3DInfo(u,s.shape,o,1,i),d=new Rj(l);return n.runWebGLProgram(d,[r,s],"float32")}var Wj={kernelName:Hp,backendName:"webgl",kernelFunc:Lj},Bj=tw+`
return cos(x);
`,Vj=Ke({opSnippet:Bj}),jj={kernelName:vs,backendName:"webgl",kernelFunc:Vj},Uj=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,Hj=Ke({opSnippet:Uj}),Gj={kernelName:xo,backendName:"webgl",kernelFunc:Hj},qj=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,u]=e,[l]=t,[d,p]=n;this.outputShape=[l,d,p,u];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,y,A]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[g,x,w]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
const float height_ratio = float(${f});
const float width_ratio = float(${g});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${s}) {
return;
}
float height_scale = ${y};
float width_scale = ${x};
float in_y = ${A};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${w};
if( in_x < 0.0 || in_x > ${m} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${c} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},Xj=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=a,d=new qj(r.shape,s.shape,o,u,l);return n.runWebGLProgram(d,[r,s,i],"float32")},Kj={kernelName:bo,backendName:"webgl",kernelFunc:Xj},bw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${vw(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${ut(a)} coords = getOutputCoords();
int end = ${ww(a,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${ww(a,"coords")} = idx;
val += getX(${vw(a,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Zj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,u=r.shape.length,l=R.getAxesPermutation([s],u),d=r;l!=null&&(d=mn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=R.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Vn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new bw(d.shape,!1,o),y=f.getCustomSetupFunc(m),A=h;h=n.runWebGLProgram(f,[h],h.dtype,y),n.disposeIntermediateTensorInfo(A)}if(i){let m=new bw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(l!=null){let m=R.getUndoAxesPermutation(l),f=mn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var Yj={kernelName:ws,backendName:"webgl",kernelFunc:Zj};function Jj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(s.dataId),d=Wv(u,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(s),d=wW(u,l,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Qj={kernelName:Gp,backendName:"webgl",kernelFunc:Jj},eU=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 tU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=u*s,c=l*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new eU(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var nU={kernelName:vo,backendName:"webgl",kernelFunc:tU},kw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,u=e.padInfo.left,l=e.strideHeight,d=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,y="",A="";n&&(a?y=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?y=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`
float activation(float x) {
${n}
}
`,A="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${l}, ${d});
const ivec2 pads = ivec2(${o}, ${u});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${f};
int q = d2 - d1 * ${f};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${p};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${m}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${g}
${A}
setOutput(result);
}
`}},Iw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,u=e.padInfo.top,l=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,y=f,A=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)A+=`
vec4 xTexelC${b*2};
int xTexelC${b*2}Ready;
vec4 xC${b};`;for(let b=0;b<m;b++){for(let v=0;v<f;v++)A+=`
xTexelC${v*2} = vec4(0.0);
xTexelC${v*2}Ready = 0;
xC${v} = vec4(0.0);`;A+=`
xR = xRCorner + ${b*c};
if (xR >=0 && xR < ${i}) {
`;for(let v=0;v<(y+1)/2;v++){let N=v*2,I=N*h;if(A+=`
xC = xCCorner + ${I};
`,p===1){if(N<f&&(l%2==1?(A+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
`,h===1&&I>0?A+=`
xC${N} = vec4(xTexelC${I-2}.zw, xTexelC${I}.xy);
`:A+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < ${o}) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
previous.zw = vec2(0.0);
}
xC${N} = vec4(previous.zw, xTexelC${I}.xy);
} else {
xC${N} = vec4(0.0, 0.0, xTexelC${I}.xy);
}
`):A+=`
if (xC >= 0 && xC < ${o} && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xC, d1);
if (xC + 1 >= ${o}) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
xC${N} = xTexelC${I};
`,I+1<f)){let E=l%2==0?k.nearestLargerEven(h):h;h%2==0&&l%2==1||h%2!=0&&l%2!=1?(A+=`
xCOffset = xC + ${l%2} + ${E};
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${I+2}Ready == 0) {
xTexelC${I+2} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${I+2}.zw = vec2(0.0);
}
xTexelC${I+2}Ready = 1;
}
`,h>1&&(A+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xCOffset, d1);
xTexelC${I}Ready = 1;
}
`),A+=`
xC${N+1} = vec4(xTexelC${I}.zw, xTexelC${I+2}.xy);
`):E===1?A+=`
xC${N+1} = xTexelC${I};
`:A+=`
xCOffset = xC + ${E};
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${I+2}Ready == 0) {
xTexelC${I+2} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= ${o}) {
xTexelC${I+2}.zw = vec2(0.0);
}
xTexelC${I+2}Ready = 1;
}
xC${N+1} = xTexelC${I+2};
`}}else I<f&&(l%2==1?(A+=`
xCOffset = xC + 1 - ${p};
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= ${o}) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${I+2}Ready == 0) {
xTexelC${I+2} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= ${o}) {
xTexelC${I+2}.zw = vec2(0.0);
}
xTexelC${I+2}Ready = 1;
}
xC${N} = vec4(xTexelC${I}.zw, xTexelC${I+2}.zw);
`,I+1<f&&(A+=`
final = vec4(0.0);
xCOffset = xC + 1 + ${p};
if(xCOffset >= 0 && xCOffset < ${o}) {
final = getX(batch, xR, xCOffset, d1);
}
xC${N+1} = vec4(xTexelC${I+2}.xy, final.xy);
`)):(A+=`
if(xC >= 0 && xC < ${o} && xTexelC${I}Ready == 0) {
xTexelC${I} = getX(batch, xR, xC, d1);
if (xC + 1 >= ${o}) {
xTexelC${I}.zw = vec2(0.0);
}
xTexelC${I}Ready = 1;
}
xCOffset = xC + ${p};
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${I+2}Ready == 0) {
xTexelC${I+2} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= ${o}) {
xTexelC${I+2}.zw = vec2(0.);
}
xTexelC${I+2}Ready = 1;
}
xC${N} = vec4(
xTexelC${I}.xy, xTexelC${I+2}.xy);
`,I+1<f&&(A+=`
xC${N+1} = vec4(xTexelC${I}.zw, xTexelC${I+2}.zw);
`)));N<f&&(A+=`
wTexel = getW(${b}, ${I}, d1, q);
dotProd += xC${N} * vec4(wTexel.xz, wTexel.xz);
`,I+1<f&&(A+=`
wTexel = getW(${b}, ${I+1}, d1, q);
dotProd += xC${N+1} * vec4(wTexel.xz, wTexel.xz);
`))}A+=`
}
`}let g="",x="";n&&(a?g=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?g=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:g=`vec4 activation(vec4 x) {
${n}
}`,x="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${g}
const ivec2 strides = ivec2(${d}, ${p});
const ivec2 pads = ivec2(${u}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${s};
int q = d2 - d1 * ${s};
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);
${A}
vec4 result = dotProd - vec4(0.000000000000001);
${w}
${x}
setOutput(result);
}
`}};function aU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=a,d=u;d==null&&(d=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(r.shape,s.shape,i,d,o,l,!0),c;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new Iw(p):c=new kw(p),n.runWebGLProgram(c,[r,s],"float32")}var rU={kernelName:ks,backendName:"webgl",kernelFunc:aU},sU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},iU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function oU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:d}=a,p=R.computeConv2DInfo(r.shape,d,i,o,u,l,!0),c=new sU(p);return n.runWebGLProgram(c,[r,s],"float32")}var lU={kernelName:qp,backendName:"webgl",kernelFunc:oU};function uU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:d}=a,p=R.computeConv2DInfo(d,s.shape,i,o,u,l,!0),c=new iU(p);return n.runWebGLProgram(c,[r,s],"float32")}var dU={kernelName:Xp,backendName:"webgl",kernelFunc:uU},pU=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function cU(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=Ae({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new pU(s),u=n.runWebGLProgram(o,[i],i.dtype),l=Ae({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var hU={kernelName:Kp,backendName:"webgl",kernelFunc:cU},fU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:d,left:p}=a;this.userCode=`
const ivec2 strides = ivec2(${r}, ${s});
const ivec2 pads = ivec2(${d}, ${p});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${u};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${l};
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 mU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a,l=R.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",u),d,p=new fU(l);d=n.runWebGLProgram(p,[r,s],"float32");let c=Ae({inputs:{x:d},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(d),c}var yU={kernelName:Mu,backendName:"webgl",kernelFunc:mU};function AU(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:u}=R.decodeEinsumEquation(r,s.length);R.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:d}=R.getEinsumComputePath(o,u),p=d.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let y of d[f]){let{permutationIndices:A,expandDims:g}=R.getEinsumPermutation(h,u[y]),x;R.isIdentityPermutation(A)?x=s[y]:(x=mn({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),m.push(x));let w=x.shape.slice();for(let b=0;b<g.length;++b)w.splice(g[b],0,1);k.arraysEqual(x.shape,w)||(x=Ae({inputs:{x},backend:n,attrs:{shape:w}}),m.push(x)),c===null?c=x:(c=Cy({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(l[f]>=0&&(c=Mh({inputs:{x:c},backend:n,attrs:{axis:l[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var gU={kernelName:Jp,backendName:"webgl",kernelFunc:AU},xU="return (x >= 0.0) ? x : (exp(x) - 1.0);",bU=`
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;
`,vU=Ke({opSnippet:xU,packedOpSnippet:bU}),wU={kernelName:wo,backendName:"webgl",kernelFunc:vU},kU="return (b >= 1.0) ? a : a * (b + 1.0);",IU=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,SU=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Sd(IU,a.shape,r.shape):new Ul(kU,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},NU={kernelName:Qp,backendName:"webgl",kernelFunc:SU},TU=`
return vec4(equal(a, b));
`,EU="return float(a == b);",CU=nn({opSnippet:EU,packedOpSnippet:TU,dtype:"bool"}),RU={kernelName:Io,backendName:"webgl",kernelFunc:CU},MU=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${R.ERF_P};
float a1 = ${R.ERF_A1};
float a2 = ${R.ERF_A2};
float a3 = ${R.ERF_A3};
float a4 = ${R.ERF_A4};
float a5 = ${R.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,FU=Ke({opSnippet:MU}),$U={kernelName:ko,backendName:"webgl",kernelFunc:FU},Sw="return exp(x);",Nw=Ke({opSnippet:Sw,packedOpSnippet:Sw,cpuKernelImpl:SW}),DU={kernelName:Ss,backendName:"webgl",kernelFunc:Nw};function $y(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),u=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Ae({inputs:{x:s},backend:a,attrs:{shape:o}})}var zU={kernelName:So,backendName:"webgl",kernelFunc:$y},Tw="return exp(x) - 1.0;",OU=Ke({opSnippet:Tw,packedOpSnippet:Tw,cpuKernelImpl:NW}),_U={kernelName:No,backendName:"webgl",kernelFunc:OU},Ew=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${a});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${a}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function Cw(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),u=o.shape,l=new Ew("real",u,t),d=new Ew("imag",u,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:u},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:u}],c=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=qr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=Ae({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function PU(e){let{inputs:t,backend:n}=e,{input:a}=t;return Cw(a,!1,n)}var LU={kernelName:ec,backendName:"webgl",kernelFunc:PU},WU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Dy(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new WU(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var BU={kernelName:Fu,backendName:"webgl",kernelFunc:Dy},VU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},jU={kernelName:To,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new VU(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},Rw="return floor(x);",UU=Ke({opSnippet:Rw,packedOpSnippet:Rw,cpuKernelImpl:TW}),HU={kernelName:Ns,backendName:"webgl",kernelFunc:UU},GU=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,qU=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,XU=nn({opSnippet:GU,packedOpSnippet:qU,dtype:"int32"}),KU={kernelName:Ts,backendName:"webgl",kernelFunc:XU},ZU=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},YU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,a]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${a}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},JU={kernelName:yc,backendName:"webgl",kernelFunc:QU},Gl;function QU(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[l,u],p=[l,u,s];(o||i)&&(Gl==null&&(Gl=document.createElement("canvas").getContext("2d")),Gl.canvas.width=u,Gl.canvas.height=l,Gl.drawImage(r,0,0,u,l),r=Gl.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=aa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=J().getBool("WEBGL_PACK")?new YU(p):new ZU(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function eH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=R.convertConv2DDataFormat(d),y=R.computeConv2DInfo(r.shape,s.shape,u,p,l,c,!1,f),A,g=[];if(y.filterHeight===1&&y.filterWidth===1&&y.dilationHeight===1&&y.dilationWidth===1&&y.strideHeight===1&&y.strideWidth===1&&(y.padInfo.type==="SAME"||y.padInfo.type==="VALID"))A=gw({x:r,filter:s,convInfo:y,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(J().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=xw({x:r,filter:s,convInfo:y,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let w=i!=null,b=o!=null,v=h==="leakyrelu",N=h?Ch(h,!1):null,I=new Aw(y,w,N,b,v),E=[r,s];if(i&&E.push(i),o&&E.push(o),v){let $=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));E.push($),g.push($)}A=n.runWebGLProgram(I,E,"float32")}let x=Ae({inputs:{x:A},backend:n,attrs:{shape:y.outShape}});return g.push(A),g.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var tH={kernelName:li,backendName:"webgl",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(R.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let y=R.computeConv2DInfo(r.shape,s.shape,u,f,l,p,!0),A=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,g=c?Ch(c,A):null,x=[r,s],w=i!=null,b=o!=null,v=c==="leakyrelu";if(w&&x.push(i),b&&x.push(o),v){let E=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(E),m.push(E)}let N;A?N=new Iw(y,w,g,b,v):N=new kw(y,w,g,b,v);let I=n.runWebGLProgram(N,x,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),I}var aH={kernelName:ui,backendName:"webgl",kernelFunc:nH},rH=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ut(t.length),r=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${a} strides = ${a}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function sH(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],[o,u,l,d]=R.prepareAndValidate(a,r),p=Ae({inputs:{x:r},backend:n,attrs:{shape:[u,i]}}),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/l,l]}}),h=new rH(i,d,[u,l]),m=n.runWebGLProgram(h,[c,p],c.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),f}var iH={kernelName:Co,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=lH(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function lH(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function uH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,u=k.parseAxisParam(i,r.shape)[0],l=R.segment_util.collectGatherOpShapeInfo(r,s,u,o),d=k.sizeFromShape(s.shape),p=[],c=Ae({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Ae({inputs:{x:s},backend:n,attrs:{shape:[l.batchSize,d/l.batchSize]}});p.push(c),p.push(h);let m=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let g=n.bufferSync(h),x=n.bufferSync(c),w=EW(x,g,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(l.outputShape,w.dtype,w.values)}let f=new oH(c.shape,m),y=n.runWebGLProgram(f,[c,h],c.dtype);p.push(y);let A=Ae({inputs:{x:y},backend:n,attrs:{shape:l.outputShape}});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),A}var dH={kernelName:Eo,backendName:"webgl",kernelFunc:uH},pH="return float(a > b);",cH=`
return vec4(greaterThan(a, b));
`,hH=nn({opSnippet:pH,packedOpSnippet:cH,cpuKernelImpl:CW,dtype:"bool"}),fH={kernelName:Ro,backendName:"webgl",kernelFunc:hH},mH="return float(a >= b);",yH=`
return vec4(greaterThanEqual(a, b));
`,AH=nn({opSnippet:mH,packedOpSnippet:yH,dtype:"bool"}),gH={kernelName:Cs,backendName:"webgl",kernelFunc:AH};function xH(e){let{inputs:t,backend:n}=e,{input:a}=t;return Cw(a,!0,n)}var bH={kernelName:tc,backendName:"webgl",kernelFunc:xH},vH="return float(!isnan(x) && !isinf(x));",wH=Ke({opSnippet:vH,dtype:"bool"}),kH={kernelName:Mo,backendName:"webgl",kernelFunc:wH},IH="return float(isinf(x));",SH=Ke({opSnippet:IH,dtype:"bool"}),NH={kernelName:Fo,backendName:"webgl",kernelFunc:SH},TH="return float(isnan(x));",EH=Ke({opSnippet:TH,dtype:"bool"}),CH={kernelName:$o,backendName:"webgl",kernelFunc:EH},RH="return float(a < b);",MH=`
return vec4(lessThan(a, b));
`,FH=nn({opSnippet:RH,packedOpSnippet:MH,cpuKernelImpl:RW,dtype:"bool"}),$H={kernelName:Do,backendName:"webgl",kernelFunc:FH},DH="return float(a <= b);",zH=`
return vec4(lessThanEqual(a, b));
`,OH=nn({opSnippet:DH,packedOpSnippet:zH,dtype:"bool"}),_H={kernelName:zo,backendName:"webgl",kernelFunc:OH};function PH(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=MW(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var LH={kernelName:ac,backendName:"webgl",kernelFunc:PH},WH=`if (x < 0.0) return NAN;
return log(x);`,BH=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,VH=Ke({opSnippet:WH,packedOpSnippet:BH,cpuKernelImpl:FW}),jH={kernelName:Fs,backendName:"webgl",kernelFunc:VH},UH="return log(1.0 + x);",HH=Ke({opSnippet:UH}),GH={kernelName:Oo,backendName:"webgl",kernelFunc:HH},qH="return float(a >= 1.0 && b >= 1.0);",XH=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,KH=nn({opSnippet:qH,packedOpSnippet:XH,dtype:"bool"}),ZH={kernelName:_o,backendName:"webgl",kernelFunc:KH},YH="return float(!(x >= 1.0));",JH=Ke({opSnippet:YH}),QH={kernelName:$u,backendName:"webgl",kernelFunc:JH},eG="return float(a >= 1.0 || b >= 1.0);",tG=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,nG=nn({opSnippet:eG,packedOpSnippet:tG,dtype:"bool"}),aG={kernelName:Du,backendName:"webgl",kernelFunc:nG},rG=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},sG=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},iG=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:u}=a,l=J().getBool("WEBGL_PACK_NORMALIZATION")?new sG(r.shape,s,i,o,u):new rG(r.shape,s,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},oG={kernelName:zu,backendName:"webgl",kernelFunc:iG},lG=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${a}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${a})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},uG=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:d}=a,p=new lG(r.shape,o,u,l,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},dG={kernelName:rc,backendName:"webgl",kernelFunc:uG};function pG(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=$i(i,e.dtype,"max",a),u=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),u}function Mw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=R.getAxesPermutation(l,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let g=n.texData.get(h.dataId).values,x=new Array(o);for(let v=0;v<x.length;v++)x[v]=r.shape[d[v]];let w=Ty(g,r.shape,r.dtype,d,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=w}else h=Rh(r,d,n);l=R.getInnerMostAxes(l.length,o)}R.assertAxesAreInnerMostDims("max",l,o);let[m,f]=R.computeOutAndReduceShapes(h.shape,l),y=m;i&&(y=R.expandShapeToKeepDim(m,u));let A;if(c){let g=n.texData.get(h.dataId).values,x=$W(g,k.sizeFromShape(f),y,r.dtype);A=n.makeTensorInfo(y,r.dtype);let w=n.texData.get(A.dataId);w.values=x}else A=pG(h,f,y,n);return p&&n.disposeIntermediateTensorInfo(h),A}var cG={kernelName:$s,backendName:"webgl",kernelFunc:Mw},hG=Zv+`
return max(a, b);
`,fG=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Eh+`
return result;
`,mG=nn({opSnippet:hG,packedOpSnippet:fG,cpuKernelImpl:DW}),yG={kernelName:Ds,backendName:"webgl",kernelFunc:mG};function AG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;_l(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(R.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=R.computePool2DInfo(r.shape,s,i,l,o,u);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Vn({inputs:{x:r},backend:n});let p=new Nd(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var gG={kernelName:zs,backendName:"webgl",kernelFunc:AG};function xG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=a,d=[1,1,1],p=R.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new Ry(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var bG={kernelName:Ou,backendName:"webgl",kernelFunc:xG},vG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,u=r*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},wG=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=u-1-e.padInfo.top,c=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
const ivec3 pads = ivec3(${d}, ${p}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${o};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${u};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${u} * ${l} +
wR * ${l} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function kG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:u,pad:l,dimRoundingMode:d}=a,p=[1,1,1],c=R.computePool3DInfo(i.shape,o,u,p,l,d),h=new Ry(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new wG(c),y=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var IG={kernelName:ic,backendName:"webgl",kernelFunc:kG};function SG(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;_l([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:d,dimRoundingMode:p}=a,c=R.computePool2DInfo(o.shape,u,l,1,d,p),h=!0,m=new Nd(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),y=new vG(c),A=n.runWebGLProgram(y,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),A}var NG={kernelName:sc,backendName:"webgl",kernelFunc:SG};function TG(e,t,n,a){let r=new Nd(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Nd(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var EG={kernelName:oc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,u=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let l=[1,1];k.assert(R.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let d=R.computePool2DInfo(a.shape,r,s,l,i),[p,c]=TG(a,o,d,u);return[p,c]}};function CG(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=Ae({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=$i(i,"float32","mean",a),u=Ae({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),u}var RG={kernelName:Os,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,u=k.parseAxisParam(s,a.shape),l=u,d=R.getAxesPermutation(l,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,w=new Array(o);for(let N=0;N<w.length;N++)w[N]=a.shape[d[N]];let b=Ty(x,a.shape,a.dtype,d,w);m=i.makeTensorInfo(w,a.dtype);let v=i.texData.get(m.dataId);v.values=b}else m=Rh(a,d,i);h.push(m),l=R.getInnerMostAxes(l.length,o)}R.assertAxesAreInnerMostDims("sum",l,o);let[f,y]=R.computeOutAndReduceShapes(m.shape,l),A=f;r&&(A=R.expandShapeToKeepDim(f,u));let g=CG(m,y,A,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return g}};function MG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=R.getAxesPermutation(l,o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=R.getInnerMostAxes(l.length,r.shape.length)),R.assertAxesAreInnerMostDims("min",l,o);let[c,h]=R.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),y=$i(f,f.dtype,"min",n),A;if(i){let g=R.expandShapeToKeepDim(c,u);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),d!=null&&n.disposeIntermediateTensorInfo(p),A}var FG={kernelName:_s,backendName:"webgl",kernelFunc:MG},$G=Zv+`
return min(a, b);
`,DG=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Eh+`
return result;
`,zG=nn({opSnippet:$G,packedOpSnippet:DG,cpuKernelImpl:zW}),OG={kernelName:Ps,backendName:"webgl",kernelFunc:zG},_G=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,d)=>l[0]+e[d]+l[1]);let a=e.length,r=ut(a),s=t.map(l=>l[0]).join(","),i=t.map((l,d)=>l[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),u=n==="reflect"?0:1;if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${u};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${u};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${a}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${u};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
}
}
${r} coords = outC - start;
setOutput(getX(${o}));
}
`}},PG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=fn("rc",a),u=fn("source",a),l=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${p};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${p};
}
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${d});
${o[a-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${d});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${p}) +
gte * ((end - 1) * 2 - source + ${p});
source -= start;
`;c=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${u.join()}), ${d});
${o[a-1]} += 1;
if(${l}) {
${h}
result[1] = getChannel(getX(${u.join()}), ${d});
}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {
${h}
result[2] = getChannel(getX(${u.join()}), ${d});
${o[a-1]} += 1;
if(${l}) {
${h}
result[3] = getChannel(getX(${u.join()}), ${d});
}
}
`}this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${c}
setOutput(result);
}
`}},LG=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new PG(a.shape,r,s):new _G(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},WG={kernelName:Ls,backendName:"webgl",kernelFunc:LG},BG=`if (b == 0.0) return NAN;
return mod(a, b);`,VG=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Eh+`
return result;
`,jG=nn({opSnippet:BG,packedOpSnippet:VG}),UG={kernelName:Po,backendName:"webgl",kernelFunc:jG},HG=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},GG=`
if (a == b) {
return 1.0;
};
return a / b;`,qG=`
// 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;
`,Fw=nn({opSnippet:GG,packedOpSnippet:qG,checkOutOfBounds:!0}),XG={kernelName:Is,backendName:"webgl",kernelFunc:Fw},$w="return a - b;",Dw=nn({opSnippet:$w,packedOpSnippet:$w,supportsComplex:!0,cpuKernelImpl:HW}),KG={kernelName:ai,backendName:"webgl",kernelFunc:Dw};function zw(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=Mw({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=R.expandShapeToKeepDim(o.shape,i),l=Ae({inputs:{x:o},backend:n,attrs:{shape:u}}),d=Dw({inputs:{a:r,b:l},backend:n}),p=Nw({inputs:{x:d},backend:n}),c=Mh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Ae({inputs:{x:c},backend:n,attrs:{shape:u}}),m=Fw({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var ZG={kernelName:ti,backendName:"webgl",kernelFunc:zw};function YG(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,u=o?r:zw({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],d=u.shape[1],p=new HG(l,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[u],"int32",c);return o||n.disposeIntermediateTensorInfo(u),h}var JG={kernelName:lc,backendName:"webgl",kernelFunc:YG},Ow="return -x;";function QG(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=_W(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Vl(a.shape,Ow):r=new Gr(a.shape,Ow),n.runWebGLProgram(r,[a],a.dtype)}var eq={kernelName:Lo,backendName:"webgl",kernelFunc:QG},tq=Ga.nonMaxSuppressionV3Impl;function nq(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=a,l=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=tq(l,d,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var aq={kernelName:Bo,backendName:"webgl",kernelFunc:nq},rq=Ga.nonMaxSuppressionV4Impl;function sq(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=rq(d,p,i,o,u,l);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var iq={kernelName:Vo,backendName:"webgl",kernelFunc:sq},oq=Ga.nonMaxSuppressionV5Impl;function lq(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=u,f=l,{selectedIndices:y,selectedScores:A}=oq(d,p,c,h,m,f);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var uq={kernelName:jo,backendName:"webgl",kernelFunc:lq},dq=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${a}), float(${n}),
float(index == coords.y)));
}
`}},pq=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,u=k.sizeFromShape(r.shape),l=new dq(u,s,i,o),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=Ae({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},cq={kernelName:Bs,backendName:"webgl",kernelFunc:pq};function Oh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Ed({inputs:{input:a},backend:n}),s=Oh({inputs:{x:r},backend:n}),i=zh({inputs:{input:a},backend:n}),o=Oh({inputs:{x:i},backend:n}),u=qr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Dy({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var hq={kernelName:ol,backendName:"webgl",kernelFunc:Oh};function _w(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Ed({inputs:{input:a},backend:n}),s=_w({inputs:{x:r},backend:n}),i=zh({inputs:{input:a},backend:n}),o=Oh({inputs:{x:i},backend:n}),u=qr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Dy({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var fq={kernelName:Uo,backendName:"webgl",kernelFunc:_w};function mq(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return $y({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let p=$y({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),l=yw({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var yq={kernelName:Ho,backendName:"webgl",kernelFunc:mq},Aq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
int start = ${s};
int end = ${i};
uniform float value;
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${s});
${r} end = ${r}(${i});
uniform float value;
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${o}));
}
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},gq=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=fn("rc",a),u=fn("source",a),l=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
if(${l}) {
`,a===1?"":`}
rc = outputLoc;
${o[a-2]} += 1;
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
if(${l}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
${p[m]}
if (${c}) {
result[${m}] = float(value);
} else {
${r} source = rc - start;
result[${m}] = getChannel(getX(${u.join()}), ${d});
}
`;h+=a===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${s});
const ${r} end = ${r}(${i});
uniform float value;
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},Pw=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gq(r.shape,s,i):new Aq(r.shape,s,i),u=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,u)},xq={kernelName:Vs,backendName:"webgl",kernelFunc:Pw},bq=`
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`,vq=`
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
`+Eh+`
return result;
`,wq=nn({opSnippet:bq,packedOpSnippet:vq}),kq={kernelName:js,backendName:"webgl",kernelFunc:wq};function Iq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=[],l=k.parseAxisParam(s,r.shape),d=l,p=R.getAxesPermutation(d,o),c=r;p!=null&&(c=mn({inputs:{x:r},backend:n,attrs:{perm:p}}),d=R.getInnerMostAxes(d.length,o),u.push(c)),R.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:y,outDtype:A}=PW(c.shape,c.dtype,m,d);h=n.makeTensorInfo(y,A,f)}else{let[m,f]=R.computeOutAndReduceShapes(c.shape,d),y=k.sizeFromShape(f),A=Ae({inputs:{x:c},backend:n,attrs:{shape:[-1,y]}}),g=vc(r.dtype),x=$i(A,g,"prod",n);h=Ae({inputs:{x},backend:n,attrs:{shape:m}}),u.push(A),u.push(x)}if(i){u.push(h);let m=R.expandShapeToKeepDim(h.shape,l);h=Ae({inputs:{x:h},backend:n,attrs:{shape:m}})}return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var Sq={kernelName:Go,backendName:"webgl",kernelFunc:Iq},Lw=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=LW(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},Nq={kernelName:_u,backendName:"webgl",kernelFunc:Lw},Tq="return 1.0 / x;",Eq=Ke({opSnippet:Tq}),Cq={kernelName:qo,backendName:"webgl",kernelFunc:Eq},Rq=Ia+`
return (x < 0.0) ? 0.0 : x;
`,Mq=`
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;
`,Fq=Ke({opSnippet:Rq,packedOpSnippet:Mq}),$q={kernelName:Hs,backendName:"webgl",kernelFunc:Fq},Dq=Ia+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,zq=`
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;
`,Oq=Ke({opSnippet:Dq,packedOpSnippet:zq}),_q={kernelName:qs,backendName:"webgl",kernelFunc:Oq},Pq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/d[0]},
${l[1]/d[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`}},Lq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/d[0]},
${l[1]/d[1]},
${l[1]/d[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${u-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function Wq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Lq(r.shape,u,l,s,i):new Pq(r.shape,u,l,s,i);return n.runWebGLProgram(d,[r],"float32")}var Bq={kernelName:Gs,backendName:"webgl",kernelFunc:Wq},Vq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],u=[n&&s>1?s-1:s,n&&i>1?i-1:i],l=o[0]/u[0],d=o[1]/u[1],p=1/l,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*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(${l});
const float widthScale = float(${d});
const float invHeightScale = float(${p});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${a-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function jq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Vq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Uq={kernelName:pc,backendName:"webgl",kernelFunc:jq},Hq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${l[0]/d[0]},
${l[1]/d[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${c};
// 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);
}
`}},Gq=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${l[0]/d[0]},
${l[1]/d[1]},
${l[1]/d[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${c};
// 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 < ${u-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function qq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Gq(r.shape,u,l,s,i):new Hq(r.shape,u,l,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var Xq={kernelName:Pu,backendName:"webgl",kernelFunc:qq},Kq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],u=[n&&s>1?s-1:s,n&&i>1?i-1:i],l=o[0]/u[0],d=o[1]/u[1],p=1/l,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${l});
const float widthScale = float(${d});
const float invHeightScale = float(${p});
const float invWidthScale = float(${c});
const int winHeight = int(${h});
const int winWidth = int(${m});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${u[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${u[1]}));
int sourceNearestRow = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Zq(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new Kq(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Yq={kernelName:dc,backendName:"webgl",kernelFunc:Zq},Jq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ut(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},Qq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=fn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(a.slice())};
if(${r}){
result.g = ${u(a.slice())};
}
if(${s}) {
result.b = ${l(a.slice())};
if(${r}) {
result.a = ${d(a.slice())};
}
}
setOutput(result);
}
`;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((A,g)=>c(g,h)),f=m.join(","),y=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${y}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function eX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Vn({inputs:{x:r},backend:n});let u=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Qq(r.shape,o):new Jq(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var tX={kernelName:Xs,backendName:"webgl",kernelFunc:eX},nX=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
uniform vec4 params;
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]));
${r}
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},aX={kernelName:ll,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,u=new nX(a.shape,s),[l,d]=R.getImageCenter(i,a.shape[1],a.shape[2]),p=u.getCustomSetupFunc(l,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(u,[a],a.dtype,p)}},rX=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,sX=Ke({opSnippet:rX}),iX={kernelName:Ks,backendName:"webgl",kernelFunc:sX},oX="return inversesqrt(x);",lX=Ke({opSnippet:oX,cpuKernelImpl:WW}),uX={kernelName:Zs,backendName:"webgl",kernelFunc:lX},Ww=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),u=ut(s.length),l="";n===1?l="i":n===2&&(l="i, j");let d=`getIndices(${l})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${r});
void main() {
${u} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${d});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${c};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function dX(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:u,sliceSize:l,strides:d,outputSize:p}=R.calculateShapes(s,r,i),c=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Ae({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),m=Ae({inputs:{x:s},backend:n,attrs:{shape:[u,l]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),y=new Ww(u,o,h.shape.length,m.shape.length,d,c),A=n.runWebGLProgram(y,[m,h,f],m.dtype),g=Ae({inputs:{x:A},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(f),g}var pX={kernelName:Ko,backendName:"webgl",kernelFunc:dX},cX=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);a=o.join(),r=u.join()}let s=ut(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${a});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function hX(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new cX(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],da(r.dtype,s.dtype))}var fX={kernelName:Zo,backendName:"webgl",kernelFunc:hX},mX=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${R.SELU_SCALEALPHA};
float scale = ${R.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,yX=Ke({opSnippet:mX}),AX={kernelName:Yo,backendName:"webgl",kernelFunc:yX},gX="return 1.0 / (1.0 + exp(-1.0 * x));",xX=Ke({opSnippet:gX}),bX={kernelName:Js,backendName:"webgl",kernelFunc:xX},vX=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,wX=Ke({opSnippet:vX}),kX={kernelName:el,backendName:"webgl",kernelFunc:wX},IX=tw+`
return sin(x);
`,SX=Ke({opSnippet:IX}),NX={kernelName:Ys,backendName:"webgl",kernelFunc:SX},TX=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,EX=Ke({opSnippet:TX}),CX={kernelName:Qo,backendName:"webgl",kernelFunc:EX},RX=`
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`,MX=Ke({opSnippet:RX}),FX={kernelName:tl,backendName:"webgl",kernelFunc:MX},$X=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,g)=>A*g),u=[[0,0]];u.push(...i);for(let A=1+s.length;A<r.shape.length;++A)u.push([0,0]);let l=[],d=Pw({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=R.getReshaped(d.shape,s,o,!1),c=R.getPermuted(p.length,s.length,!1),h=R.getReshapedPermuted(d.shape,s,o,!1),m=Ae({inputs:{x:d},backend:n,attrs:{shape:p}}),f=mn({inputs:{x:m},backend:n,attrs:{perm:c}}),y=Ae({inputs:{x:f},backend:n,attrs:{shape:h}});return l.push(d),l.push(m),l.push(f),l.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},DX={kernelName:Lu,backendName:"webgl",kernelFunc:$X};function zX(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(r.dataId),l=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=VW(o,a.shape,a.dtype,u,r.dtype,l,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(y=>Number(y)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var OX={kernelName:cc,backendName:"webgl",kernelFunc:zX};function _X(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),u=Array.from(n.readSync(s.dataId)),[l,d,p]=jW(o,a.shape,a.dtype,i,u);return[n.makeTensorInfo(d,a.dtype,l),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var PX={kernelName:hc,backendName:"webgl",kernelFunc:_X};function LX(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:u,numUpdates:l,strides:d,outputSize:p}=R.calculateShapes(s,r,o),c=!1,h=new Ww(l,u,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=Ae({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var WX={kernelName:fc,backendName:"webgl",kernelFunc:LX};function BX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],u=R.prepareSplitSize(r,s,o),l=r.shape.length,d=new Array(l).fill(0),p=r.shape.slice();return u.map(c=>{let h=[...p];h[o]=c;let m=Td({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var VX={kernelName:nl,backendName:"webgl",kernelFunc:BX},jX="return sqrt(x);",UX=Ke({opSnippet:jX}),HX={kernelName:Qs,backendName:"webgl",kernelFunc:UX},GX="return x * x;",qX=Ke({opSnippet:GX}),XX={kernelName:Wu,backendName:"webgl",kernelFunc:qX},Bw="return (a - b) * (a - b);",KX=nn({opSnippet:Bw,packedOpSnippet:Bw}),ZX={kernelName:ni,backendName:"webgl",kernelFunc:KX};function YX({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Ia+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Gr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var JX={kernelName:Dr,backendName:"webgl",kernelFunc:YX},QX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function eK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:y,newShape:A,outShape:g}=un.sliceInfo(r.shape,s,i,o,u,l,d,p,c),x=Ae({inputs:{x:r},backend:n,attrs:{shape:A}}),w;if(h){let v=Td({inputs:{x},backend:n,attrs:{begin:m,size:y}});w=Ae({inputs:{x:v},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(v)}else if(g.some(v=>v===0))w=n.makeTensorInfo(g,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let v=n.texData.get(x.dataId).values,N=Be(x.shape,x.dtype,v),I=UW(g,N,f,m);w=n.makeTensorInfo(g,x.dtype,I.values)}else{let v=new QX(m,f,g);w=n.runWebGLProgram(v,[x],x.dtype)}let b=Ae({inputs:{x:w},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),b}var tK={kernelName:al,backendName:"webgl",kernelFunc:eK},nK="return tan(x);",aK=Ke({opSnippet:nK}),rK={kernelName:ri,backendName:"webgl",kernelFunc:aK},sK=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,iK=Ke({opSnippet:sK}),oK={kernelName:si,backendName:"webgl",kernelFunc:iK},lK=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=uK(e);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function uK(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function Vw(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),u=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,l=Be(r.shape,r.dtype,u),d=GW(l,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new lK(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var dK={kernelName:$r,backendName:"webgl",kernelFunc:Vw};function pK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[u,l]=qW(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}var cK={kernelName:rl,backendName:"webgl",kernelFunc:pK},hK=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 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 (${o} == 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 < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
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(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 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 fK(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=a,[d,p,c,h]=r.shape,[m,f]=l!=null?l:[p,c],y=[d,m,f,h],A=new hK(p,c,i,o,u,y);return n.runWebGLProgram(A,[r,s],"float32")}var mK={kernelName:sl,backendName:"webgl",kernelFunc:fK};function yK(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;_l(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:u,indices:l}=XW(i,r,s.shape,s.dtype);return[a.makeTensorInfo(u,s.dtype,o),a.makeTensorInfo([l.length],"int32",l)]}var AK={kernelName:mc,backendName:"webgl",kernelFunc:yK};function gK(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[s],l=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(l[d++]=i.shape[f]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(u);for(let f=0;f<m.length;f++){c[s]=f;let y=Td({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),A=Ae({inputs:{x:y},backend:n,attrs:{shape:l}});m[f]=A,p.push(y)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var xK={kernelName:il,backendName:"webgl",kernelFunc:gK},bK=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,d=n%4,p=`
sumValue += dot(values, segFilter);
`,c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${l}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
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 + ${l};
if (${d===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
);
${p}
} else if (${d===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
);
${p}
} else if (${d===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
);
${p}
}
setOutput(${u});
}
`}};function vK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,u=[],l=0,d=R.getAxesPermutation([l],o),p=r;d!=null&&(p=mn({inputs:{x:r},backend:n,attrs:{perm:d}}),u.push(p),l=R.getInnerMostAxes(1,o)[0]);let c=R.segment_util.computeOutShape(p.shape,l,i),h=k.sizeFromShape([p.shape[l]]),m=Ae({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(m);let f=vc(r.dtype),y=(w,b,v,N,I)=>{let E=w.shape[0],$=w.shape[1],O=R.segment_util.segOpComputeOptimalWindowSize($,I),z={windowSize:O,inSize:$,batchSize:E,numSegments:I},P=new bK(z,b),D=n.compileAndRun(P,[w,v],N);if(u.push(D),D.shape[1]===I)return D;let U=Lw({backend:n,attrs:{start:0,stop:I,step:1,dtype:"float32"}}),X=Vw({inputs:{x:U},backend:n,attrs:{reps:[$/O]}});return u.push(U),u.push(X),y(D,b,X,N,I)},A=y(m,"unsortedSegmentSum",s,f,i),g=Ae({inputs:{x:A},backend:n,attrs:{shape:c}}),x=g;if(d!=null){u.push(g);let w=R.getUndoAxesPermutation(d);x=mn({inputs:{x},backend:n,attrs:{perm:w}})}return u.forEach(w=>n.disposeIntermediateTensorInfo(w)),x}var wK={kernelName:Bu,backendName:"webgl",kernelFunc:vK},kK=[oG,dG,GB,XB,YB,eV,nV,sV,oV,uV,hV,mV,gV,vV,EV,IV,MV,zV,$V,LV,BV,jV,qV,ej,nj,lj,dj,fj,Aj,TB,wj,Fj,Dj,Nj,Pj,Wj,Oj,jj,Gj,Kj,Yj,Qj,nU,lU,dU,rU,hU,yU,gU,wU,NU,RU,$U,DU,zU,_U,LU,BU,jU,HU,KU,JU,tH,aH,iH,dH,fH,gH,NB,bH,bj,kH,NH,CH,CB,$H,_H,LH,GH,jH,ZH,QH,aG,cG,bG,gG,IG,NG,EG,yG,RG,FG,OG,WG,UG,JG,DB,eq,aq,iq,uq,rj,cq,fq,yq,xq,kq,MB,Sq,Nq,sj,XG,Cq,_q,$q,OB,Bq,Uq,Xq,Yq,tX,aX,iX,uX,pX,fX,AX,bX,kX,NX,CX,JV,ZG,FX,DX,OX,PX,WX,VX,HX,XX,ZX,JX,tK,KG,jB,rK,oK,dK,cK,mK,UB,AK,xK,wK,hq];for(let e of kK)di(e);var En;(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"})(En||(En={}));var Cd;(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"})(Cd||(Cd={}));var jw;function IK(e){jw=e.wasm.cwrap(oi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function SK(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let I=n.dataIdMap.get(i.dataId);if(I.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${I.shape.length}.`);m=I.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,y=Cd[d];if(y==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let A=u?r.shape[2]:r.shape[1],g=l?s.shape[1]:s.shape[2],x=r.shape[0],w=n.makeOutput([x,A,g],r.dtype),b=n.dataIdMap.get(w.dataId).id,v=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return jw(c,v,r.shape.length,h,N,s.shape.length,u,l,y,m,f,p||0,b),w}var NK={kernelName:oi,backendName:"wasm",setupFunc:IK,kernelFunc:SK};function yn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,u=s.makeOutput(i.shape,i.dtype),l=s.dataIdMap.get(u.dataId).id;return k.sizeFromShape(u.shape)===0||t(o,l),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var TK=yn(oo);function An(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:u}=i,{a:l,b:d}=u,p=o.dataIdMap.get(l.dataId).id,c=o.dataIdMap.get(d.dataId).id,h=n!=null?n:l.dtype,m=R.assertAndGetBroadcastShape(l.shape,d.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let y=new Uint8Array(new Int32Array(l.shape).buffer),A=new Uint8Array(new Int32Array(d.shape).buffer),g=o.dataIdMap.get(f.dataId).id,x=()=>a(p,y,l.shape.length,c,A,d.shape.length,En[l.dtype],g);if(t&&l.dtype==="float32")return x(),f;let w=R.getBroadcastDims(l.shape,m),b=R.getBroadcastDims(d.shape,m),v=w.every((I,E)=>I===E),N=b.every((I,E)=>I===E);if(v&&N)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${l.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var EK=!0,CK=An(Mr,EK),Uw;function RK(e){Uw=e.wasm.cwrap(hs,null,["array","number","number","number"])}function MK(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return Uw(s,r.length,En[a.dtype],i),a}var FK={kernelName:hs,backendName:"wasm",setupFunc:RK,kernelFunc:MK};function _h(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var $K={kernelName:Rs,backendName:"wasm",kernelFunc:_h},Hw;function DK(e){Hw=e.wasm.cwrap(ii,null,["number","array","number","number","number","array","number"])}function Ph(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=OK(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=zK(t.x.shape,a.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=_h({inputs:t,backend:n});return m.shape=o,m}let l=n.makeOutput(o,u.dtype),d=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return Hw(d,h,u.shape.length,En[u.dtype],p,c,s.length),l}function zK(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function OK(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var _K={kernelName:ii,backendName:"wasm",kernelFunc:Ph,setupFunc:DK};function Xr(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=R.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let d=new Array(r);for(let c=0;c<d.length;c++)d[c]=a[o[c]];i=R.getInnerMostAxes(i.length,r),u=Ph({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var Gw;function PK(e){Gw=e.wasm.cwrap(po,null,["number, number, number"])}function LK(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Xr(i,r,t);if(c){let g=t.dataIdMap.get(l.dataId).id;u=l,o=g}let h=u.shape.length;R.assertAxesAreInnerMostDims("all",d,h);let[m,f]=R.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(f),A=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let g=t.dataIdMap.get(A.dataId).id;Gw(o,y,g)}if(c&&t.disposeData(l.dataId),s){let g=R.expandShapeToKeepDim(A.shape,p);A.shape=g}return A}var WK={kernelName:po,backendName:"wasm",setupFunc:PK,kernelFunc:LK},qw;function BK(e){qw=e.wasm.cwrap(co,null,["number, number, number"])}function VK(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Xr(i,r,t);if(c){let g=t.dataIdMap.get(l.dataId).id;u=l,o=g}let h=u.shape.length;R.assertAxesAreInnerMostDims("any",d,h);let[m,f]=R.computeOutAndReduceShapes(u.shape,d),y=k.sizeFromShape(f),A=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let g=t.dataIdMap.get(A.dataId).id;qw(o,y,g)}if(c&&t.disposeData(l.dataId),s){let g=R.expandShapeToKeepDim(A.shape,p);A.shape=g}return A}var jK={kernelName:co,backendName:"wasm",setupFunc:BK,kernelFunc:VK},Xw;function UK(e){Xw=e.wasm.cwrap(fs,null,["number","number","number","number","number"])}function HK(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,u=s,{transposed:l,axes:d,inputWasTransposed:p}=Xr(s,r,t);if(p){let A=t.dataIdMap.get(l.dataId).id;A!==i&&(u=l,o=A)}let c=u.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),y=u.shape[d[0]];return Xw(o,En[u.dtype],f,y,m),p&&t.disposeData(l.dataId),h}var GK={kernelName:fs,backendName:"wasm",kernelFunc:HK,setupFunc:UK},Kw;function qK(e){Kw=e.wasm.cwrap(ms,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function XK(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,d=R.computePool2DInfo(r.shape,i,o,1,u,l),p=d.filterHeight,c=d.filterWidth,h=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,y=d.padInfo.left,A=d.strideHeight,g=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let w=a.makeOutput(d.outShape,"float32"),b=a.dataIdMap.get(w.dataId).id;return Kw(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,m,f,y,A,g,x,b),w}var KK={kernelName:ms,backendName:"wasm",setupFunc:qK,kernelFunc:XK};function Sa(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. 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zt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Ra=class{constructor(e,t,n,a,r,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=a,this.callArgs=r,this.outputTensorIndex=i,this.id=m4(),s!=null&&(this.originalName=s4(s),this.name=i4(this.originalName)),this.rank=t.length}},wne=0,n0=class{constructor(e,t){this.callArgs=t,this.id=wne++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},kne=0,Xe=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=kne++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=gr(n)+"_"+Qh(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let a=e.dtype;a==null&&(a=e.inputDType),a==null&&(a="float32"),this.dtype=a}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Ta(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new j(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Cn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Cn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Ar(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Use \`getOutputAt(nodeIndex)\` instead.`);return Cn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=ft(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=ft(this.inputSpec);if(e.length!==t.length)throw new j(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let a=e[n],r=t[n];if(r==null)continue;let s=a.rank;if(r.ndim!=null&&s!==r.ndim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${s}`);if(r.maxNDim!=null&&s>r.maxNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${s}`);if(r.minNDim!=null&&s<r.minNDim)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${s}.`);if(r.dtype!=null&&a.dtype!==r.dtype)throw new j(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${a.dtype}.`);if(r.axes){let i=a.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],d=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(d)===-1)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=a.shape[i];if(o!=null&&u!=null&&o!==u)throw new j(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${a.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=ft(e),a=!0;for(let s of n)if(!(s instanceof Ra)){a=!1;break}let r=!0;for(let s of n)if(s instanceof Ra){r=!1;break}if(a===r)throw new j("Arguments to apply() must be all SymbolicTensors or all Tensors");return _i(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of ft(e))s.push(i.shape);this.build(Cn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let s=this.call(e,t),i=ft(s),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(s=Cn(o),this.activityRegularizer!=null)throw new Oe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=Ine(e),i=this.computeOutputShape(s),o,u=Sne(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,d)=>new Ra(u,l,this,ft(e),t,this.name,d)):o=new Ra(u,i,this,ft(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Oe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,a)=>{n!=null&&e[a]!=null&&e[a]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 Ar(`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 Ar(`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 Ta(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return t0(this.weights)}build(e){this.built=!0}getWeights(e=!1){return lA(e?this.trainableWeights:this.weights)}setWeights(e){V(()=>{let t=this.weights;if(t.length!==e.length)throw new j(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],a=lA(t);for(let r=0;r<a.length;++r){let s=a[r],i=t[r],o=e[r];if(!k.arraysEqual(s.shape,o.shape))throw new j(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}uA(n)})}addWeight(e,t,n,a,r,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new j(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(a=yt("zeros"));let o=a.apply(t,n),u=new A4(o,n,e,s,i);return o.dispose(),r!=null&&this.addLoss(()=>r.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=ft(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,a,r,s,i=null){let o=ft(e);t=ft(t),n=ft(n),a=ft(a),r=e0(r),s=e0(s);let u=[],l=[],d=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),d.push(p.tensorIndex);new n0({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:a,inputShapes:r,outputShapes:s},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function Ine(e){e=ft(e);let t=[];for(let n of e)t.push(n.shape);return Cn(t)}function Sne(e){return"float32"}function g4(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let a=t.inboundNodes[n];if(a.inboundLayers.length===0)return a.inputTensors;{let r=[];for(let s=0;s<a.inboundLayers.length;s++){let i=a.inputTensors[s],o=a.inboundLayers[s],u=a.nodeIndices[s],l=g4(i,o,u);for(let d of l)r.indexOf(d)===-1&&r.push(d)}return r}}}var Kl=class extends Xe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Qh("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new j("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new j("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new j("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let a=new Ra(this.dtype,this.batchInputShape,this,[],{},this.name);a.nodeIndex=0,a.tensorIndex=0,new n0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[a],outputTensors:[a],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new j(`Cannot pass any input to an InputLayer's apply() method. 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function _4(e,t){return aae(e,t,"classWeight")}async function P4(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=V(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());Ie(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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es(e){if(e==null){let t={};return t.className="linear",t.config={},IA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},IA(t)}else return e instanceof Mn?e:IA(e)}function SA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var o8=class extends ae.Serializable{},Ud=class extends o8{constructor(e){super();SA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=$t([1]);return this.hasL1&&(t=se(t,Se(W(this.l1,Lt(e))))),this.hasL2&&(t=se(t,Se(W(this.l2,Pd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ud.className="L1L2";ae.registerClass(Ud);function Nae(e){return SA(e),new Ud({l1:e!=null?e.l1:null,l2:0})}function Tae(e){return SA(e),new Ud({l2:e!=null?e.l2:null,l1:0})}var l8={l1l2:"L1L2"};function dt(e){return Vy(e)}function u8(e,t={}){return $d(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function At(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in l8?l8[e]:e,config:{}};return u8(t)}else return e instanceof o8?e:u8(e)}var NA=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Pe(e);let n=Ha(e);return this.maxValue!=null&&(n=Nn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};NA.className="ReLU";ae.registerClass(NA);var TA=class extends Xe{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=Pe(e);return rd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};TA.className="LeakyReLU";ae.registerClass(TA);var EA=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=yt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=At(e.alphaRegularizer),this.alphaConstraint=jt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new j(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Pe(e),ld(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Nt(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:Vt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};EA.className="PReLU";ae.registerClass(EA);var CA=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Oe(`Non-default alpha value (${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=Pe(e);return wl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};CA.className="ELU";ae.registerClass(CA);var RA=class extends Xe{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=Pe(e);return n.mul(Od(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};RA.className="ThresholdedReLU";ae.registerClass(RA);var MA=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new kA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(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}};MA.className="Softmax";ae.registerClass(MA);function Ql(e,t,n){if(typeof e=="number")return Di(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!qte(r))throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Fa(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function Ja(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+Yr([n-t,0]);else if(a==="same")e=e*t;else throw new j(`Unsupport padding mode: ${a}.`);return e}function FA(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,1]):e))}function d8(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function Eae(e,t,n,a=1,r="valid",s,i=1){return V(()=>{if(s==null&&(s=Na()),Ft(s),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Qe(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Mc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ca(o,n)),o})}function p8(e,t,n,a=[1,1],r="valid",s,i,o=null){return V(()=>{if(s==null&&(s=Na()),Ft(s),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=FA(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ur.conv2d({x:u,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(u=Qe(u,[0,3,1,2])),u})}function Cae(e,t,n,a=[1,1,1],r="valid",s,i){return V(()=>{if(s==null&&(s=Na()),Ft(s),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=d8(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=k1(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ca(o,n)),s==="channelsFirst"&&(o=Qe(o,[0,4,1,2,3])),o})}var $A=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",$A.verifyArgs(t),this.rank=e,Kt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ql(t.kernelSize,e,"kernelSize"),this.strides=Ql(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,sa(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=yt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=jt(t.biasConstraint),this.biasRegularizer=At(t.biasRegularizer),this.activityRegularizer=At(t.activityRegularizer),this.dilationRate=Ql(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`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 j(`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 j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ka("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Uy(e.kernelSize,"number",1,3))throw new j(`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:Qr(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hd=class extends $A{constructor(e,t){super(e,t);this.kernel=null,Hd.verifyArgs(t),this.filters=t.filters,Kt(this.filters,"filters"),this.kernelInitializer=yt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=jt(t.kernelConstraint),this.kernelRegularizer=At(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n,a=this.bias==null?null:this.bias.read(),r=Q6(this.activation.getClassName());if(r!=null&&this.rank===2)n=p8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=Eae(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=p8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Cae(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Fa(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:Vt(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 j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Gd=class extends Hd{constructor(e){super(2,e);Gd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Uy(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Gd.className="Conv2D";ae.registerClass(Gd);var qd=class extends Hd{constructor(e){super(3,e);qd.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 j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};qd.className="Conv3D";ae.registerClass(qd);var DA=class extends Gd{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Pe(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],u=a[i],l=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=Ja(o,p,l,this.padding),m=Ja(u,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,1]));let y=Fc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Qe(y,[0,3,1,2])),this.bias!=null&&(y=Ca(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[a]=Ja(t[a],o,s,this.padding),t[r]=Ja(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};DA.className="Conv2DTranspose";ae.registerClass(DA);var zA=class extends qd{constructor(e){super(e);if(this.inputSpec=[new zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new j("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Pe(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let u=a[o],l=a[s],d=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],y=this.strides[2],A=Ja(u,m,p,this.padding),g=Ja(l,f,c,this.padding),x=Ja(d,y,h,this.padding),w=[r,A,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,4,1]));let b=l3(n,this.kernel.read(),w,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Qe(b,[0,4,1,2,3])),this.bias!==null&&(b=Ca(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=Ja(t[a],l,i,this.padding),t[r]=Ja(t[r],d,o,this.padding),t[s]=Ja(t[s],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};zA.className="Conv3DTranspose";ae.registerClass(zA);var c8=class extends Hd{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("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 j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=At(t.depthwiseRegularizer),this.depthwiseConstraint=jt(t.depthwiseConstraint),this.pointwiseInitializer=yt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=At(t.pointwiseRegularizer),this.pointwiseConstraint=jt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${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 j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Qe(e,[0,2,3,1])),n=B1(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ca(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseConstraint),e.pointwiseConstraint=Vt(this.pointwiseConstraint),e}};c8.className="SeparableConv";var OA=class extends c8{constructor(e){super(2,e)}};OA.className="SeparableConv2D";ae.registerClass(OA);var p0=class extends Hd{constructor(e){super(1,e);p0.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"&&!Uy(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};p0.className="Conv1D";ae.registerClass(p0);var _A=class extends Xe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=jh(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return jh(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=jh(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return jh(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}};_A.className="Cropping2D";ae.registerClass(_A);var PA=class extends Xe{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,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ute(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Pe(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Qe(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Qe(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};PA.className="UpSampling2D";ae.registerClass(PA);function Rae(e,t,n=[1,1],a="valid",r,s){return V(()=>{r==null&&(r=Na()),Ft(r);let i=FA(e,r);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=vl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}var LA=class extends $A{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=yt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=jt(e.depthwiseConstraint),this.depthwiseRegularizer=At(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n=Rae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ca(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Fa(t,this.kernelSize[0],this.padding,this.strides[0]),s=Fa(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=Vt(this.depthwiseRegularizer),e}};LA.className="DepthwiseConv2D";ae.registerClass(LA);function h8(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function f8(e,t,n,a=!1,r,s,i=!1,o=!1){return V(()=>{let u=t.shape.length;if(u<3)throw new j(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Ea(2,u));if(t=Qe(t,l),s!=null)throw new Oe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===u-1&&(r=dn(r,-1)),r=Qe(r,l)),a&&(t=Wn(t,0),r!=null&&(r=Wn(r,0)));let d=[],p,c=n,h=t.shape[0],m=fa(t),f;r!=null&&(f=fa(r));for(let A=0;A<h;++A){let g=m[A],x=V(()=>e(g,c));if(r==null)p=x[0],c=x[1];else{let w=V(()=>{let b=f[A],v=Ln(b).sub(b),N=x[0].mul(b).add(c[0].mul(v)),I=c.map((E,$)=>x[1][$].mul(b).add(E.mul(v)));return{output:N,newStates:I}});p=w.output,c=w.newStates}o&&d.push(p)}let y;return o&&(y=pn(d,1)),[p,y,c]})}var Qa=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new f0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("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 Ea(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){oA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Oe("Constants support is not implemented in RNN yet.");oA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new zt({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Oe("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new zt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Ar("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):Ie(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new j(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Gt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=h8(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new zt({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Ra){let o=[e].concat(s),u=this.inputSpec.concat(i),l=this.inputSpec;this.inputSpec=u;let d=super.apply(o,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Pe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new j(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=f8((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=o[0],l=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?l:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return V(()=>{let t=$t(e.shape);return t=Se(t,[1,2]),t=_d(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Jy(t,[1,n]):t):this.cell.stateSize>1?[Jy(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()===Qa.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=Ma(a,n);return new e(Object.assign(t,{cell:r}))}};Qa.className="RNN";ae.registerClass(Qa);var Xd=class extends Xe{},c0=class extends Xd{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Xl([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xl([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Za(W(e,s),this.kernel.read()):r=Za(e,this.kernel.read()),this.bias!=null&&(r=Ca(r,this.bias.read())),i!=null&&(n=W(n,i));let o=se(r,Za(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qr(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};c0.className="SimpleRNNCell";ae.registerClass(c0);var WA=class extends Qa{constructor(e){e.cell=new c0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};WA.className="SimpleRNN";ae.registerClass(WA);var h0=class extends Xd{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Kt(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Xl([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xl([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=W(e,r[0]));let l=Za(e,this.kernel.read());this.useBias&&(l=Ca(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=W(a,s[0]));let d=this.recurrentKernel.read(),[p,c]=qt(d,[2*this.units,this.units],d.rank-1),h=Za(a,p),[m,f,y]=qt(l,3,l.rank-1),[A,g]=qt(h,2,h.rank-1);i=this.recurrentActivation.apply(se(m,A)),o=this.recurrentActivation.apply(se(f,g));let x=Za(W(o,a),c);u=this.activation.apply(se(y,x));let w=se(W(i,a),W(se(1,It(i)),u));return[w,w]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qr(this.activation),recurrentActivation:Qr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};h0.className="GRUCell";ae.registerClass(h0);var BA=class extends Qa{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 h0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};BA.className="GRU";ae.registerClass(BA);var Kd=class extends Xd{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Kt(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=yt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=At(e.kernelRegularizer),this.recurrentRegularizer=At(e.recurrentRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.kernelConstraint=jt(e.kernelConstraint),this.recurrentConstraint=jt(e.recurrentConstraint),this.biasConstraint=jt(e.biasConstraint),this.dropout=Xl([1,Yr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Xl([1,Yr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends ya{apply(i,o){let u=r.apply([s]),l=new Hh().apply([s]),d=r.apply([s*2]);return u4(u4(u,l),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new j(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,d;0<this.dropout&&this.dropout<1&&(e=W(e,s[0]));let p=Za(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=W(a,i[0])),p=se(p,Za(a,this.recurrentKernel.read())),this.useBias&&(p=Ca(p,this.bias.read()));let[c,h,m,f]=qt(p,4,p.rank-1);o=this.recurrentActivation.apply(c),u=this.recurrentActivation.apply(h),l=se(W(u,r),W(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let y=W(d,this.activation.apply(l));return[y,y,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qr(this.activation),recurrentActivation:Qr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),recurrentConstraint:Vt(this.recurrentConstraint),biasConstraint:Vt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Kd.className="LSTMCell";ae.registerClass(Kd);var VA=class extends Qa{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 Kd(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};VA.className="LSTM";ae.registerClass(VA);var f0=class extends Xd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){oA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{_i(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push(Ma(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return lA(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}uA(t)}};f0.className="StackedRNNCells";ae.registerClass(f0);function ts(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>p4(t(),n),i=()=>Ld(s,t,a);return!r||r<=1?Gt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Gt(o.clone()))}var Mae=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},m8=class extends Qa{constructor(e){if(e.unroll)throw new Oe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Oe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new zt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=$t(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Ar("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`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()):Ie(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Gt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],d=Fa(u,a[0],r,s[0],i[0]),p=Fa(l,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};m8.className="ConvRNN2D";var m0=class extends Kd{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Kt(this.filters,"filters"),this.kernelSize=Ql(n,2,"kernelSize"),this.kernelSize.forEach(o=>Kt(o,"kernelSize")),this.strides=Ql(a||1,2,"strides"),this.strides.forEach(o=>Kt(o,"strides")),this.padding=r||"valid",sa(this.padding),this.dataFormat=s||"channelsLast",Ft(this.dataFormat),this.dilationRate=Ql(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Kt(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ya{apply(d,p){let c=u.apply([l]),h=Pn([l]),m=u.apply([l*2]);return Yy([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,u=(Y,re,ne)=>!re||!re[ne]?Y:W(re[ne],Y),l=u(a,o,0),d=u(a,o,1),p=u(a,o,2),c=u(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=u(r,h,0),f=u(r,h,1),y=u(r,h,2),A=u(r,h,3),g=3,[x,w,b,v]=qt(this.kernel.read(),i,g),[N,I,E,$]=this.useBias?qt(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,x,N,this.padding),d=this.inputConv(d,w,I,this.padding),p=this.inputConv(p,b,E,this.padding),c=this.inputConv(c,v,$,this.padding);let[O,z,P,D]=qt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,O),f=this.recurrentConv(f,z),y=this.recurrentConv(y,P),A=this.recurrentConv(A,D);let U=this.recurrentActivation.apply(se(l,m)),X=this.recurrentActivation.apply(se(d,f)),G=se(W(X,s),W(U,this.activation.apply(se(p,y)))),ee=W(this.recurrentActivation.apply(se(c,A)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Mae(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=pr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ca(r,n,this.dataFormat):r}recurrentConv(e,t){return pr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};m0.className="ConvLSTM2DCell";ae.registerClass(m0);var jA=class extends m8{constructor(e){let t=new m0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};jA.className="ConvLSTM2D";ae.registerClass(jA);var y0=class extends Xe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ld(()=>p4(n,this.rate,r,this.seed),()=>n,a)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};y0.className="Dropout";ae.registerClass(y0);var UA=class extends y0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};UA.className="SpatialDropout1D";ae.registerClass(UA);var HA=class extends Xe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Kt(this.units,"units"),this.activation=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=yt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=yt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=jt(e.kernelConstraint),this.biasConstraint=jt(e.biasConstraint),this.kernelRegularizer=At(e.kernelRegularizer),this.biasRegularizer=At(e.biasRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(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=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=Q6(this.activation.getClassName()),r;return a!=null?r=Za(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Za(n,this.kernel.read()),this.bias!=null&&(r=Ca(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Qr(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:Vt(this.kernelConstraint),biasConstraint:Vt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};HA.className="Dense";ae.registerClass(HA);var GA=class extends Xe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new j(`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],Zr(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return Zte(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};GA.className="Flatten";ae.registerClass(GA);var qA=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=es(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Qr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Activation";ae.registerClass(qA);var XA=class extends Xe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Pe(e),Xte(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};XA.className="RepeatVector";ae.registerClass(XA);var KA=class extends Xe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let u=a[o];if(this.isUnknown(u))if(s===null)s=o;else throw new j("Can only specifiy one unknown dimension.");else r*=u}let i=Zr(e);if(s!==null){if(r===0||i%r!=0)throw new j(n);a[s]=i/r}else if(i!==r)throw new j(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Reshape";ae.registerClass(KA);var ZA=class extends Xe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Ea(1,e.dims.length+1);if(!k.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Qe(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ZA.className="Permute";ae.registerClass(ZA);var YA=class extends Xe{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=Pe(e),a=-1;return Qu(ki(n,this.maskValue),a)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=-1,r=!0,s=Qu(ki(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};YA.className="Masking";ae.registerClass(YA);var JA=class extends Xe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Kt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Kt(this.outputDim,"outputDim"),this.embeddingsInitializer=yt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=At(e.embeddingsRegularizer),this.activityRegularizer=At(e.activityRegularizer),this.embeddingsConstraint=jt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Pe(e),ki(e,Ge(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new j(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Od(n,"int32")),d4(this.embeddings.read(),n.as1D()).reshape(st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:Vt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};JA.className="Embedding";ae.registerClass(JA);var Vi=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Oe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new j("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new j(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Kr(t),t.length>1)throw new j(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&Kr(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=Yr(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=_d(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,d=l[0],p=l.slice(1).concat([d]),c=o.reshape([d].concat(Zr(l.slice(1))));c=Qe(c,[1,0]),c=c.reshape(p),n.push(c),r=!0}else if(u>1){let l=Ea(1,u).concat([0]);n.push(Qe(o,l)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,u=o.length,l=o[u-1],d=[l].concat(o.slice(0,o.length-1));s=Qe(s.reshape([-1,l]),[1,0]).reshape(d)}else if(i>1){let o=[i-1].concat(Ea(0,i-1));s=Qe(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=Kr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an Array");if(!Array.isArray(e))throw new j("`inputs` should be an Array");if(t.length!==e.length)throw new j(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:dn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ca(n,t[a]);return n})}},QA=class extends Vi{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};QA.className="Add";ae.registerClass(QA);var e2=class extends Vi{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=W(t,e[n]);return t})}};e2.className="Multiply";ae.registerClass(e2);var t2=class extends Vi{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return W(1/e.length,t)})}};t2.className="Average";ae.registerClass(t2);var n2=class extends Vi{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ua(t,e[n]);return t})}};n2.className="Maximum";ae.registerClass(n2);var a2=class extends Vi{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Nl(t,e[n]);return t})}};a2.className="Minimum";ae.registerClass(a2);var r2=class extends Vi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new j("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new j("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>Yy(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new j("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new j("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new j("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new j(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(Ln(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(dn(t[s],-1)):a.push(t[s]);let r=lt(a,this.axis);return Cc(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};r2.className="Concatenate";ae.registerClass(r2);function Zd(e,t){for(;e<0;)e+=t;return e}function Fae(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Oe("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Oe("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return V(()=>{let i;if(a>r){i=a-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=t.reshape(t.shape.concat(u))}else if(r>a){i=r-a;let u=[];for(let l=0;l<i;++l)u.push(1);e=e.reshape(e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let u=s[0]!==e.shape.length-1,l=s[1]===t.shape.length-1;o=e.matMul(t,u,l)}if(i>0){let u;a>r?u=a+r-3:u=a-1;let l=[];for(let d=u;d<u+i;++d)l.push(d);o=o.squeeze(l)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var s2=class extends Vi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new j(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new j(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>Zd(r,e[s].shape.length)):a=[Zd(this.axes,t.shape.length),Zd(this.axes,n.shape.length)],this.normalize&&(t=a0(t,a[0]),n=a0(n,a[1])),Fae(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Zd(this.axes,e.length),Zd(this.axes,t.length)],n}computeOutputShape(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};s2.className="Dot";ae.registerClass(s2);var i2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Ld(()=>Uh(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};i2.className="GaussianNoise";ae.registerClass(i2);var o2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Ld(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(Uh(n.shape,1,a))},()=>n,t.training||!1):n})}};o2.className="GaussianDropout";ae.registerClass(o2);var l2=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ld(()=>{let a=Pe(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Vr(Tl(n),this.rate);o=Od(o,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,l=-u*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(u).add(l)},()=>Pe(e),t.training||!1)}return e})}};l2.className="AlphaDropout";ae.registerClass(l2);function Yd(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=t3(e,t,n,a,r,s);else if(e.rank===3)i=n3(e,t,n,a,r,s);else if(e.rank===4)i=a3(e,t,n,a,r,s);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function $ae(e,t,n,a,r=.001){return V(()=>{let s=Bc(e,a),i=s.mean,o=s.variance;return[Yd(e,i,o,n,t,r),i,o]})}function Dae(e,t,n,a,r=.001){return V(()=>{let s=Bc(e,a),i=s.mean,o=s.variance,u=[];for(let h of Ea(0,e.rank))a.indexOf(h)!==-1?u.push(1):u.push(e.shape[h]);let l=i.reshape(u),d=o.reshape(u),p=t==null?null:t.reshape(u),c=n==null?null:n.reshape(u);return[Yd(e,l,d,c,p,r),i,o]})}function zae(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Ea(0,e.rank-1))?$ae(e,t,n,a,r):Dae(e,t,n,a,r)}var u2=class extends Xe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.movingMeanInitializer=yt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=yt(e.movingVarianceInitializer||"ones"),this.betaConstraint=jt(e.betaConstraint),this.gammaConstraint=jt(e.gammaConstraint),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new j(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new zt({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,a=Pe(e),r=a.shape,s=r.length,i=Ea(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let u=Di(1,s);u[o]=r[o];let l=i.slice();l.sort();let d=!k.arraysEqual(l,Ea(0,s).slice(0,s-1)),p=()=>{if(d){let y=this.movingMean.read().reshape(u),A=this.movingVariance.read().reshape(u),g=this.center?this.beta.read().reshape(u):null,x=this.scale?this.gamma.read().reshape(u):null;return Yd(a,y,A,g,x,this.epsilon)}else return Yd(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,m]=zae(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(y,A,g)=>{V(()=>{let x=1-g,w=y.read(),b=w.sub(A).mul(x);y.write(w.sub(b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}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:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:Vt(this.betaConstraint),gammaConstraint:Vt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};u2.className="BatchNormalization";ae.registerClass(u2);var d2=class extends Xe{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=yt(e.betaInitializer||"zeros"),this.gammaInitializer=yt(e.gammaInitializer||"ones"),this.betaRegularizer=At(e.betaRegularizer),this.gammaRegularizer=At(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Kr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),a=n.shape,r=a.length;return V(()=>{let s=!0,{mean:i,variance:o}=Bc(n,this.axis,s),u=Di(1,r);for(let m of this.axis)u[m]=a[m];let l=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(u):m,d=l(this.gamma.read()),p=l(this.beta.read()),c=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(c.push(a[m]),h.push(1)):(c.push(1),h.push(a[m]));return i=i.tile(c),o=o.tile(c),d=d.tile(h),p=p.tile(h),Yd(n,i,o,p,d,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:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};d2.className="LayerNormalization";ae.registerClass(d2);function Oae(e,t,n){return V(()=>{if(e.rank!==4)throw new j(`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 j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Na()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],cr(e,a)})}var p2=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Na():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new j(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 j(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new j(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>Oae(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};p2.className="ZeroPadding2D";ae.registerClass(p2);function A0(e,t,n,a,r,s){return V(()=>{Ft(r),a4(s),sa(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Na()),s==null&&(s="max"),e=FA(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=id(e,t,n,o):i=td(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}function y8(e,t,n,a,r,s){return V(()=>{Ft(r),a4(s),sa(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Na()),s==null&&(s="max"),e=d8(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=z1(e,t,n,o):i=x1(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,4,1,2,3])),i})}var A8=class extends Xe{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 j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Kt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new j(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,sa(this.padding),this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){e=st(e);let t=Fa(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=_d(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ha(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},c2=class extends A8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),sa(a),A0(e,t,n,a,r,"max")}};c2.className="MaxPooling1D";ae.registerClass(c2);var h2=class extends A8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),sa(a),A0(e,t,n,a,r,"avg")}};h2.className="AveragePooling1D";ae.registerClass(h2);var g8=class extends Xe{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 j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),sa(this.padding),this.inputSpec=[new zt({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Fa(t,this.poolSize[0],this.padding,this.strides[0]),n=Fa(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},f2=class extends g8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),sa(a),A0(e,t,n,a,r,"max")}};f2.className="MaxPooling2D";ae.registerClass(f2);var m2=class extends g8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),sa(a),A0(e,t,n,a,r,"avg")}};m2.className="AveragePooling2D";ae.registerClass(m2);var x8=class extends Xe{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 j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Kt(this.poolSize,"poolSize"),Kt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),sa(this.padding),this.inputSpec=[new zt({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Fa(t,this.poolSize[0],this.padding,this.strides[0]),n=Fa(n,this.poolSize[1],this.padding,this.strides[1]),a=Fa(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},y2=class extends x8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),sa(a),y8(e,t,n,a,r,"max")}};y2.className="MaxPooling3D";ae.registerClass(y2);var A2=class extends x8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),sa(a),y8(e,t,n,a,r,"avg")}};A2.className="AveragePooling3D";ae.registerClass(A2);var b8=class extends Xe{constructor(e){super(e);this.inputSpec=[new zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Oe}},g2=class extends b8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Pe(e);return St(n,1)})}};g2.className="GlobalAveragePooling1D";ae.registerClass(g2);var x2=class extends b8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Pe(e);return Tn(n,1)})}};x2.className="GlobalMaxPooling1D";ae.registerClass(x2);var v8=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(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 Oe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},b2=class extends v8{call(e,t){return V(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};b2.className="GlobalAveragePooling2D";ae.registerClass(b2);var v2=class extends v8{call(e,t){return V(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Tn(n,[1,2]):Tn(n,[2,3])})}};v2.className="GlobalMaxPooling2D";ae.registerClass(v2);var w8=class extends Xe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=Ma(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},w2=class extends w8{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new j(`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=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return V(()=>(e=Pe(e),f8((n,a)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};w2.className="TimeDistributed";ae.registerClass(w2);function _ae(e){Oi(jte,"BidirectionalMergeMode",e)}var Pae="concat",k2=class extends w8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ma(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=Ma(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Pae:e.mergeMode,_ae(this.mergeMode),e.weights)throw new Oe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Cn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=h8(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let l=n.map(d=>new zt({shape:d.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(a!=null)throw new Oe("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ra;for(let u of s)if(u instanceof Ra!==o)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(s),l=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=d,p}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Wn(r,1));let 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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];V(()=>{t=H(t,[1,n,r]);for(let o=0;o<e.length;++o){let u=o===0?0:a[o-1],l=[0,u,0],d=[1,e[o],r];s[o]=H(Re(t,l,d),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},Qd=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);ga(t,r.shape,"TensorList shape mismatch: "),Gt(r)}),this.idTensor=we(0),this.maxNumElements=a,Gt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Qd([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);ga(e,this.elementShape,"TensorList shape mismatch: ");let a=Jd(this.elementShape,this.tensors,e);return V(()=>{let r=this.tensors.map(s=>H(s,a));return pn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Jd(this.elementShape,this.tensors,e),a=this.tensors.pop();return ga(a.shape,e,"TensorList shape mismatch: "),H(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(ga(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Gt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);ga(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=Jd(this.elementShape,this.tensors,t);return H(this.tensors[e],a)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);ga(this.elementShape,t.shape,"TensorList shape mismatch: "),Gt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);ga(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=Jd(this.elementShape,this.tensors,n);return e.length===0?pa([],[0].concat(a)):V(()=>{let r=e.map(s=>H(this.tensors[s],a));return pn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);ga(this.elementShape,t,"TensorList shape mismatch: ");let n=Jd(this.elementShape,this.tensors,t);return this.size()===0?pa([],[0].concat(n)):V(()=>{let a=this.tensors.map(r=>H(r,n));return lt(a,0)})}};function Wse(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);ga(r,t,"TensorList shape mismatch: ");let s=fa(e);return new Qd(s,t,a)}function Bse(e,t,n){return new Qd([],e,t,n)}function Vse(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new Qd([],n,e.dtype,a),i=fa(e,0);return t.forEach((o,u)=>{s.setItem(o,i[u])}),s}function jse(e,t,n){let a=0,r=t.map(d=>(a+=d,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=L2(s,n),o=a===0?0:e.size/a,u=V(()=>{let d=[];e=H(e,[1,a,o]);for(let p=0;p<t.length;++p){let c=p===0?0:r[p-1],h=[0,c,0],m=[1,t[p],o];d[p]=H(Re(e,h,m),i)}return e.dispose(),d}),l=new Qd([],n,e.dtype,t.length);for(let d=0;d<u.length;d++)l.setItem(d,u[d]);return l}var Use=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=S("thenBranch",e,t,n),r=S("elseBranch",e,t,n),s=S("cond",e,t,n),i=S("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=S("body",e,t,n),r=S("cond",e,t,n),s=S("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(d=>d.id),u=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let l=s;for(;u[0];){let d=l;l=await n.functionMap[a].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return l}case"LoopCond":{let a=S("pred",e,t,n);return[vr(a)]}case"Switch":{let a=S("pred",e,t,n),r=S("data",e,t,n);return r.kept||(r=vr(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>gn(r,t,n)!==void 0);if(a){let r=gn(a,t,n);return[vr(r)]}return}case"Enter":{let a=S("frameName",e,t,n),r=S("tensor",e,t,n);return n.enterFrame(a),[vr(r)]}case"Exit":{let a=S("tensor",e,t,n);return n.exitFrame(),[vr(a)]}case"NextIteration":{let a=S("tensor",e,t,n);return n.nextIteration(),[vr(a)]}case"TensorArrayV3":{let a=S("size",e,t,n),r=S("dtype",e,t,n),s=S("elementShape",e,t,n),i=S("dynamicSize",e,t,n),o=S("clearAfterRead",e,t,n),u=S("identicalElementShapes",e,t,n),l=S("name",e,t,n),d=new Lse(l,r,a,s,u,i,o);return n.addTensorArray(d),[d.idTensor,we(1)]}case"TensorArrayWriteV3":{let a=S("tensorArrayId",e,t,n),r=S("index",e,t,n),s=S("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=S("tensorArrayId",e,t,n),r=S("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=S("tensorArrayId",e,t,n),r=S("indices",e,t,n),s=S("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=S("tensorArrayId",e,t,n),r=S("indices",e,t,n),s=S("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=S("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=S("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=S("tensorArrayId",e,t,n),r=S("tensor",e,t,n),s=S("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=S("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[we(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=S("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=S("tensorListId",e,t,n),r=S("index",e,t,n),s=S("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=S("tensorListId",e,t,n),r=S("index",e,t,n),s=S("elementShape",e,t,n),i=S("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=S("indices",e,t,n),r=S("tensor",e,t,n),s=S("elementShape",e,t,n),i=S("numElements",e,t,n),o=Vse(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=S("elementShape",e,t,n),r=S("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=S(s,e,t,n),o=Bse(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=S("tensorListId",e,t,n),r=S("indices",e,t,n),s=S("elementShape",e,t,n),i=S("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=S("tensorListId",e,t,n),r=S("elementShape",e,t,n),s=S("elementDType",e,t,n),i=S("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=S("tensor",e,t,n),r=S("elementShape",e,t,n),s=S("elementDType",e,t,n),i=Wse(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=S("tensorListId",e,t,n),r=n.getTensorList(a.id),s=S("dtype",e,t,n),i=S("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=S("tensorListId",e,t,n),r=S("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let a=S("tensorListId",e,t,n),r=S("elementShape",e,t,n),s=S("elementDType",e,t,n);return[n.getTensorList(a.id).popBack(r,s)]}case"TensorListSplit":{let a=S("tensor",e,t,n),r=S("elementShape",e,t,n),s=S("lengths",e,t,n),i=jse(a,s,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function ak(e,t,n){let[a,r]=S("fusedOps",e,t,n),s=a==="biasadd",i=r==="prelu",o=a==="fusedbatchnorm",u=S("numArgs",e,t,n);if(s){if(i&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let l=S("strides",e,t,n),d=b0(e,t,n),p=S("dataFormat",e,t,n).toUpperCase(),c=S("dilations",e,t,n),[h,m]=S("args",e,t,n),f=S("leakyreluAlpha",e,t,n);return{stride:l,pad:d,dataFormat:p,dilations:c,biasArg:h,preluArg:m,activationFunc:r,leakyreluAlpha:f}}var Hse=(e,t,n)=>{switch(e.op){case"Conv1D":{let a=S("stride",e,t,n),r=S("pad",e,t,n),s=S("dataFormat",e,t,n).toUpperCase(),i=S("dilation",e,t,n);return[Mc(S("x",e,t,n),S("filter",e,t,n),a,r,s,i)]}case"Conv2D":{let a=S("strides",e,t,n),r=b0(e,t,n),s=S("dataFormat",e,t,n).toUpperCase(),i=S("dilations",e,t,n);return[pr(S("x",e,t,n),S("filter",e,t,n),[a[1],a[2]],r,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:d}=ak(e,t,n);return[Ur.conv2d({x:S("x",e,t,n),filter:S("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:a,pad:r,dataFormat:s,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:d}=ak(e,t,n);return[Ur.depthwiseConv2d({x:S("x",e,t,n),filter:S("filter",e,t,n),strides:[a[1],a[2]],pad:r,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let a=S("outputShape",e,t,n),r=S("strides",e,t,n),s=b0(e,t,n);return[Fc(S("x",e,t,n),S("filter",e,t,n),a,[r[1],r[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let a=S("strides",e,t,n),r=b0(e,t,n),s=S("dilations",e,t,n),i=S("dataFormat",e,t,n).toUpperCase();return[vl(S("input",e,t,n),S("filter",e,t,n),[a[1],a[2]],r,i,[s[1],s[2]])]}case"Conv3D":{let a=S("strides",e,t,n),r=S("pad",e,t,n),s=S("dataFormat",e,t,n).toUpperCase(),i=S("dilations",e,t,n);return[k1(S("x",e,t,n),S("filter",e,t,n),[a[1],a[2],a[3]],r,s,[i[1],i[2],i[3]])]}case"AvgPool":{let a=S("strides",e,t,n),r=S("pad",e,t,n),s=S("kernelSize",e,t,n);return[td(S("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPool":{let a=S("strides",e,t,n),r=S("pad",e,t,n),s=S("kernelSize",e,t,n);return[id(S("x",e,t,n),[s[1],s[2]],[a[1],a[2]],r)]}case"MaxPoolWithArgmax":{let 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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 ik(e,t,n,a){let r=new Set,s=[],i=null,o=null,u=new Set,l=Object.keys(e).map(c=>jn(c)[0]),d=[];a!=null&&(d=a.map(c=>jn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((ok(c)||pie(c)||cie(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&l.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function oie(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>jn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{a.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{a.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{a.has(d.name)&&s.push(d)});let u=new Set,l=[];for(;s.length>0;){let d=s.pop();u.add(d.name),t[d.name]||l.push(d),d.children.forEach(p=>{!u.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>u.has(c.name))&&s.push(p)})}return l}var lie=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],uie=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],die=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function ok(e){return lie.indexOf(e.op)>=0}function pie(e){return uie.indexOf(e.op)>=0}function cie(e){return die.indexOf(e.op)>=0}var B2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new B2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=ik(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return oie(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[jn(d)[0]]),r=t.map(d=>jn(d)[0]),s=r.map(d=>this.graph.nodes[d]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let u={},l={};return V(()=>{let d=new sk(this.weightMap,u,l,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,y]=jn(m),A=[];A[y]=e[m],p[f]=A});let c=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let y=rk(f,p,d,this._resourceManager);if(k.isPromise(y))throw new Error(`The execution of the op '${f.op}' returned a promise. 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in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=jn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=jn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},hie=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 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t=In.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(In.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=In.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new B2(J8.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=J8.Instance.transformGraph(e.modelInitializer);this.initializer=new B2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=In.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof We)&&!Array.isArray(e))return 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Array(e),n=this.length();for(let a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};V2.INITIAL_CAPACITY=32;function fk(e){return new Tie(e)}function j2(e){return new Eie(e)}function Sie(e,t){return new yk(e,t)}function Nie(e,t=ns.FAIL){return new _ie(e,t)}var Zt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new zie(this,e)}filter(e){return new $ie(this,e)}map(e){return new Die(this,e)}mapAsync(e){return new mk(this,e)}serialMapAsync(e){return new mk(this,e).serial()}flatmap(e){return new Oie(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Fie(this,e,t)}columnMajorBatch(e,t=!0,n=pk){return this.rowMajorBatch(e,t).map(a=>bie(a,n))}concatenate(e,t){return new yk(fk([this,e]),t)}take(e){return e<0||e==null?this:new Mie(this,e)}skip(e){return e<0||e==null?this:new Rie(this,e)}prefetch(e){return new Ak(this,e)}shuffle(e,t){return new Pie(this,e,t)}serial(){return new Cie(this)}},Tie=class extends Zt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:kie(e),done:!1}}},Eie=class extends Zt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},Cie=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Rie=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ie(e.value)}return this.upstream.next()}},Mie=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Fie=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},$ie=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ie(e.value)}}},Die=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=va.getTensorsInContainer(e.value),n=this.transform(e.value),a=va.getTensorsInContainer(n);for(let r of t)va.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},zie=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},mk=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=va.getTensorsInContainer(e.value),n=await this.transform(e.value),a=va.getTensorsInContainer(n);for(let r of t)va.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},U2=class extends Zt{constructor(){super();this.outputQueue=new V2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Oie=class extends U2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=va.getTensorsInContainer(e.value),n=this.transform(e.value),a=va.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)va.isTensorInList(r,a)||r.dispose();return!0}},yk=class extends Zt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ns;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ns||(ns={}));var _ie=class extends Zt{constructor(e,t=ns.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function a(s){return s instanceof Zt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await ck(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ns.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ns.SHORTEST:return{value:null,done:!0};case ns.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Ak=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new hk(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},Pie=class extends Ak{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=gie.alea(n||k.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},tu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Un(async()=>(await n.iterator()).columnMajorBatch(e,t,Bie),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Un(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Un(async()=>(await t.iterator()).filter(a=>V(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Un(async()=>(await t.iterator()).map(n=>V(()=>e(n))),this.size)}mapAsync(e){let t=this;return Un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Un(async()=>{let a=j2(async()=>({value:await t.iterator(),done:!1}));return Sie(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Un(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=Aie.alea(t||k.now().toString());return Un(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Un(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};tu.MAX_BUFFER_SIZE=1e4;function Un(e,t=null){return new class extends tu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Lie(e){return Un(async()=>fk(e),e.length)}function Wie(e){if(!eu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Un(async()=>{let n=await ck(e,a=>{if(a instanceof tu)return{value:a.iterator(),recurse:!1};if(eu(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Nie(n,ns.SHORTEST)},t)}function Bie(e){if(e===null)return null;let t=e[0];return vie(t)?{value:Vie(e),recurse:!1}:{value:null,recurse:!0}}function Vie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof We?pn(e):pa(e)}var gk=class extends tu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},w0='"',ep=Symbol("out"),xk=Symbol("field"),k0=Symbol("quote"),H2=Symbol("quoteafterquote"),bk=Symbol("quoteinquote"),vk=class extends tu{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new gk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&k.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?a[s]=u:n[s]=u}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=ep;for(let i=0;i<r;i++)switch(s){case ep:switch(e.charAt(i)){case w0:a=i+1,s=k0;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=ep;break;default:s=xk,a=i;break}break;case xk:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=ep,a=i+1;break;default:}break;case k0:switch(e.charAt(i)){case w0:s=H2;break;default:}break;case H2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=ep,a=i+1;break;case w0:s=k0;break;default:s=bk;break}break;case bk:switch(e.charAt(i)){case w0:s=k0;break;default:}break;default:}if(s===H2?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},wk=class extends Zt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new wk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),pa(n,t)}},kk=class extends Zt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Mt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=ka([s,r,o,i],[1,4])}else this.cropBox=ka([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new kk(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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ioe=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],ooe=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],loe=[33,133,362,263,1,78,308],Yoe=ioe.map(e=>ap[e]),Joe=ooe.map(e=>ap[e]),Qoe=loe.map(e=>ap[e]);var K2=er.leftEyeLower0,Z2=er.rightEyeLower0,ru={leftBounds:[K2[0],K2[K2.length-1]],rightBounds:[Z2[0],Z2[Z2.length-1]]},E0={count:468,mouth:13,symmetryLine:[13,er.midwayBetweenEyes[0]]},Bk={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},su={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function C0(e,t,n,a){for(let r=0;r<X2.length;r++){let{key:s,indices:i}=X2[r],o=er[`${n}${s}`];if(!a||a.includes(s))for(let u=0;u<i.length;u++){let l=i[u];e[o[u]]=[t[l][0],t[l][1],(t[l][2]+e[o[u]][2])/2]}}}var Y2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=np({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?T0(a,[0,0]):N0,u=a!==0?i.map(p=>[...Ok(p,o),p[2]]):i,l=a!==0?zk(r):N0,d=[...nu({startPoint:n.startPoint,endPoint:n.endPoint}),1];return u.map(p=>[Math.round(p[0]+as(d,l[0])),Math.round(p[1]+as(d,l[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[ru.leftBounds[0]][2],a=t[ru.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=S0(I0(G2([t[a],t[r]]),this.irisEnlarge)),o=np(i),u=je.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&Qn.flags.IS_BROWSER&&(u=je.flipLeftRight(u)),{box:i,boxSize:o,crop:u}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<su.numCoordinates;i++){let o=t[i*3],u=t[i*3+1],l=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],u/this.irisSize*a[1]+n.startPoint[1],l])}return{rawCoords:s,iris:s.slice(su.index)}}getAdjustedIrisCoords(t,n,a){let r=t[er[`${a}EyeUpper0`][su.upperCenter]][2],s=t[er[`${a}EyeLower0`][su.lowerCenter]][2],i=(r+s)/2;return n.map((o,u)=>{let l=i;return u===2?l=r:u===4&&(l=s),[o[0],o[1],l]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks.arraySync(),confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=Mk({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),u=I0(o),l=S0(u),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...l,confidence:p,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=V(()=>this.storedBoxes.map((i,o)=>{let u,l=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&Qn.flags.IS_BROWSER){let[x,w]=i.landmarks.length>=E0.count?E0.symmetryLine:Bk.symmetryLine;l=q2(i.landmarks[x],i.landmarks[w]);let b=nu({startPoint:i.startPoint,endPoint:i.endPoint}),v=[b[0]/t.shape[2],b[1]/t.shape[1]],N=je.rotateWithOffset(t,l,0,v);d=T0(-l,b),n.face.mesh.enabled?u=au({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255):u=au({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.boxSize,this.boxSize]).div(255)}else{d=N0;let x=t.clone();n.face.mesh.enabled?u=au({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):u=au({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:u};let[,p,c]=this.meshDetector.execute(u),h=p.dataSync()[0];if(h<n.face.detector.minConfidence)return this.storedBoxes[o].confidence=h,null;let f=H(c,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:x,boxSize:w,crop:b}=this.getEyeBox(f,u,ru.leftBounds[0],ru.leftBounds[1],!0),{box:v,boxSize:N,crop:I}=this.getEyeBox(f,u,ru.rightBounds[0],ru.rightBounds[1]),$=this.irisModel.predict(lt([b,I])).dataSync(),O=$.slice(0,su.numCoordinates*3),{rawCoords:z,iris:P}=this.getEyeCoords(O,x,w,!0),D=$.slice(su.numCoordinates*3),{rawCoords:U,iris:X}=this.getEyeCoords(D,v,N),G=this.getLeftToRightEyeDepthDifference(f);Math.abs(G)<30?(C0(f,z,"left",null),C0(f,U,"right",null)):G<1?C0(f,z,"left",["EyeUpper0","EyeLower0"]):C0(f,U,"right",["EyeUpper0","EyeLower0"]);let ee=this.getAdjustedIrisCoords(f,P,"left"),Y=this.getAdjustedIrisCoords(f,X,"right");f=f.concat(ee).concat(Y)}let y=this.transformRawCoords(f,i,l,d),A=i.confidence;if(i=I0(G2(y),1.5),i.confidence=A,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&Qn.flags.IS_BROWSER){let[x,w]=i.landmarks.length>=E0.count?E0.symmetryLine:Bk.symmetryLine;l=q2(i.landmarks[x],i.landmarks[w]);let b=nu({startPoint:i.startPoint,endPoint:i.endPoint}),v=[b[0]/t.shape[2],b[1]/t.shape[1]],N=je.rotateWithOffset(t.toFloat(),l,0,v);d=T0(-l,b),u=au({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255)}let g={mesh:y,box:i,faceConfidence:h,boxConfidence:i.confidence,image:u};return this.storedBoxes[o]={...S0(i),confidence:i.confidence,faceConfidence:h},g}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Ct=[null,null,null],J2;async function Q2(e,t){let n=await J2.predict(e,t),a=[],r=0;for(let s of n||[]){if(!s||s.isDisposedInternal)continue;let i=s.mesh.map(d=>[d[0]/e.shape[2],d[1]/e.shape[1],d[2]/J2.meshSize]),o={};if(s.mesh&&s.mesh.length>0)for(let d of Object.keys(er))o[d]=er[d].map(p=>s.mesh[p]);let u=s.box?[Math.trunc(Math.max(0,s.box.startPoint[0])),Math.trunc(Math.max(0,s.box.startPoint[1])),Math.trunc(Math.min(e.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0])),Math.trunc(Math.min(e.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1]))]:[0,0,0,0],l=s.box?[s.box.startPoint[0]/e.shape[2],s.box.startPoint[1]/e.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/e.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/e.shape[1]]:[0,0,0,0];a.push({id:r++,score:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxScore:Math.round(100*s.boxConfidence)/100,faceScore:Math.round(100*s.faceConfidence)/100,box:u,boxRaw:l,mesh:s.mesh,meshRaw:i,annotations:o,image:s.image,tensor:s.image}),s.coords&&s.coords.dispose()}return a}async function eg(e){return!Ct[0]&&e.face.enabled||!Ct[1]&&e.face.mesh.enabled||!Ct[2]&&e.face.iris.enabled?(Ct=await Promise.all([!Ct[0]&&e.face.enabled?Wk(e):null,!Ct[1]&&e.face.mesh.enabled?gt(vt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Ct[2]&&e.face.iris.enabled?gt(vt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Ct[1]||!Ct[1].modelUrl?de("load model failed:",e.face.mesh.modelPath):e.debug&&de("load model:",Ct[1].modelUrl)),e.face.iris.enabled&&(!Ct[2]||!Ct[2].modelUrl?de("load model failed:",e.face.iris.modelPath):e.debug&&de("load model:",Ct[2].modelUrl))):e.debug&&(Ct[0]&&de("cached model:",Ct[0].model.modelUrl),Ct[1]&&de("cached model:",Ct[1].modelUrl),Ct[2]&&de("cached model:",Ct[2].modelUrl)),J2=new Y2(Ct[0],Ct[1],Ct[2]),Ct}var Vk=ji,jk=ap;var sg={};xa(sg,{load:()=>rg,predict:()=>M0});var uoe=["angry","disgust","fear","happy","sad","surprise","neutral"],Da,R0=[],Uk=0,ng=Number.MAX_SAFE_INTEGER,ag=[.2989,.587,.114];async function rg(e){return Da?e.debug&&de("cached model:",Da.modelUrl):(Da=await gt(vt(e.modelBasePath,e.face.emotion.modelPath)),!Da||!Da.modelUrl?de("load model failed:",e.face.emotion.modelPath):e.debug&&de("load model:",Da.modelUrl)),Da}async function M0(e,t,n,a){return Da?ng<t.face.emotion.skipFrames&&t.skipFrame&&Uk===a&&R0[n]&&R0[n].length>0?(ng++,R0[n]):(ng=0,new Promise(async r=>{let s=je.resizeBilinear(e,[Da.inputs[0].shape[2],Da.inputs[0].shape[1]],!1),[i,o,u]=qt(s,3,3);s.dispose();let l=W(i,ag[0]),d=W(o,ag[1]),p=W(u,ag[2]);i.dispose(),o.dispose(),u.dispose();let c=Ec([l,d,p]);l.dispose(),d.dispose(),p.dispose();let h=V(()=>c.sub(.5).mul(2));c.dispose();let m=[];if(t.face.emotion.enabled){let f=await Da.predict(h),y=f.dataSync();Ie(f);for(let A=0;A<y.length;A++)y[A]>t.face.emotion.minConfidence&&m.push({score:Math.min(.99,Math.trunc(100*y[A])/100),emotion:uoe[A]});m.sort((A,g)=>g.score-A.score)}h.dispose(),R0[n]=m,Uk=a,r(m)})):null}var dg={};xa(dg,{enhance:()=>ug,load:()=>og,match:()=>Gk,predict:()=>$0,similarity:()=>lg});var za,F0=[],Hk=0,ig=Number.MAX_SAFE_INTEGER;async function og(e){let t=vt(e.modelBasePath,e.face.description.modelPath);return za?e.debug&&de("cached model:",t):(za=await gt(t),za?e.debug&&de("load model:",t):de("load model failed:",e.face.description.modelPath)),za}function lg(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function Gk(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=lg(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function ug(e){return V(()=>{let n=e.image||e.tensor||e;if(!(n instanceof We))return null;let a=[[.05,.15,.85,.85]];return za.inputs[0].shape?(n.shape.length===3?je.cropAndResize(dn(n,0),a,[0],[za.inputs[0].shape[2],za.inputs[0].shape[1]]):je.cropAndResize(n,a,[0],[za.inputs[0].shape[2],za.inputs[0].shape[1]])).mul(255):null})}async function $0(e,t,n,a){var r,s;return za?ig<t.face.description.skipFrames&&t.skipFrame&&Hk===a&&((r=F0[n])==null?void 0:r.age)&&((s=F0[n])==null?void 0:s.age)>0?(ig++,F0[n]):(ig=0,new Promise(async i=>{let o=ug(e),u,l={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(u=await za.predict(o)),Ie(o),u&&(V(()=>{let d=u.find(f=>f.shape[1]===1).dataSync(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(l.gender=d[0]<=.5?"female":"male",l.genderScore=Math.min(.99,p));let c=u.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],h=u.find(f=>f.shape[1]===100).dataSync();l.age=Math.round(h[c-1]>h[c+1]?10*c-100*h[c-1]:10*c+100*h[c+1])/10;let m=u.find(f=>f.shape[1]===1024);l.descriptor=[...m.dataSync()]}),u.forEach(d=>Ie(d))),F0[n]=l,Hk=a,i(l)})):null}var doe=(e,t)=>{let n=(c,h)=>Math.atan2(c[1]-h[1],c[0]-h[0]),a=[0,-.1],r=1,s=e[33][2]>e[263][2],i=s?e[473]:e[468],o=s?[(e[133][0]+e[33][0])/2,(e[133][1]+e[33][1])/2]:[(e[263][0]+e[362][0])/2,(e[263][1]+e[362][1])/2],u=s?[e[133][0]-e[33][0],e[23][1]-e[27][1]]:[e[263][0]-e[362][0],e[253][1]-e[257][1]],l=[(o[0]-i[0])/u[0]-a[0],r*(i[1]-o[1])/u[1]-a[1]],d=Math.sqrt(l[0]**2+l[1]**2);return d=Math.min(d,t[2]/2,t[3]/2),{bearing:(n([0,0],l)+Math.PI/2)%Math.PI,strength:d}},poe=(e,t)=>{let n=y=>{let A=Math.sqrt(y[0]*y[0]+y[1]*y[1]+y[2]*y[2]);return y[0]/=A,y[1]/=A,y[2]/=A,y},a=(y,A)=>{let g=y[0]-A[0],x=y[1]-A[1],w=y[2]-A[2];return[g,x,w]},r=(y,A)=>{let g=y[1]*A[2]-y[2]*A[1],x=y[2]*A[0]-y[0]*A[2],w=y[0]*A[1]-y[1]*A[0];return[g,x,w]},s=y=>{let[A,g,x,w,b,v,N,I,E]=y,$,O,z;return w<1?w>-1?(z=Math.asin(w),O=Math.atan2(-N,A),$=Math.atan2(-v,b)):(z=-Math.PI/2,O=-Math.atan2(I,E),$=0):(z=Math.PI/2,O=Math.atan2(I,E),$=0),{pitch:2*-$,yaw:2*-O,roll:2*-z}},i=y=>{let A=(x,w,b,v)=>Math.atan2(v-w,b-x);return{pitch:A(y[10][1],y[10][2],y[152][1],y[152][2]),yaw:A(y[33][0],y[33][2],y[263][0],y[263][2]),roll:A(y[33][0],y[33][1],y[263][0],y[263][1])}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let u=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(y=>[y[0]*t[0]/u,y[1]*t[1]/u,y[2]]),d=n(a(l[1],l[0])),p=n(a(l[3],l[2])),c=n(r(p,d));p=r(d,c);let h=[p[0],p[1],p[2],d[0],d[1],d[2],c[0],c[1],c[2]],m=s(h),f=o.length===478?doe(o,e.box):{bearing:0,strength:0};return{angle:m,matrix:h,gaze:f}},pg=async(e,t)=>{var d,p,c,h,m,f;let n,a,r,s,i,o,u=[];e.state="run:face",n=Je();let l=await Q2(t,e.config);if(e.performance.face=Math.trunc(Je()-n),!t.shape||t.shape.length!==4)return[];if(!l)return[];for(let y=0;y<l.length;y++){if(e.analyze("Get Face"),!l[y].image||l[y].image.isDisposedInternal){de("Face object is disposed:",l[y].image);continue}let A=poe(l[y],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?M0(l[y].image,e.config,y,l.length):{}:(e.state="run:emotion",n=Je(),s=e.config.face.emotion.enabled?await M0(l[y].image,e.config,y,l.length):{},e.performance.emotion=Math.trunc(Je()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?$0(l[y],e.config,y,l.length):[]:(e.state="run:description",n=Je(),o=e.config.face.description.enabled?await $0(l[y].image,e.config,y,l.length):[],e.performance.embedding=Math.trunc(Je()-n)),e.analyze("End Description:"),e.config.async&&([a,r,s,i,o]=await Promise.all([a,r,s,i,o])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=l[y])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((h=(c=l[y])==null?void 0:c.annotations)==null?void 0:h.rightEyeIris)&&(delete l[y].annotations.leftEyeIris,delete l[y].annotations.rightEyeIris);let g=((m=l[y].annotations)==null?void 0:m.leftEyeIris)&&((f=l[y].annotations)==null?void 0:f.rightEyeIris)?Math.max(Math.abs(l[y].annotations.leftEyeIris[3][0]-l[y].annotations.leftEyeIris[1][0]),Math.abs(l[y].annotations.rightEyeIris[4][1]-l[y].annotations.rightEyeIris[2][1]))/t.shape[2]:0;u.push({...l[y],id:y,age:o.age,gender:o.gender,genderScore:o.genderScore,embedding:o.descriptor,emotion:s,iris:g!==0?Math.trunc(500/g/11.7)/100:0,rotation:A,tensor:e.config.face.detector.return?ha(l[y].image):null}),Ie(l[y].image),l[y].image&&delete l[y].image,e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var xg={};xa(xg,{load:()=>gg,predict:()=>Ag});var rp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],qk=rp.length,sp=rp.reduce((e,t,n)=>(e[t]=n,e),{}),coe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],hoe=coe.map(([e,t])=>[sp[e],sp[t]]),Xk=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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je.nonMaxSuppressionAsync(u,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=l.arraySync();s.dispose(),l.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(u,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=V(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),u.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=V(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let u of i){let l=u.box.dataSync(),d=l.slice(0,2),p=l.slice(2,4),c=u.palmLandmarks.arraySync();u.box.dispose(),u.palmLandmarks.dispose(),o.push(n9({startPoint:d,endPoint:p,palmLandmarks:c,confidence:u.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function boe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function r9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return boe(n)}var s9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function rs(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function voe(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function i9(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(rs(e[r],voe(t,s)))}return n}function vg(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=s9(t[0],t[1]),i=i9(s,r),o=s9(-t[0],-t[1]);return i9(i,o)}function o9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-rs(t[0],n),-rs(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function wg(e,t){return[rs(e,t[0]),rs(e,t[1])]}var woe=5,l9=1.65,u9=[0,5,9,13,17,1,2],koe=0,Ioe=2,kg=class{constructor(t,n){var a;this.handDetector=t,this.handPoseModel=n,this.inputSize=(a=this.handPoseModel)==null?void 0:a.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>wg([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return O0(_0(r),woe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=O0(_0(n),l9);a.palmLandmarks=[];for(let r=0;r<u9.length;r++)a.palmLandmarks.push(t[u9[r]].slice(0,2));return a}transformRawCoords(t,n,a,r){let s=z0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),u=vg(a,[0,0]),l=o.map(h=>[...wg(h,u),h[2]]),d=o9(r),p=[...ip(n),1],c=[rs(p,d[0]),rs(p,d[1])];return l.map(h=>[Math.trunc(h[0]+c[0]),Math.trunc(h[1]+c[1]),Math.trunc(h[2])])}async estimateHands(t,n){let a=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(a=!0));let s=[];for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let u=n.hand.rotation?r9(o.palmLandmarks[koe],o.palmLandmarks[Ioe]):0,l=ip(o),d=[l[0]/t.shape[2],l[1]/t.shape[1]],p=n.hand.rotation&&Qn.flags.IS_BROWSER?je.rotateWithOffset(t,u,0,d):t.clone(),c=vg(-u,l),h=a?this.getBoxForPalmLandmarks(o.palmLandmarks,c):o,m=t9(h,p,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),p.dispose();let[y,A]=await this.handPoseModel.predict(f);f.dispose();let g=y.dataSync()[0];if(y.dispose(),g>=n.hand.minConfidence){let x=H(A,[-1,3]),w=x.arraySync();A.dispose(),x.dispose();let b=this.transformRawCoords(w,h,u,c),v=this.getBoxForHandLandmarks(b);this.storedBoxes[i]={...v,confidence:g};let N={landmarks:b,confidence:g,box:{topLeft:v.startPoint,bottomRight:v.endPoint}};s.push(N)}else this.storedBoxes[i]=null;A.dispose()}else{let u=O0(_0(o),l9),l={confidence:o.confidence,box:{topLeft:u.startPoint,bottomRight:u.endPoint}};s.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}};var d9={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},ss,is,p9;async function Ig(e,t){let n=await p9.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let l of Object.keys(d9))s[l]=d9[l].map(d=>n[r].landmarks[d]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],u=[0,0,0,0];if(i&&i.length>0){for(let l of i)l[0]<o[0]&&(o[0]=l[0]),l[1]<o[1]&&(o[1]=l[1]),l[0]>o[2]&&(o[2]=l[0]),l[1]>o[3]&&(o[3]=l[1]);o[2]-=o[0],o[3]-=o[1],u=[o[0]/e.shape[2],o[1]/e.shape[1],o[2]/e.shape[2],o[3]/e.shape[1]]}else o=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2],n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1],n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],u=[n[r].box.topLeft[0]/e.shape[2],n[r].box.topLeft[1]/e.shape[1],(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/e.shape[2],(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/e.shape[1]];a.push({id:r,score:Math.round(100*n[r].confidence)/100,box:o,boxRaw:u,keypoints:i,annotations:s})}return a}async function Sg(e){!ss||!is?([ss,is]=await Promise.all([e.hand.enabled?gt(vt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?gt(vt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!ss||!ss.modelUrl?de("load model failed:",e.hand.detector.modelPath):e.debug&&de("load model:",ss.modelUrl),!is||!is.modelUrl?de("load model failed:",e.hand.skeleton.modelPath):e.debug&&de("load model:",is.modelUrl))):(e.debug&&de("cached model:",ss.modelUrl),e.debug&&de("cached model:",is.modelUrl));let t=new bg(ss);return p9=new kg(t,is),[ss,is]}var Eg={};xa(Eg,{load:()=>P0,predict:()=>Tg});var c9=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],h9=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Fn;async function P0(e){return Fn?e.debug&&de("cached model:",Fn.modelUrl):(Fn=await gt(vt(e.modelBasePath,e.body.modelPath)),Fn.width=parseInt(Fn.signature.inputs["input_1:0"].tensorShape.dim[2].size),Fn.height=parseInt(Fn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!Fn||!Fn.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Fn.modelUrl)),Fn}async function Tg(e,t){var f;if(!Fn)return[];if(!t.body.enabled)return[];let n={width:e.shape[2],height:e.shape[1]},a=je.resizeBilinear(e,[Fn.width,Fn.height],!1),r=fe(a,[255]);a.dispose();let s=await Fn.predict(r),i=((f=s.find(y=>y.size===195||y.size===155))==null?void 0:f.dataSync())||[];s.forEach(y=>y.dispose()),r.dispose();let o=[],u=(i==null?void 0:i.length)===195?c9:h9,l=5;for(let y=0;y<i.length/l;y++)o.push({id:y,part:u[y],position:[Math.trunc(n.width*i[l*y+0]/255),Math.trunc(n.height*i[l*y+1]/255),Math.trunc(i[l*y+2])+0],positionRaw:[i[l*y+0]/255,i[l*y+1]/255,i[l*y+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[l*y+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[l*y+4]))))/100});let d=o.map(y=>y.position[0]),p=o.map(y=>y.position[1]),c=[Math.min(...d),Math.min(...p),Math.max(...d)-Math.min(...d),Math.max(...p)-Math.min(...d)],h=[0,0,0,0],m=o.reduce((y,A)=>A.score>y?A.score:y,0);return[{id:0,score:m,box:c,boxRaw:h,keypoints:o}]}var $n,tr=[],Cg=[0,0,0,0],Rg=[0,0,0,0],L0=0,Mg=Number.MAX_SAFE_INTEGER,Soe=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function f9(e){return $n?e.debug&&de("cached model:",$n.modelUrl):($n=await gt(vt(e.modelBasePath,e.body.modelPath)),!$n||!$n.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",$n.modelUrl)),$n}function Noe(e,t){let[n,a]=e.shape;return V(()=>{let r=(o,u)=>ye(o,W(fe(o,we(u,"int32")),we(u,"int32"))),s=H(e,[a*n]),i=Tn(s,0).dataSync()[0];if(i>t){let o=yi(s,0),u=r(o,n).dataSync()[0],l=fe(o,we(n,"int32")).dataSync()[0];return[u,l,i]}return[0,0,i]})}async function Fg(e,t){return Mg<t.body.skipFrames&&t.skipFrame&&Object.keys(tr).length>0?(Mg++,[{id:0,score:L0,box:Cg,boxRaw:Rg,keypoints:tr}]):(Mg=0,new Promise(async n=>{let a=V(()=>{if(!$n.inputs[0].shape)return null;let l=je.resizeBilinear(e,[$n.inputs[0].shape[2],$n.inputs[0].shape[1]],!1);return W(l,2).sub(1)}),r;if(t.body.enabled&&(r=await $n.predict(a)),a.dispose(),r){tr.length=0;let l=r.squeeze();Ie(r);let d=l.unstack(2);Ie(l);for(let p=0;p<d.length;p++){let[c,h,m]=Noe(d[p],t.body.minConfidence);L0>t.body.minConfidence&&tr.push({score:Math.round(100*m)/100,part:Soe[p],positionRaw:[c/$n.inputs[0].shape[2],h/$n.inputs[0].shape[1]],position:[Math.round(e.shape[2]*c/$n.inputs[0].shape[2]),Math.round(e.shape[1]*h/$n.inputs[0].shape[1])]})}d.forEach(p=>Ie(p))}L0=tr.reduce((l,d)=>d.score>l?d.score:l,0);let s=tr.map(l=>l.position[0]),i=tr.map(l=>l.position[1]);Cg=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=tr.map(l=>l.positionRaw[0]),u=tr.map(l=>l.positionRaw[1]);Rg=[Math.min(...o),Math.min(...u),Math.max(...o)-Math.min(...o),Math.max(...u)-Math.min(...u)],n([{id:0,score:L0,box:Cg,boxRaw:Rg,keypoints:tr}])}))}var Oa,nr=[],$g=[0,0,0,0],Dg=[0,0,0,0],ou=0,zg=Number.MAX_SAFE_INTEGER,Toe=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function Og(e){return Oa?e.debug&&de("cached model:",Oa.modelUrl):(Oa=await gt(vt(e.modelBasePath,e.body.modelPath)),!Oa||!Oa.modelUrl?de("load model failed:",e.body.modelPath):e.debug&&de("load model:",Oa.modelUrl)),Oa}async function _g(e,t){return zg<t.body.skipFrames&&t.skipFrame&&Object.keys(nr).length>0?(zg++,[{id:0,score:ou,box:$g,boxRaw:Dg,keypoints:nr}]):(zg=0,new Promise(async n=>{let a=V(()=>{if(!Oa.inputs[0].shape)return null;let l=je.resizeBilinear(e,[Oa.inputs[0].shape[2],Oa.inputs[0].shape[1]],!1);return me(l,"int32")}),r;if(t.body.enabled&&(r=await Oa.predict(a)),a.dispose(),r){nr.length=0;let l=r.arraySync();Ie(r);let d=l[0][0];for(let p=0;p<d.length;p++)ou=d[p][2],ou>t.body.minConfidence&&nr.push({score:Math.round(100*ou)/100,part:Toe[p],positionRaw:[d[p][1],d[p][0]],position:[Math.round(e.shape[2]*d[p][1]),Math.round(e.shape[1]*d[p][0])]})}ou=nr.reduce((l,d)=>d.score>l?d.score:l,0);let s=nr.map(l=>l.position[0]),i=nr.map(l=>l.position[1]);$g=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=nr.map(l=>l.positionRaw[0]),u=nr.map(l=>l.positionRaw[1]);Dg=[Math.min(...o),Math.min(...u),Math.max(...o)-Math.min(...o),Math.max(...u)-Math.min(...u)],n([{id:0,score:ou,box:$g,boxRaw:Dg,keypoints:nr}])}))}var Vg={};xa(Vg,{load:()=>Wg,predict:()=>Bg});var lu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var qn,Pg=[],Lg=Number.MAX_SAFE_INTEGER,W0=2.5;async function Wg(e){if(qn)e.debug&&de("cached model:",qn.modelUrl);else{qn=await gt(vt(e.modelBasePath,e.object.modelPath));let t=Object.values(qn.modelSignature.inputs);if(qn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!qn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!qn||!qn.modelUrl?de("load model failed:",e.object.modelPath):e.debug&&de("load model:",qn.modelUrl)}return qn}async function Eoe(e,t,n,a){let r=0,s=[];for(let l of[1,2,4])V(()=>{var y,A;let d=l*13,p=(y=e.find(g=>g.shape[1]===d**2&&g.shape[2]===lu.length))==null?void 0:y.squeeze(),c=(A=e.find(g=>g.shape[1]===d**2&&g.shape[2]<lu.length))==null?void 0:A.squeeze(),m=c.reshape([-1,4,c.shape[1]/4]).argMax(2).arraySync(),f=p.arraySync();for(let g=0;g<p.shape[0];g++)for(let x=0;x<p.shape[1];x++){let w=f[g][x];if(w>a.object.minConfidence&&x!==61){let 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r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!r||!s)return{tensor:null,canvas:Ce};let i=r,o=s;if(i>B0&&(i=B0,o=i*s/r),o>B0&&(o=B0,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ce||(Ce==null?void 0:Ce.width)!==i||(Ce==null?void 0:Ce.height)!==o)&&(Ce=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ce==null?void 0:Ce.width)!==i&&(Ce.width=i),(Ce==null?void 0:Ce.height)!==o&&(Ce.height=o));let u=Ce.getContext("2d");if(e instanceof ImageData?u.putImageData(e,0,0):t.filter.flip&&typeof u.translate!="undefined"?(u.translate(r,0),u.scale(-1,1),u.drawImage(e,0,0,r,s,0,0,Ce==null?void 0:Ce.width,Ce==null?void 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p=Math.abs(l.annotations.leftEyeIris[3][0]-l.annotations.leftEyeIris[1][0])/2,c=Math.abs(l.annotations.leftEyeIris[4][1]-l.annotations.leftEyeIris[2][1])/2;r.ellipse(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(l.annotations&&l.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(l.annotations.rightEyeIris[3][0]-l.annotations.rightEyeIris[1][0])/2,c=Math.abs(l.annotations.rightEyeIris[4][1]-l.annotations.rightEyeIris[2][1])/2;r.ellipse(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((i=(s=l.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((u=(o=l.rotation)==null?void 0:o.gaze)==null?void 0:u.bearing)){r.strokeStyle="pink",r.beginPath();let p=[l.annotations.leftEyeIris[0][0]+Math.sin(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[3],l.annotations.leftEyeIris[0][1]+Math.cos(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[2]];r.moveTo(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1]),r.lineTo(p[0],p[1]);let c=[l.annotations.rightEyeIris[0][0]+Math.sin(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[3],l.annotations.rightEyeIris[0][1]+Math.cos(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[2]];r.moveTo(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1]),r.lineTo(c[0],c[1]),r.stroke()}}}}}async function w9(e,t,n){var s;let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let i=0;i<t.length;i++){if(r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(op(r,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints)for(let o=0;o<t[i].keypoints.length;o++)r.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,Kg(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&(r.font=a.font,t[i].keypoints))for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:a.color,r.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4);if(a.drawPolygons&&t[i].keypoints){let o,u=[];u.length=0,o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),lp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),u.length===4&&Zg(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="leftHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftKnee"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftAnkle"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftHeel"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftFoot"),o&&u.push([o.position[0],o.position[1]]),lp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightKnee"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightAnkle"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightHeel"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightFoot"),o&&u.push([o.position[0],o.position[1]]),lp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftElbow"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftWrist"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftPalm"),o&&u.push([o.position[0],o.position[1]]),lp(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightElbow"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightWrist"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightPalm"),o&&u.push([o.position[0],o.position[1]]),lp(r,u,a)}}}}async function k9(e,t,n){let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,op(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText("hand",s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,Kg(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,u)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 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i=`${Math.round(100*s.score)}% ${s.label}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}}async function Moe(e,t,n){let a=zn(os,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s=0;s<t.length;s++)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,op(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels){let i=`person #${s}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,t[s].box[0]+3,1+t[s].box[1]+a.lineHeight,t[s].box[2])),r.fillStyle=a.labelColor,r.fillText(i,t[s].box[0]+2,0+t[s].box[1]+a.lineHeight,t[s].box[2])}r.stroke()}}}async function Foe(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function $oe(e,t,n){let a=Je(),r=zn(os,n);!t||!e||e instanceof HTMLCanvasElement&&(v9(e,t.face,r),w9(e,t.body,r),k9(e,t.hand,r),I9(e,t.object,r),b9(e,t.gesture,r),t.performance.draw=Math.trunc(Je()-a))}function S9(e,t,n,a,r){var o,u,l,d,p,c,h,m,f,y,A,g,x,w,b,v;let s=0,i=[];for(let N of e){let I={id:s++,face:N,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let D of t)N.box[0]>D.box[0]&&N.box[0]<D.box[0]+D.box[2]&&N.box[1]+N.box[3]>D.box[1]&&N.box[1]+N.box[3]<D.box[1]+D.box[3]&&(I.body=D);if(I.body)for(let D of n)D.box[0]+D.box[2]>I.body.box[0]&&D.box[0]+D.box[2]<I.body.box[0]+I.body.box[2]&&D.box[1]+D.box[3]>I.body.box[1]&&D.box[1]+D.box[3]<I.body.box[1]+I.body.box[3]&&I.hands&&(I.hands.left=D),D.box[0]<I.body.box[0]+I.body.box[2]&&D.box[0]>I.body.box[0]&&D.box[1]+D.box[3]>I.body.box[1]&&D.box[1]+D.box[3]<I.body.box[1]+I.body.box[3]&&I.hands&&(I.hands.right=D);for(let D of a)D.face!==void 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N9(e){var a,r,s,i,o,u,l,d,p,c,h,m,f,y,A,g,x,w,b,v,N;let t=1e3/(Date.now()-e.timestamp)/4;if(!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let I=0;I<e.body.length;I++){let E=e.body[I].box.map((z,P)=>((t-1)*$e.body[I].box[P]+z)/t),$=e.body[I].boxRaw.map((z,P)=>((t-1)*$e.body[I].boxRaw[P]+z)/t),O=e.body[I].keypoints.map((z,P)=>({score:z.score,part:z.part,position:[$e.body[I].keypoints[P]?((t-1)*$e.body[I].keypoints[P].position[0]+z.position[0])/t:z.position[0],$e.body[I].keypoints[P]?((t-1)*$e.body[I].keypoints[P].position[1]+z.position[1])/t:z.position[1]],positionRaw:[$e.body[I].keypoints[P]?((t-1)*$e.body[I].keypoints[P].positionRaw[0]+z.positionRaw[0])/t:z.position[0],$e.body[I].keypoints[P]?((t-1)*$e.body[I].keypoints[P].positionRaw[1]+z.positionRaw[1])/t:z.position[1]]}));$e.body[I]={...e.body[I],box:E,boxRaw:$,keypoints:O}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let I=0;I<e.hand.length;I++){let E=e.hand[I].box.map((D,U)=>((t-1)*$e.hand[I].box[U]+D)/t),$=e.hand[I].boxRaw.map((D,U)=>((t-1)*$e.hand[I].boxRaw[U]+D)/t),O=e.hand[I].keypoints.map((D,U)=>D.map((X,G)=>((t-1)*$e.hand[I].keypoints[U][G]+X)/t)),z=Object.keys(e.hand[I].annotations),P={};for(let D of z)P[D]=e.hand[I].annotations[D].map((U,X)=>U.map((G,ee)=>((t-1)*$e.hand[I].annotations[D][X][ee]+G)/t));$e.hand[I]={...e.hand[I],box:E,boxRaw:$,keypoints:O,annotations:P}}if(!$e.face||e.face.length!==$e.face.length)$e.face=JSON.parse(JSON.stringify(e.face));else for(let I=0;I<e.face.length;I++){let E=e.face[I].box.map((z,P)=>((t-1)*$e.face[I].box[P]+z)/t),$=e.face[I].boxRaw.map((z,P)=>((t-1)*$e.face[I].boxRaw[P]+z)/t),O={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};O.matrix=(a=e.face[I].rotation)==null?void 0:a.matrix,O.angle={roll:((t-1)*(((s=(r=$e.face[I].rotation)==null?void 0:r.angle)==null?void 0:s.roll)||0)+(((o=(i=e.face[I].rotation)==null?void 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2Q==`;var T9="2.0.0";var uu,up,dp,Ui,Hi,du,H0,pp,G0,q0,X0,K0,E9=class{constructor(t){Jn(this,uu,void 0);Jn(this,up,void 0);Jn(this,dp,void 0);Jn(this,Ui,void 0);Jn(this,Hi,void 0);Jn(this,du,void 0);this.analyze=(...t)=>{if(!on(this,up))return;let n=this.tf.engine().state.numTensors,a=on(this,uu);ba(this,uu,n);let r=n-a;r!==0&&de(...t,r)};Jn(this,H0,t=>{if(!on(this,dp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof We))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Jn(this,pp,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=Je();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&de("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&de("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&de("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&de(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&de("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&Rk();try{await this.tf.setBackend(this.config.backend)}catch(r){de("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.object.enabled||this.tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(de("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&de(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(Je()-a)}});this.next=t=>N9(t||this.result);Jn(this,G0,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let u=0;u<r.length/3;u++)s+=r[3*u+2];a.dispose();let i=100*(Math.max(s,on(this,Hi))/Math.min(s,on(this,Hi))-1);ba(this,Hi,s);let o=i<Math.max(this.config.cacheSensitivity,on(this,du));return ba(this,du,i>10*this.config.cacheSensitivity?0:i),o});Jn(this,q0,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(j0);break;case"full":n=await t(U0);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});Jn(this,X0,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+j0;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+U0;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));Jn(this,K0,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(j0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(U0)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&de("Warmup tfjs-node not loaded");return a});this.config=zn(p5,t||{}),this.tf=tp,this.draw=Yg,this.version=T9,this.state="idle",ba(this,uu,0),ba(this,up,!1),ba(this,dp,!1),ba(this,Ui,!0),ba(this,du,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null},this.image=n=>Xg(n,this.config),this.classes={facemesh:tg,emotion:sg,faceres:dg,body:this.config.body.modelPath.includes("posenet")?xg:Eg,hand:Ng,nanodet:Vg,centernet:qg},this.faceTriangulation=Vk,this.faceUVMap=jk,this.sysinfo=c5(),ba(this,Hi,1)}similarity(t,n){return lg(t,n)}enhance(t){return ug(t)}match(t,n,a=0){return Gk(t,n,a)}async load(t){this.state="load";let n=Je();t&&(this.config=zn(this.config,t)),on(this,Ui)&&(this.config.debug&&de(`version: ${this.version}`),this.config.debug&&de(`tfjs version: ${this.tf.version_core}`),this.config.debug&&de("platform:",this.sysinfo.platform),this.config.debug&&de("agent:",this.sysinfo.agent),await on(this,pp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&de("configuration:",this.config),this.config.debug&&de("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.movenet,this.models.nanodet,this.models.centernet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?eg(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?rg(this.config):null),this.models.handpose||(this.config.hand.enabled?Sg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?gg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?P0(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?f9(this.config):null),this.models.movenet||(this.config.body.enabled&&this.config.body.modelPath.includes("movenet")?Og(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?Wg(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?Hg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?og(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await eg(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await rg(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await Sg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await gg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await P0(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await P0(this.config)),this.config.body.enabled&&!this.models.movenet&&this.config.body.modelPath.includes("movenet")&&(this.models.movenet=await Og(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await Wg(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await Hg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await og(this.config))),on(this,Ui)&&(this.config.debug&&de("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ba(this,Ui,!1));let a=Math.trunc(Je()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async a=>{this.state="config";let r;this.config=zn(this.config,n),this.state="check";let s=on(this,H0).call(this,t);s&&(de(s,t),a({error:s}));let i=Je();await on(this,pp).call(this),await this.load(),r=Je();let o=Xg(t,this.config);if(!o||!o.tensor){de("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.performance.image=Math.trunc(Je()-r),this.analyze("Get Image:"),r=Je(),this.config.skipFrame=await on(this,G0).call(this,o.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Je()-r),this.analyze("Check Changed:");let u,l,d,p,c;this.config.async?(u=this.config.face.enabled?pg(this,o.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Je(),u=this.config.face.enabled?await pg(this,o.tensor):[],c=Math.trunc(Je()-r),c>0&&(this.performance.face=c)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?Ag(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?Tg(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?Fg(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?_g(o.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Je(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await Ag(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await Tg(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?await Fg(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?await _g(o.tensor,this.config):[]),c=Math.trunc(Je()-r),c>0&&(this.performance.body=c)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?Ig(o.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Je(),d=this.config.hand.enabled?await Ig(o.tensor,this.config):[],c=Math.trunc(Je()-r),c>0&&(this.performance.hand=c)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?Bg(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?Gg(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Je(),this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?await Bg(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?await Gg(o.tensor,this.config):[]),c=Math.trunc(Je()-r),c>0&&(this.performance.object=c)),this.analyze("End Object:"),this.config.async&&([u,l,d,p]=await Promise.all([u,l,d,p]));let h=[];this.config.gesture.enabled&&(r=Je(),h=[...y9(u),...m9(l),...g9(d),...A9(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Je()-r)),this.performance.total=Math.trunc(Je()-i),this.state="idle",this.result={face:u,body:l,hand:d,gesture:h,object:p,performance:this.performance,canvas:o.canvas,timestamp:Date.now(),get persons(){var m;return S9(u,l,d,h,(m=o==null?void 0:o.tensor)==null?void 0:m.shape)}},Ie(o.tensor),a(this.result)})}async warmup(t){let n=Je();if(t&&(this.config=zn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await on(this,q0).call(this):typeof Image!="undefined"?a=await on(this,X0).call(this):a=await on(this,K0).call(this);let r=Je();return this.config.debug&&de("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};uu=new WeakMap,up=new WeakMap,dp=new WeakMap,Ui=new WeakMap,Hi=new WeakMap,du=new WeakMap,H0=new WeakMap,pp=new WeakMap,G0=new WeakMap,q0=new WeakMap,X0=new WeakMap,K0=new WeakMap;return zoe;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
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