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human
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/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Sr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Sr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return vu.print(this,e)}clone(){return this.throwIfDisposed(),vu.clone(this)}toString(e=!1){let t=this.dataSync();return JN(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),vu.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Sr().makeVariable(this,e,t,n)}};Object.defineProperty(Ke,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function re(){return l2("Tensor",()=>Ke)}re();var hd=class extends Ke{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!jr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Sr().disposeTensor(this),this.dataId=e.dataId,Sr().incRef(this,null)}dispose(){Sr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(hd,Symbol.hasInstance,{value:e=>e instanceof Ke&&e.assign!=null&&e.assign instanceof Function});var ar={};Le(ar,{assertTypesMatch:()=>M5,getTensorsInContainer:()=>x2,isTensorInList:()=>a9,makeTypesMatch:()=>Mt});var f2;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(f2||(f2={}));var m2;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(m2||(m2={}));var g2;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(g2||(g2={}));var y2;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(y2||(y2={}));var A2;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(A2||(A2={}));var r9={float32:y2,int32:m2,bool:g2,complex64:A2};function Ln(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return r9[e][t]}function fd(e){return Ln(e,"int32")}function Mt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Ln(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function M5(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function a9(e,t){return t.some(n=>n.id===e.id)}function x2(e){let t=[],n=new Set;return z5(e,t,n),t}function z5(e,t,n){if(e==null)return;if(e instanceof Ke){t.push(e);return}if(!o9(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),z5(a,t,n))}}function o9(e){return Array.isArray(e)||typeof e=="object"}function b2(e){return e.kernelName!=null}var L5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},md=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new L5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Ir(`${e} backend was already registered. 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 qN(this.backendInstance),!0}setupRegisteredKernels(){Zr(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Zr(e).forEach(n=>{n.disposeFunc!=null&&n.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 Gl)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,Ir(`Initialization of backend ${e} failed`),Ir(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Ir(`Initialization of backend ${e} failed`),Ir(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:s,asyncInit:r}=this.initializeBackend(n);if(r||s)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),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return md.nextTensorId++}nextVariableId(){return md.nextVariableId++}clone(e){let t=W.runKernel(Ba,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return W.runKernel(Ca,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Gh(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 s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=b2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(b2(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Gh(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:k,dtype:S}=b;return this.makeTensorFromDataId(w,k,S)});if(s){let b=this.getTensorsForGradient(h,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:c,attrs:u}=e,d=b2(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,c,t,d,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=c2(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,c)=>a[c]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&ba(e[0])&&(r=e.map(i=>ud(i)));let a=s.write(r,t,n),o=new Ke(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=v5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Ke(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new hd(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*s2(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 hd||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*s2(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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of 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a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=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===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(M(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));M(r instanceof Ke,()=>"The result y returned by f() must be a tensor.");let a=ZN(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Lh,o)}var sF=U({sparseSegmentMean_:nF});function rF(e,t,n){let s=_(e,"data","sparseSegmentSum"),r=_(t,"indices","sparseSegmentSum"),a=_(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Bh,o)}var aF=U({sparseSegmentSum_:rF});function oF(e,t,n,s,r,a,o,i){let l=_(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let c=_(t,"dataSplits","stringNGrams");if(c.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let u={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:c},p=W.runKernel(sd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var iF=U({stringNGrams_:oF});function lF(e,t,n=!0){let s=_(e,"input","stringSplit","string"),r=_(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=W.runKernel(Wh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var uF=U({stringSplit_:lF});function cF(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return W.runKernel(Vh,r,s)}var dF=U({stringToHashBucketFast_:cF}),pF={fft:Dd,ifft:Lu,rfft:_d,irfft:If},hF={hammingWindow:U_,hannWindow:fv,frame:mv,stft:q_},$e={flipLeftRight:Y_,grayscaleToRGB:Q_,resizeNearestNeighbor:vv,resizeBilinear:bv,rotateWithOffset:tP,cropAndResize:K_,nonMaxSuppression:sP,nonMaxSuppressionAsync:dP,nonMaxSuppressionWithScore:hP,nonMaxSuppressionWithScoreAsync:mP,nonMaxSuppressionPadded:yP,nonMaxSuppressionPaddedAsync:xP,threshold:IP,transform:CP},kv={bandPart:NP,gramSchmidt:RP,qr:DP},fF={absoluteDifference:FP,computeWeightedLoss:ta,cosineDistance:MP,hingeLoss:LP,huberLoss:WP,logLoss:UP,meanSquaredError:HP,sigmoidCrossEntropy:XP,softmaxCrossEntropy:YP},Pd={sparseFillEmptyRows:QP,sparseReshape:tF,sparseSegmentMean:sF,sparseSegmentSum:aF},_f={stringNGrams:iF,stringSplit:uF,stringToHashBucketFast:dF},na=class extends I3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return te(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return X3(e,t)}dispose(){this.iterations_!=null&&te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ee(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(na,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Pf=class extends na{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>tt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;j(()=>{let c=ue(L(i,this.rho),L(vt(o),1-this.rho)),u=L(fe(Cn(ue(l,this.epsilon)),Cn(ue(i,this.epsilon))),o),d=ue(L(l,this.rho),L(vt(u),1-this.rho));i.assign(c),l.assign(d);let p=ue(L(u,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(te(this.accumulatedGrads.map(e=>e.variable)),te(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Pf.className="Adadelta";vo(Pf);var Ff=class extends na{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>_u(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;j(()=>{let i=ue(o,vt(a));o.assign(i);let l=ue(L(fe(a,Cn(ue(i,W.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&te(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)}};Ff.className="Adagrad";vo(Ff);var Of=class extends na{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=Ee(t).variable(),this.accBeta2=Ee(n).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=xe(1,this.accBeta1),s=xe(1,this.accBeta2);t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>tt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>tt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=ue(L(u,this.beta2),L(vt(l),1-this.beta2)),h=fe(d,n),f=fe(p,s);c.assign(d),u.assign(p);let m=ue(L(fe(h,ue(Cn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&te(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),j(()=>{this.accBeta1.assign(ea(this.beta1,this.iterations_+1)),this.accBeta2.assign(ea(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};Of.className="Adam";vo(Of);var Mf=class extends na{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ee(0).variable(),this.accBeta1=Ee(t).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=xe(1,this.accBeta1),s=fe(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:tt(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:tt(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=L(u,this.beta2),h=Kt(l),f=Rr(p,h);c.assign(d),u.assign(f);let m=ue(L(fe(s,n),fe(d,ue(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ue(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&te(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)}};Mf.className="Adamax";vo(Mf);var Fd=class extends na{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=W.registeredVariables[n];j(()=>{let o=ue(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=An(Ee(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};zf.className="Momentum";vo(zf);var Lf=class extends na{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>tt(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>tt(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;j(()=>{let c=ue(L(i,this.decay),L(vt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=ue(L(u,this.decay),L(o,1-this.decay)),p=fe(L(o,this.learningRate),Cn(xe(c,ue(vt(d),this.epsilon)))),h=ue(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=xe(r,h);r.assign(f)}else{let u=ue(L(i,this.decay),L(vt(o),1-this.decay)),d=ue(L(l,this.momentum),fe(L(o,this.learningRate),Cn(ue(u,this.epsilon))));i.assign(u),l.assign(d);let p=xe(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&te(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&te(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&te(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(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.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)}};Lf.className="RMSProp";vo(Lf);var sl=class{static sgd(e){return new Fd(e)}static momentum(e,t,n=!1){return new zf(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new Lf(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new Of(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Pf(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new Mf(e,t,n,s,r)}static 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E={};Le(E,{ERF_A1:()=>CF,ERF_A2:()=>TF,ERF_A3:()=>NF,ERF_A4:()=>EF,ERF_A5:()=>RF,ERF_P:()=>SF,PARALLELIZE_THRESHOLD:()=>D1,SELU_SCALE:()=>Sv,SELU_SCALEALPHA:()=>Iv,applyActivation:()=>$f,assertAndGetBroadcastShape:()=>Tt,assertAxesAreInnerMostDims:()=>$$,assertParamsConsistent:()=>gF,assignToTypedArray:()=>OF,axesAreInnerMostDims:()=>d1,calculateShapes:()=>p3,checkEinsumDimSizes:()=>VF,combineLocations:()=>Z3,complexWithEvenIndex:()=>_F,complexWithOddIndex:()=>PF,computeConv2DInfo:()=>xd,computeConv3DInfo:()=>$3,computeDefaultPad:()=>Z2,computeDilation2DInfo:()=>eR,computeOptimalWindowSize:()=>AF,computeOutAndReduceShapes:()=>Y3,computeOutShape:()=>yF,computePool2DInfo:()=>R3,computePool3DInfo:()=>tR,convertConv2DDataFormat:()=>D3,decodeEinsumEquation:()=>BF,eitherStridesOrDilationsAreOne:()=>Nr,expandShapeToKeepDim:()=>el,exponent:()=>zF,exponents:()=>MF,fromStringArrayToUint8:()=>YF,fromUint8ToStringArray:()=>ZF,getAxesPermutation:()=>J3,getBroadcastDims:()=>jR,getComplexWithIndex:()=>FF,getEinsumComputePath:()=>UF,getEinsumPermutation:()=>WF,getFusedBiasGradient:()=>Rf,getFusedDyActivation:()=>Ef,getImageCenter:()=>xF,getInnerMostAxes:()=>D$,getPermuted:()=>vF,getReductionAxes:()=>sn,getReshaped:()=>bF,getReshapedPermuted:()=>wF,getSliceBeginCoords:()=>kF,getSliceSize:()=>IF,getUndoAxesPermutation:()=>p1,isIdentityPermutation:()=>GF,log:()=>EN,mergeRealAndImagArrays:()=>$F,prepareAndValidate:()=>d3,prepareSplitSize:()=>jF,segment_util:()=>Nv,shouldFuse:()=>Df,slice_util:()=>yn,splitRealAndImagArrays:()=>DF,tupleValuesAreOne:()=>wo,upcastType:()=>Ln,validateInput:()=>O2,validateUpdateShape:()=>F2,warn:()=>Ir});function 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rt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Qf(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Co(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;$r(x===0,"input layer has >1 nodes"),$r(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof Gu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},s={},r={},a={},o=[],i=(y,A,x,b,w,k)=>{(b==null||w==null||k==null)&&(b=y.sourceLayer,w=y.nodeIndex,k=y.tensorIndex);let S=b.inboundNodes[w];if(x.indexOf(S)!==-1)throw new hr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(_r.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let N=S.inboundLayers.length;for(let R=0;R<N;R++){let P=S.inputTensors[R],$=S.inboundLayers[R],D=S.nodeIndices[R],T=S.tensorIndices[R];i(P,A,x,$,D,T)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],c=[];for(let y of this.outputs)i(y,l,c);let u=o.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=s[y.outboundLayer.id]==null?0:s[y.outboundLayer.id];A=Math.max(A,x),s[y.outboundLayer.id]=A,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],k=y.nodeIndices[b],S=w.inboundNodes[k],N=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,N),n[S.id]=S}}let d={};for(let y in t){let A=t[y];A in d||(d[A]=[]),d[A].push(n[y])}let p={};for(let y in s){let A=s[y];A in p||(p[A]=[]),p[A].push(r[y])}let h=Object.keys(p).map(y=>parseInt(y,10)).sort(Wf);this.layers=[];for(let y of h){let A=p[y];A.sort((x,b)=>{let w=a[x.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let x of A)x instanceof _r&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,h=Object.keys(d).map(y=>parseInt(y,10)).sort(Wf);let f=this.inputs.slice(),m=[];for(let y of h)for(let A of d[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new hr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new hr(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new nm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new q(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new q(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new q(`${a.length} of ${s} weights are not set: ${a}`)}ny(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${cy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=uy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=Nt(e);let n=new dl;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Gd(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=Nt(e);let n;return t==null?n=al(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=em(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Wf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=n[A];u.push(x)}let d=c.computeOutputShape(as(u)),p=em(d),h=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],u=`${i.name}_${l}_${c}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];$r(i in n),r.push(n[i])}return as(r)}runInternalGraph(e,t){t==null&&(t=al(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Wf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,y,A;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),y=Nt(u.call(x,f)),A=Nt(u.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),y=Nt(u.call(m,f)),A=Nt(u.computeMask(m,g));if(u.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],w=y[x],k=A[x];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){$r(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,c]=n[i.id];o.push(l.shape),r.push(l),a.push(c)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof _r?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=_r.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=_r.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=_r.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],y=d.nodeIndices[m],A=d.tensorIndices[m],x=_r.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,h])}l.push(f)}}}let c={};c.name=a.name,c.className=o,c.config=i,c.inboundNodes=l,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=_r.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[a];s.push([o.name,c,u])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=_r.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[a];r.push([o.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let y=[],A;for(let x of g){let b=x[0],w=x[1],k=x[2];if(A=x[3]==null?{}:x[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=w){o(m,g);return}let N=S.inboundNodes[w];y.push(N.outputTensors[k])}y.length>0&&m.apply(as(y),A)}function l(m){let g=m.name,y=yr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(s),r[g]=y,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${x}`);o(y,x)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!ez(a);)for(let m of u){let g=r[m.name];if(g.name in a){let y=a[g.name];delete a[g.name];for(let A of y)i(g,A)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],y=m[1],A=m[2];$r(g in r);let b=r[g].inboundNodes[y].outputTensors;d.push(b[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];$r(g in r);let b=r[g].inboundNodes[y].outputTensors;p.push(b[A])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function IL(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Iw(e,t){return IL(e,t,"classWeight")}async function Sw(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return ir(e);if(e.shape.length===2){if(e.shape[1]>1)return Fs(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());te(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Zt(o,"float32")}else return null}function SL(e,t){return L(e,t)}var CL=32;function Cw(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=Tw("input",e.inputNames,n),o=Tw("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)v.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function Tw(e,t,n){if(n instanceof Ke)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function TL(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function NL(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.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}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(Nw(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=TL(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=pw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=hw(u,d,n.epochs,null,null,EL(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(s||(m=await t.iterator());s?y<n.batchesPerEpoch:!0;){let x=await m.next();if(s&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:b,ys:w}=Cw(e,x.value),k={};k.batch=A,k.size=b[0].shape[0],await p.onBatchBegin(A,k);let S=[];if(n.classWeight!=null){let P=Iw(n.classWeight,e.outputNames);for(let $=0;$<P.length;++$)S.push(await Sw(w[$],null,P[$]))}let N=b.concat(w).concat(S),R=i(N);te(N);for(let P=0;P<l.length;++P){let $=l[P],D=R[P];k[$]=D,An(D)}await p.onBatchEnd(A,k),iw(k),A++,y++}if(s?y>=n.batchesPerEpoch:x.done){if(r){let b;Nw(n.validationData)?b=Nt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=Nt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?CL:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,g),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function EL(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function Nw(e){return typeof e.iterator=="function"}function RL(e){return typeof e.next=="function"}async function $L(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new Ve("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=RL(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let c=await o.next();if(a=j(()=>{if(c.value){let{xs:u,ys:d}=Cw(e,c.value),p=u.concat(d),h=j(()=>r(p));if(te(p),l===0)for(let m=0;m<h.length;++m)a.push(Ee(0));let f=p[0].shape[0];for(let m=0;m<h.length;++m){let g=h[m],y=a[m];a[m]=j(()=>ue(a[m],L(f,g))),l>0&&te(y)}te(h),i+=f,++l}return a}),c.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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e.metrics)r[a]=ol(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=es.getSaveHandlers(e);if(l.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new q(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await es.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:zL,generatedBy:`TensorFlow.js tfjs-layers v${cy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await es.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=es.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;bw(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){bw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};aa.className="Model";de.registerClass(aa);var _w=class extends aa{};_w.className="Functional";de.registerClass(_w);async function LL(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Ud(n),r=yr(s,t);if(e.weightsManifest!=null){let a=await es.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),te(a)}return r}async function BL(e,t){if(t==null&&(t={}),typeof e=="string"){let n=es.getLoadHandlers(e,t);if(n.length===0)n.push(es.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return WL(e,void 0,t)}async function WL(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=yr(Ud(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. 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Add some layers first.");this.model=new aa({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof qu))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=yr(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};qu.className="Sequential";de.registerClass(qu);function UL(e){return new aa(e)}function GL(e){return new qu(e)}function HL(e,t){return t==null&&(t={}),BL(e,t)}function Pw(e){return ow(e)}function jL(e,t){Qs.registerCallbackConstructor(e,t)}var is=class extends de.Serializable{getConfig(){return{}}},Fw=class extends is{apply(e,t=1){return Az(e,t)}};Fw.className="elu";de.registerClass(Fw);var Ow=class extends is{apply(e){return bf(e)}};Ow.className="selu";de.registerClass(Ow);var Mw=class extends is{apply(e){return cr(e)}};Mw.className="relu";de.registerClass(Mw);var zw=class extends is{apply(e){return j(()=>Fu(6,cr(e)))}};zw.className="relu6";de.registerClass(zw);var Lw=class extends is{apply(e){return e}};Lw.className="linear";de.registerClass(Lw);var Bw=class extends is{apply(e){return ns(e)}};Bw.className="sigmoid";de.registerClass(Bw);var Ww=class extends is{apply(e){return bz(e)}};Ww.className="hardSigmoid";de.registerClass(Ww);var Vw=class extends is{apply(e){return Qi(e)}};Vw.className="softplus";de.registerClass(Vw);var Uw=class extends is{apply(e){return xz(e)}};Uw.className="softsign";de.registerClass(Uw);var Gw=class extends is{apply(e){return Zi(e)}};Gw.className="tanh";de.registerClass(Gw);var gy=class extends is{apply(e,t=-1){return nl(e,t)}};gy.className="softmax";de.registerClass(gy);var Hw=class extends is{apply(e,t=-1){return hf(e,t)}};Hw.className="logSoftmax";de.registerClass(Hw);var jw=class extends is{apply(e,t=1){return j(()=>L(ns(L(e,t)),e))}};jw.className="swish";de.registerClass(jw);var qw=class extends is{apply(e){return j(()=>L(e,Zi(Qi(e))))}};qw.className="mish";de.registerClass(qw);function Ro(e){return e.getClassName()}function yy(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"activation")}function $o(e){if(e==null){let t={};return t.className="linear",t.config={},yy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},yy(t)}else return e instanceof is?e:yy(e)}function Ay(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 Xw=class extends de.Serializable{},jd=class extends Xw{constructor(e){super();Ay(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 j(()=>{let t=jt([1]);return this.hasL1&&(t=ue(t,ke(L(this.l1,Kt(e))))),this.hasL2&&(t=ue(t,ke(L(this.l2,Bd(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jd.className="L1L2";de.registerClass(jd);function qL(e){return Ay(e),new jd({l1:e!=null?e.l1:null,l2:0})}function XL(e){return Ay(e),new jd({l2:e!=null?e.l2:null,l1:0})}var Kw={l1l2:"L1L2"};function It(e){return F1(e)}function Zw(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Kw?Kw[e]:e,config:{}};return Zw(n)}else return e instanceof Xw?e:Zw(e)}var xy=class extends rt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let n=cr(e);return this.maxValue!=null&&(n=ss(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};xy.className="ReLU";de.registerClass(xy);var by=class extends rt{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=Ge(e);return kd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};by.className="LeakyReLU";de.registerClass(by);var vy=class extends rt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Pt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ft(e.alphaRegularizer),this.alphaConstraint=on(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 q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=At(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Yt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ge(e),Ed(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Lt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};vy.className="PReLU";de.registerClass(vy);var wy=class extends rt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${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=Ge(e);return Du(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wy.className="ELU";de.registerClass(wy);var ky=class extends rt{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=Ge(e);return L(n,pe(rs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ky.className="ThresholdedReLU";de.registerClass(ky);var Iy=class extends rt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new gy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ge(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}};Iy.className="Softmax";de.registerClass(Iy);function Xu(e,t,n){if(typeof e=="number")return al(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!fz(r))throw new q(`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 Ar(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Pr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+No([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function Sy(e,t){return j(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,1]):e))}function Yw(e,t){return j(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,4,1]):e))}function KL(e,t,n,s=1,r="valid",a,o=1){return j(()=>{if(a==null&&(a=pr()),qt(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=et(e,[0,2,1])),r==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=af(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=mr(i,n)),i})}function Jw(e,t,n,s=[1,1],r="valid",a,o,i=null){return j(()=>{if(a==null&&(a=pr()),qt(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Sy(e,a);if(r==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=So.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=et(l,[0,3,1,2])),l})}function ZL(e,t,n,s=[1,1,1],r="valid",a,o){return j(()=>{if(a==null&&(a=pr()),qt(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Yw(e,a);if(r==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=n1(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=mr(i,n)),a==="channelsFirst"&&(i=et(i,[0,4,1,2,3])),i})}var Cy=class extends rt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Cy.verifyArgs(t),this.rank=e,bn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xu(t.kernelSize,e,"kernelSize"),this.strides=Xu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Ms(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,qt(this.dataFormat),this.activation=$o(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=on(t.biasConstraint),this.biasRegularizer=Ft(t.biasRegularizer),this.activityRegularizer=Ft(t.activityRegularizer),this.dilationRate=Xu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!M1(e.kernelSize,"number",1,3))throw new q(`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:Ro(this.activation),useBias:this.useBias,biasInitializer:Lt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qd=class extends Cy{constructor(e,t){super(e,t);this.kernel=null,qd.verifyArgs(t),this.filters=t.filters,bn(this.filters,"filters"),this.kernelInitializer=Pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=on(t.kernelConstraint),this.kernelRegularizer=Ft(t.kernelRegularizer)}build(e){e=At(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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 j(()=>{e=Ge(e);let n,s=this.bias==null?null:this.bias.read(),r=Wv(this.activation.getClassName());if(r!=null&&this.rank===2)n=Jw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=KL(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Jw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=ZL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=At(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 a=Ar(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Lt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:an(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 q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Xd=class extends qd{constructor(e){super(2,e);Xd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!M1(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Xd.className="Conv2D";de.registerClass(Xd);var Kd=class extends qd{constructor(e){super(3,e);Kd.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 q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Kd.className="Conv3D";de.registerClass(Kd);var Ty=class extends Xd{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ge(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=Pr(i,d,c,this.padding),f=Pr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,1]));let g=of(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=et(g,[0,3,1,2])),this.bias!=null&&(g=mr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=At(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Pr(t[s],i,a,this.padding),t[r]=Pr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ty.className="Conv2DTranspose";de.registerClass(Ty);var Ny=class extends Kd{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ge(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Pr(l,f,d,this.padding),A=Pr(c,m,p,this.padding),x=Pr(u,g,h,this.padding),b=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,4,1]));let w=W3(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=et(w,[0,4,1,2,3])),this.bias!==null&&(w=mr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=At(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=Pr(t[s],c,o,this.padding),t[r]=Pr(t[r],u,i,this.padding),t[a]=Pr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ny.className="Conv3DTranspose";de.registerClass(Ny);var Qw=class extends qd{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 q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("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 q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=on(t.depthwiseConstraint),this.pointwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=on(t.pointwiseConstraint)}build(e){if(e=At(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Yt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Ge(e);let n;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=et(e,[0,2,3,1])),n=b1(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=et(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=Lt(this.depthwiseInitializer),e.pointwiseInitializer=Lt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}};Qw.className="SeparableConv";var Ey=class extends Qw{constructor(e){super(2,e)}};Ey.className="SeparableConv2D";de.registerClass(Ey);var dm=class extends qd{constructor(e){super(1,e);dm.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"&&!M1(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};dm.className="Conv1D";de.registerClass(dm);var Ry=class extends rt{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 j(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let n=Uf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uf(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}};Ry.className="Cropping2D";de.registerClass(Ry);var $y=class extends rt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,dz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Ge(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=et(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a]);return et(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$y.className="UpSampling2D";de.registerClass($y);function YL(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=pr()),qt(r);let o=Sy(e,r);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=$u(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}var Dy=class extends Cy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=on(e.depthwiseConstraint),this.depthwiseRegularizer=Ft(e.depthwiseRegularizer)}build(e){if(e=At(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 j(()=>{e=Ge(e);let n=YL(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ar(t,this.kernelSize[0],this.padding,this.strides[0]),a=Ar(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}};Dy.className="DepthwiseConv2D";de.registerClass(Dy);function ek(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function tk(e,t,n,s=!1,r,a,o=!1,i=!1){return j(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(fr(2,l));if(t=et(t,c),a!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Ht(r,-1)),r=et(r,c)),s&&(t=ws(t,0),r!=null&&(r=ws(r,0)));let u=[],d,p=n,h=t.shape[0],f=Wn(t),m;r!=null&&(m=Wn(r));for(let y=0;y<h;++y){let A=f[y],x=j(()=>e(A,p));if(r==null)d=x[0],p=x[1];else{let b=j(()=>{let w=m[y],k=xe(vs(w),w),S=ue(L(x[0],w),L(p[0],k)),N=p.map((R,P)=>ue(L(x[1][P],w),L(R,k)));return{output:S,newStates:N}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=Tn(u,1)),[d,g,p]})}var Fr=class extends rt{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new fm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("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 Yt({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 fr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ey(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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 Ve("Constants support is not implemented in RNN yet.");ey(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Yt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Ve("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Yt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new q("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(s=>jt([n,s])):this.states_=[jt([n,this.cell.stateSize])];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>jt([n,s])):this.states_[0]=jt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):te(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>An(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=ek(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Yt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof gr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ge(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new q(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=tk((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return j(()=>{let t=jt(e.shape);return t=ke(t,[1,2]),t=Ld(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?H1(t,[1,n]):t):this.cell.stateSize>1?[H1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Fr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=yr(s,n);return new e(Object.assign(t,{cell:r}))}};Fr.className="RNN";de.registerClass(Fr);var Zd=class extends rt{},pm=class extends Zd{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,bn(this.units,"units"),this.activation=$o(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,No([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,No([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 j(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>vs(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>vs(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Dr(L(e,a),this.kernel.read()):r=Dr(e,this.kernel.read()),this.bias!=null&&(r=mr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(r,Dr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ro(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};pm.className="SimpleRNNCell";de.registerClass(pm);var _y=class extends Fr{constructor(e){e.cell=new pm(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};_y.className="SimpleRNN";de.registerClass(_y);var hm=class extends Zd{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 q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,bn(this.units,"units"),this.activation=$o(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=$o(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,No([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,No([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 j(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>vs(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>vs(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=Dr(e,this.kernel.read());this.useBias&&(c=mr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=xn(u,[2*this.units,this.units],u.rank-1),h=Dr(s,d),[f,m,g]=xn(c,3,c.rank-1),[y,A]=xn(h,2,h.rank-1);o=this.recurrentActivation.apply(ue(f,y)),i=this.recurrentActivation.apply(ue(m,A));let x=Dr(L(i,s),p);l=this.activation.apply(ue(g,x));let b=ue(L(o,s),L(ue(1,_t(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ro(this.activation),recurrentActivation:Ro(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};hm.className="GRUCell";de.registerClass(hm);var Py=class extends Fr{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 hm(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Py.className="GRU";de.registerClass(Py);var Yd=class extends Zd{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,bn(this.units,"units"),this.activation=$o(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=$o(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,No([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,No([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=At(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Js{apply(i,l){let c=r.apply([a]),u=new Hf().apply([a]),d=r.apply([a*2]);return Zv(Zv(c,u),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>vs(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>vs(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let d=Dr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=ue(d,Dr(s,this.recurrentKernel.read())),this.useBias&&(d=mr(d,this.bias.read()));let[p,h,f,m]=xn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=ue(L(l,r),L(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ro(this.activation),recurrentActivation:Ro(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Yd.className="LSTMCell";de.registerClass(Yd);var Fy=class extends Fr{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 Yd(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="LSTM";de.registerClass(Fy);var fm=class extends Zd{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 j(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){ey(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ll(`RNNCell_${s}`,()=>{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=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(yr(r,n));return new e({cells:s})}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 ty(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}ny(t)}};fm.className="StackedRNNCells";de.registerClass(fm);function Do(e){let{ones:t,rate:n,training:s=!1,count:r=1}=e,a=()=>Jv(t(),n),o=()=>Wd(a,t,s);return!r||r<=1?An(o().clone()):Array(r).fill(void 0).map(o).map(l=>An(l.clone()))}var JL=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},nk=class extends Fr{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Yt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,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 j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=jt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new q("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(()=>jt(r)):this.states_=[jt(r)];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>jt(r)):this.states_[0]=jt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`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()):te(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new q(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>An(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=Ar(l,s[0],r,a[0],o[0]),d=Ar(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};nk.className="ConvRNN2D";var mm=class extends Yd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,bn(this.filters,"filters"),this.kernelSize=Xu(n,2,"kernelSize"),this.kernelSize.forEach(i=>bn(i,"kernelSize")),this.strides=Xu(s||1,2,"strides"),this.strides.forEach(i=>bn(i,"strides")),this.padding=r||"valid",Ms(this.padding),this.dataFormat=a||"channelsLast",qt(this.dataFormat),this.dilationRate=Xu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>bn(i,"dilationRate"))}build(e){var t;e=At(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends Js{apply(d,p){let h=l.apply([c]),f=bs([c]),m=l.apply([c*2]);return G1([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Do({ones:()=>vs(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,J,Q)=>!J||!J[Q]?ee:L(J[Q],ee),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Do({ones:()=>vs(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),A=3,[x,b,w,k]=xn(this.kernel.read(),o,A),[S,N,R,P]=this.useBias?xn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,x,S,this.padding),u=this.inputConv(u,b,N,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,P,this.padding);let[$,D,T,O]=xn(this.recurrentKernel.read(),o,A);f=this.recurrentConv(f,$),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),y=this.recurrentConv(y,O);let B=this.recurrentActivation.apply(ue(c,f)),H=this.recurrentActivation.apply(ue(u,m)),z=ue(L(H,a),L(B,this.activation.apply(ue(d,g)))),X=L(this.recurrentActivation.apply(ue(p,y)),this.activation.apply(z));return[X,X,z]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=JL(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Qr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?mr(r,n,this.dataFormat):r}recurrentConv(e,t){return Qr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};mm.className="ConvLSTM2DCell";de.registerClass(mm);var Oy=class extends nk{constructor(e){let t=new mm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Oy.className="ConvLSTM2D";de.registerClass(Oy);var gm=class extends rt{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 s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wd(()=>Jv(n,this.rate,r,this.seed),()=>n,s)}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()}};gm.className="Dropout";de.registerClass(gm);var My=class extends gm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};My.className="SpatialDropout1D";de.registerClass(My);var zy=class extends rt{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,bn(this.units,"units"),this.activation=$o(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=on(e.kernelConstraint),this.biasConstraint=on(e.biasConstraint),this.kernelRegularizer=Ft(e.kernelRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=At(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=At(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=Wv(this.activation.getClassName()),r;return s!=null?r=Dr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Dr(n,this.kernel.read()),this.bias!=null&&(r=mr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Ro(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Dense";de.registerClass(zy);var Ly=class extends rt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=At(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],To(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=et(n,s)}return yz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Ly.className="Flatten";de.registerClass(Ly);var By=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.activation=$o(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ro(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};By.className="Activation";de.registerClass(By);var Wy=class extends rt{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 j(()=>(e=Ge(e),mz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="RepeatVector";de.registerClass(Wy);var Vy=class extends rt{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.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new q("Can only specifiy one unknown dimension.");else r*=l}let o=To(e);if(a!==null){if(r===0||o%r!=0)throw new q(n);s[a]=o/r}else if(o!==r)throw new q(n);return s}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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="Reshape";de.registerClass(Vy);var Uy=class extends rt{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=fr(1,e.dims.length+1);if(!v.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 Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=At(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return et(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="Permute";de.registerClass(Uy);var Gy=class extends rt{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=Ge(e),s=-1;return Ad(tl(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=-1,r=!0,a=Ad(tl(n,this.maskValue),s,r);return L(n,pe(a,n.dtype))})}};Gy.className="Masking";de.registerClass(Gy);var Hy=class extends rt{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(Nt(e.inputLength))}this.inputDim=e.inputDim,bn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,bn(this.outputDim,"outputDim"),this.embeddingsInitializer=Pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ft(e.embeddingsRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.embeddingsConstraint=on(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 j(()=>this.maskZero?(e=Ge(e),tl(e,tt(e))):null)}computeOutputShape(e){if(e=At(e),this.inputLength==null)return[...e,this.outputDim];let t=Nt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);n.dtype!=="int32"&&(n=Vf(n,"int32"));let s=Yv(this.embeddings.read(),G(n,[n.size]));return G(s,At(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Lt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="Embedding";de.registerClass(Hy);var hl=class extends rt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}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 s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new q("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=[At(e)]),e=e,e.length<2)throw new q(`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=Co(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Co(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=No(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Ld(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=G(i,[u].concat(To(c.slice(1))));p=et(p,[1,0]),p=G(p,d),n.push(p),r=!0}else if(l>1){let c=fr(1,l).concat([0]);n.push(et(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(et(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(fr(0,o-1));a=et(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Co(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Ht(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ks(n,t[s]);return n})}},jy=class extends hl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return t})}};jy.className="Add";de.registerClass(jy);var qy=class extends hl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};qy.className="Multiply";de.registerClass(qy);var Xy=class extends hl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return L(1/e.length,t)})}};Xy.className="Average";de.registerClass(Xy);var Ky=class extends hl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Rr(t,e[n]);return t})}};Ky.className="Maximum";de.registerClass(Ky);var Zy=class extends hl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Fu(t,e[n]);return t})}};Zy.className="Minimum";de.registerClass(Zy);var Yy=class extends hl{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 q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>G1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(pe(vs(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Ht(t[a],-1)):s.push(t[a]);let r=kt(s,this.axis);return sf(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Yy.className="Concatenate";de.registerClass(Yy);function Jd(e,t){for(;e<0;)e+=t;return e}function QL(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.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 Ve("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;c<o;++c)l.push(1);t=G(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let c=0;c<o;++c)l.push(1);e=G(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ke(L(e,t),a[0]):i=ke(L(et(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=Xe(e,t,l,c)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u<l+o;++u)c.push(u);i=dt(i,c)}return i.shape.length===1&&(i=Ht(i,1)),i})}var Jy=class extends hl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.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 Ve("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Jd(r,e[a].shape.length)):s=[Jd(this.axes,t.shape.length),Jd(this.axes,n.shape.length)],this.normalize&&(t=sm(t,s[0]),n=sm(n,s[1])),QL(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Jd(this.axes,e.length),Jd(this.axes,t.length)],n}computeOutputShape(e){v.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 Ve("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[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}};Jy.className="Dot";de.registerClass(Jy);var Qy=class extends rt{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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return Wd(()=>ue(Gf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Qy.className="GaussianNoise";de.registerClass(Qy);var eA=class extends rt{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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return this.rate>0&&this.rate<1?Wd(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Gf(n.shape,1,r))},()=>n,t.training||!1):n})}};eA.className="GaussianDropout";de.registerClass(eA);var tA=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(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 j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wd(()=>{let r=Ge(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=ko(Ou(n),this.rate);l=Vf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=ue(L(r,l),L(ue(l,-1),i));return ue(L(d,c),u)},()=>Ge(e),t.training||!1)}return e})}};tA.className="AlphaDropout";de.registerClass(tA);function Qd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=_3(e,t,n,s,r,a);else if(e.rank===3)o=P3(e,t,n,s,r,a);else if(e.rank===4)o=F3(e,t,n,s,r,a);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function eB(e,t,n,s,r=.001){return j(()=>{let a=mf(e,s),o=a.mean,i=a.variance;return[Qd(e,o,i,n,t,r),o,i]})}function tB(e,t,n,s,r=.001){return j(()=>{let a=mf(e,s),o=a.mean,i=a.variance,l=[];for(let f of fr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Qd(e,c,u,p,d,r),o,i]})}function nB(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),fr(0,e.rank-1))?eB(e,t,n,s,r):tB(e,t,n,s,r)}var nA=class extends rt{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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=on(e.betaConstraint),this.gammaConstraint=on(e.gammaConstraint),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer)}build(e){e=At(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Ge(e),r=s.shape,a=r.length,o=fr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=al(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,fr(0,a).slice(0,a-1)),d=()=>{if(u){let y=G(this.movingMean.read(),l),A=G(this.movingVariance.read(),l),x=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Qd(s,y,A,x,b,this.epsilon)}else return Qd(s,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 d();let[p,h,f]=nB(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,A,x)=>{j(()=>{let b=1-x,w=y.read(),k=L(xe(w,A),b);y.write(xe(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),movingMeanInitializer:Lt(this.movingMeanInitializer),movingVarianceInitializer:Lt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};nA.className="BatchNormalization";de.registerClass(nA);var sA=class extends rt{constructor(e){e==null&&(e={});super(e);if(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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=At(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!==Co(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Ge(e),s=n.shape,r=s.length;return j(()=>{let a=!0,{mean:o,variance:i}=mf(n,this.axis,a),l=al(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=Os(o,p),i=Os(i,p),u=Os(u,h),d=Os(d,h),Qd(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};sA.className="LayerNormalization";de.registerClass(sA);function sB(e,t,n){return j(()=>{if(e.rank!==4)throw new q(`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 q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=pr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],ur(e,s)})}var rA=class extends rt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?pr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=At(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 j(()=>sB(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};rA.className="ZeroPadding2D";de.registerClass(rA);function ym(e,t,n,s,r,a){return j(()=>{qt(r),Hv(a),Ms(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=pr()),a==null&&(a="max"),e=Sy(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Cd(e,t,n,i):o=bd(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}function sk(e,t,n,s,r,a){return j(()=>{qt(r),Hv(a),Ms(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=pr()),a==null&&(a="max"),e=Yw(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=f1(e,t,n,i):o=J2(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,4,1,2,3])),o})}var rk=class extends rt{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(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 q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(bn(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 q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ms(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=At(e);let t=Ar(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Ld(Ge(e),2);let n=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return dt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},aA=class extends rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Ms(s),ym(e,t,n,s,r,"max")}};aA.className="MaxPooling1D";de.registerClass(aA);var oA=class extends rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Ms(s),ym(e,t,n,s,r,"avg")}};oA.className="AveragePooling1D";de.registerClass(oA);var ak=class extends rt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(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 q(`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];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Ms(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ar(t,this.poolSize[0],this.padding,this.strides[0]),n=Ar(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 j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(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}},iA=class extends ak{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Ms(s),ym(e,t,n,s,r,"max")}};iA.className="MaxPooling2D";de.registerClass(iA);var lA=class extends ak{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Ms(s),ym(e,t,n,s,r,"avg")}};lA.className="AveragePooling2D";de.registerClass(lA);var ok=class extends rt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(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 q(`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];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Ms(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ar(t,this.poolSize[0],this.padding,this.strides[0]),n=Ar(n,this.poolSize[1],this.padding,this.strides[1]),s=Ar(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(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}},uA=class extends ok{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Ms(s),sk(e,t,n,s,r,"max")}};uA.className="MaxPooling3D";de.registerClass(uA);var cA=class extends ok{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Ms(s),sk(e,t,n,s,r,"avg")}};cA.className="AveragePooling3D";de.registerClass(cA);var ik=class extends rt{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},dA=class extends ik{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Ge(e);return zt(n,1)})}};dA.className="GlobalAveragePooling1D";de.registerClass(dA);var pA=class extends ik{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Ge(e);return Bn(n,1)})}};pA.className="GlobalMaxPooling1D";de.registerClass(pA);var lk=class extends rt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},hA=class extends lk{call(e,t){return j(()=>{let n=Ge(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}};hA.className="GlobalAveragePooling2D";de.registerClass(hA);var fA=class extends lk{call(e,t){return j(()=>{let n=Ge(e);return this.dataFormat==="channelsLast"?Bn(n,[1,2]):Bn(n,[2,3])})}};fA.className="GlobalMaxPooling2D";de.registerClass(fA);var uk=class extends rt{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 s=t.layer,r=yr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},mA=class extends uk{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=At(e),e.length<3)throw new q(`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=At(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Ge(e),tk((a,o)=>[Ge(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};mA.className="TimeDistributed";de.registerClass(mA);function rB(e){il(cz,"BidirectionalMergeMode",e)}var aB="concat",gA=class extends uk{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=yr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=yr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?aB:e.mergeMode,rB(this.mergeMode),e.weights)throw new Ve("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,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):as(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=ek(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Yt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof gr;for(let l of a)if(l instanceof gr!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=ws(r,1));let o;return this.mergeMode==="concat"?o=G1([s,r]):this.mergeMode==="sum"?o=ue(s,r):this.mergeMode==="ave"?o=L(.5,ue(s,r)):this.mergeMode==="mul"?o=L(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ll(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ll(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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implemented`)}},EV=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=_f.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=_f.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[_f.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},RV=(e,t,n)=>{switch(e.op){case"Cast":return[pe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Ht(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[dt(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[m1(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ur(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Nd(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[vd(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[s1(I("x",e,t,n),s,r)]}case"BroadcastTo":return[Eu(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[O3(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function qk(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return j(()=>oV(a,o,i));case"basic_math":return j(()=>iV(a,o,i));case"control":return hV(a,o,i);case"convolution":return j(()=>fV(a,o,i));case"creation":return j(()=>mV(a,o,i));case"dynamic":return gV(a,o,i);case"evaluation":return j(()=>yV(a,o,i));case"image":return j(()=>vV(a,o,i));case"graph":return j(()=>AV(a,o,i));case"logical":return j(()=>wV(a,o,i));case"matrices":return j(()=>kV(a,o,i));case"normalization":return j(()=>IV(a,o,i));case"reduction":return j(()=>SV(a,o,i));case"slice_join":return j(()=>CV(a,o,i));case"sparse":return j(()=>TV(a,o,i));case"spectral":return j(()=>NV(a,o,i));case"string":return j(()=>EV(a,o,i));case"transformation":return j(()=>RV(a,o,i));case"hash_table":return bV(a,o,i,s);case"custom":let l=vk(a.op);if(l&&l.customExecutor)return l.customExecutor(new aV(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Xk=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Kk(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>ks(p)[0]),u=[];s!=null&&(u=s.map(p=>ks(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((Zk(p)||FV(p)||OV(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function $V(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>ks(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var DV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],_V=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],PV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Zk(e){return DV.indexOf(e.op)>=0}function FV(e){return _V.indexOf(e.op)>=0}function OV(e){return PV.indexOf(e.op)>=0}var PA=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 PA(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(s=>s.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}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Kk(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return $V(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(u=>this.graph.nodes[ks(u)[0]]),r=t.map(u=>ks(u)[0]),a=r.map(u=>this.graph.nodes[u]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return j(()=>{let u=new Xk(this.weightMap,l,c,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=ks(f),y=[];y[g]=e[f],d[m]=y});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=qk(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,u,p,r,h)}}return this.parent==null&&u.dispose(p),t.map(f=>Un(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=zW(i.name,n,s);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let u=o[c.id];u===1?(c.dispose(),delete o[c.id]):u!=null&&o[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new Xk(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>Un(d,o,a)),l=i.map(d=>d.id),c=Object.keys(e).map(d=>e[d].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(h=>{h&&!h.kept&&!h.isDisposed&&!u.has(h.id)&&h.dispose()})}),this.parent==null&&a.dispose(u),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(A=>this.graph.nodes[ks(A)[0]]),o=n.map(A=>ks(A)[0]),i=o.map(A=>this.graph.nodes[A]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=Kk(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[x,b]=ks(A),w=[];w[b]=e[A],h[x]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let A=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(A)}u==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=i.filter(A=>!Zk(A)&&!Un(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw u!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${A}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&I("isConstant",u.node,s,n)&&([d]=oa(u.node.name,n)),s[u.node.name]==null){let p=qk(u.node,s,n,this._resourceManager);d||([d]=oa(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=oa(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=ks(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=ks(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ks(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},MV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},zV="?tfjs-format=file",LV="model.json",Yk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new MV}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=es.browserHTTPRequest(e,this.loadOptions);else{let t=es.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(es.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=es.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new PA(Wk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Wk.Instance.transformGraph(e.modelInitializer);this.initializer=new PA(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=es.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ke)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ut(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},d7=class extends vn{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(Z().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new d7(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 s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[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(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({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),s({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((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),nn(n,t)}},p7=class extends vn{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=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=dr([a,r,i,o],[1,4])}else this.cropBox=dr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Z().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 p7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Xs.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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tj={kernelName:vh,backendName:"cpu",kernelFunc:ej};function nj(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new tn(h.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=n.data.get(r.dataId).values,[b,w,k]=d,S=n.data.get(a.dataId).values,[N,R,P]=p,{batchSize:$,filterHeight:D,filterWidth:T,inChannels:O,inHeight:B,inWidth:H,outChannels:z,outHeight:X,outWidth:ee,strideHeight:J,strideWidth:Q}=h,ne=D-1-h.padInfo.top,K=T-1-h.padInfo.left,oe=z/O;for(let ce=0;ce<$;++ce)for(let he=0;he<O;++he)for(let Ae=0;Ae<B;++Ae){let Se=Ae-ne,Ce=Math.max(0,Math.ceil(Se/J)),Oe=Math.min(X,(D+Se)/J);for(let Ue=0;Ue<H;++Ue){let ze=Ue-K,wt=Math.max(0,Math.ceil(ze/Q)),mt=Math.min(ee,(T+ze)/Q),gt=0;for(let pt=Ce;pt<Oe;++pt){let bt=pt*J-Se;for(let Ye=wt;Ye<mt;++Ye){let 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De=Z();De.registerFlag("HAS_WEBGL",()=>De.getNumber("WEBGL_VERSION")>0);De.registerFlag("WEBGL_VERSION",()=>ax(2)?2:ax(1)?1:0);De.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);De.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>De.get("WEBGL_VERSION")===2);De.registerFlag("WEBGL_CPU_FORWARD",()=>!0);De.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);De.registerFlag("WEBGL_PACK",()=>De.getBool("HAS_WEBGL"));De.registerFlag("WEBGL_PACK_NORMALIZATION",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_CLIP",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_REDUCE",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_LAZILY_UNPACK",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_CONV_IM2COL",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>WI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>VI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",c=`
#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",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function xl(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function Mm(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function GK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function HK(e,t,n="index"){let s=e.map((a,o)=>o),r=GK(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function ix(e){let t=v.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function lx(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var qI=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:XI}=E;function jK(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=ux(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>qK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=Gn(),l=ZK(i),c,u,d=QK(i);return t.isPacked?(c=XK(t.logicalShape,o,n.enableShapeUniforms),u=JK(i)):(c=KK(t.logicalShape,o,n.enableShapeUniforms),u=YK(i)),n.packedInputs&&(d+=sZ),[d,l,u,r,c,a,n.userCode].join(`
`)}function nc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return mZ(e,t);case 1:return yZ(e,t);case 2:return xZ(e,t);case 3:return vZ(e,t);case 4:return kZ(e,t);case 5:return IZ(e);case 6:return SZ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function KI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return fZ(e);case 1:return gZ(e,t);case 2:return AZ(e,t);case 3:return bZ(e,t);default:return wZ(e,t)}}function qK(e,t,n=!1,s){let r="";n?r+=KI(e,s):r+=nc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=CZ(e,t):r+=TZ(e,t)),r}function XK(e,t,n){switch(e.length){case 0:return ZI();case 1:return rZ(e,t,n);case 2:return pZ(e,t,n);case 3:return oZ(e,t,n);default:return lZ(e,t,n)}}function KK(e,t,n){switch(e.length){case 0:return ZI();case 1:return aZ(e,t,n);case 2:return hZ(e,t,n);case 3:return iZ(e,t,n);case 4:return uZ(e,t,n);case 5:return cZ(e,t);case 6:return dZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function ZK(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function YK(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function JK(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function QK(e){return`${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);
}
${eZ}
${tZ}
${nZ}
`}var eZ=`
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);
}
`,tZ=`
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);
}
`,nZ=`
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);
}
`,sZ=`
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 ZI(){return`
int getOutputCoords() {
return 0;
}
`}function rZ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function aZ(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function oZ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function iZ(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Mm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=xl(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function lZ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
int b${c} = index / ${o};
index -= b${c} * ${o};
`+i,l=`b${c}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${l});
}
`}function uZ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${Mm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=xl(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function cZ(e,t){let n=xl(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function dZ(e,t){let n=xl(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function pZ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function hZ(e,t,n){return v.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function bl(e){return`offset${e}`}function fZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Gn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function mZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${s}() {
return sampleTexture(${n}, halfCR);
}
`;let o=bl(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,l]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
return sampleTexture(${n}, uv);
}
`}function gZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Gn();if(t)return`
vec4 ${s}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function yZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${sc(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=bl(n);return o===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function AZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Gn();if(a!=null&&v.arraysEqual(n,a))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return ${l.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${l.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${s}, uv);
}
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
return ${l.texture2D}(${s}, uv);
}
`}function xZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`;let p=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=rc(e,l),h=["row","col"];return`
${nc(p,t)}
float ${r}(int row, int col) {
return ${r}(${ac(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${sc(e)}
}
`;let c=a[0],u=a[1],d=bl(s);return u===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
return sampleTexture(${s}, uv);
}
`:c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${c}, ${u}, index);
return sampleTexture(${s}, uv);
}
`}function bZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=rc(e,p),m=["b","row","col"];return`
${KI(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${ac(m,h)});
}
`}let i=Gn();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${c}, ${d}, ${u}, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`}function vZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=rc(e,c),g=["row","col","depth"];return`
${nc(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${ac(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${sc(e)}
}
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(p===o&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let f=bl(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${s}, uv);
}
`}function wZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Gn();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
return ${r.texture2D}(${n}, uv);
}
`}function kZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let A=rc(e,l),x=["row","col","depth","depth2"];return`
${nc(A,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${ac(x,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, 1)));
${sc(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let y=bl(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${y});
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${y});
return sampleTexture(${s}, uv);
}
`}function IZ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=rc(e,l),g=["row","col","depth","depth2","depth3"];return`
${nc(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${ac(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${r})) +
depth3;
${sc(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${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, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=bl(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function SZ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=rc(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${nc(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${ac(y,a)});
}
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${sc(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]*t[4]},
${t[2]*t[3]*t[4]},
${t[3]*t[4]},
${t[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=bl(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function sc(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function CZ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=XI(e.shapeInfo.logicalShape,t.logicalShape),l=St(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(A=>`coords.${d[A+c]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((A,x)=>`coords.${d[x+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,y=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!y)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let A=a-2,x=a-1;i.indexOf(A)>-1&&i.indexOf(x)>-1?h="return vec4(outputValue.x);":i.indexOf(A)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${s}(${p});
${h}
}
`}function TZ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=St(l),u=XI(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${p}
return get${s}(${f});
}
`}function St(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ux(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function rc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ac(e,t){return t.map(n=>e[n]).join(", ")}function NZ(e,t,n,s){let r=n.map((x,b)=>{let w={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(w.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:w}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=jK(r,o,t),l=e.createProgram(i),c=null,u=e.getUniformLocation(l,"NAN",!1);Z().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let d=!1,p={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];p[b]=e.getUniformLocation(l,b,d),p[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,g,y;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),y=e.getUniformLocation(l,"outShapeStrides",d),g=e.getUniformLocation(l,"outTexShape",d));let A=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{A[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:p,customUniformLocations:A,inShapeInfos:a,outShapeInfo:o,infLoc:c,nanLoc:u,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:y,outTexShapeLocation:g}}function YI(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function EZ(e,t,n,s,r){t.program.enableShapeUniforms||(YI(t.inShapeInfos,n),YI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=ux(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,c)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,c)=>{let u=t.customUniformLocations[c],d=r[c];if(l.type==="float")e.gl.uniform1fv(u,d);else if(l.type==="vec2")e.gl.uniform2fv(u,d);else if(l.type==="vec3")e.gl.uniform3fv(u,d);else if(l.type==="vec4")e.gl.uniform4fv(u,d);else if(l.type==="int")e.gl.uniform1iv(u,d);else if(l.type==="ivec2")e.gl.uniform2iv(u,d);else if(l.type==="ivec3")e.gl.uniform3iv(u,d);else if(l.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function RZ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=ux(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=v.computeStrides(u);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),y=v.sizeFromShape(o.shape)===1,A=E.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${c?d:""}_${u.length}_${y}_${A}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Z().getNumber("WEBGL_VERSION")}`,a}function Ws(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var $Z=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ip.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Mm(["r","c","d"],e):xl(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},DZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ip.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?Mm(["r","c","d"],e):xl(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},_Z=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Ls.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
${qI}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},PZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ls.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
${qI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},FZ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?lx():ix(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},OZ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?lx():ix(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},JI={};Le(JI,{bindVertexProgramAttributeStreams:()=>i4,createBufferFromOutputTexture:()=>c4,createFloat16MatrixTexture:()=>s4,createFloat16PackedMatrixTexture:()=>o4,createFloat32MatrixTexture:()=>n4,createIndexBuffer:()=>t4,createPackedMatrixTexture:()=>a4,createUnsignedBytesMatrixTexture:()=>r4,createVertexBuffer:()=>e4,createVertexShader:()=>QI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>p4,downloadFloat32MatrixFromBuffer:()=>d4,downloadMatrixFromPackedOutputTexture:()=>f4,downloadPackedMatrixFromBuffer:()=>h4,getInternalFormatForFloat16MatrixTexture:()=>dx,getInternalFormatForFloat16PackedMatrixTexture:()=>fx,getInternalFormatForFloat32MatrixTexture:()=>cx,getInternalFormatForPackedMatrixTexture:()=>hx,getInternalFormatForUnsignedBytesMatrixTexture:()=>px,uploadDenseMatrixToTexture:()=>l4,uploadPixelDataToTexture:()=>u4});function QI(e){let t=Gn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return SI(e,n)}function e4(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return EI(e,t)}function t4(e){let t=new Uint16Array([0,1,2,2,1,3]);return RI(e,t)}function pp(e,t,n,s,r,a){DI(t,n);let o=$I(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function cx(e){return e.internalFormatFloat}function n4(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,cx(s),s.textureFormatFloat,e.FLOAT)}function dx(e){return e.internalFormatHalfFloat}function s4(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,dx(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function px(e){return e.downloadTextureFormat}function r4(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,px(s),e.RGBA,e.UNSIGNED_BYTE)}function hx(e){return e.internalFormatPackedFloat}function a4(e,t,n,s){let[r,a]=ec(t,n);return pp(e,r,a,hx(s),e.RGBA,e.FLOAT)}function fx(e){return e.internalFormatPackedHalfFloat}function o4(e,t,n,s){let[r,a]=ec(t,n);return pp(e,r,a,fx(s),e.RGBA,s.textureTypeHalfFloat)}function i4(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),sx(e,t,"clipSpacePos",n,3,a,s)&&sx(e,t,"uv",n,2,a,r)}function l4(e,t,n,s,r,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function u4(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function c4(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function d4(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function p4(e,t,n,s){let[r,a]=lp(t,n),o=4,i=new Uint8Array($K(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function h4(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(DK(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function f4(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var zm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Z().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Em(t,e)):this.gl=Mr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Z().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=up(this.gl,r),Bs(this.gl,a))this.textureHalfFloatExtension=up(this.gl,a);else if(Z().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Bs(this.gl,s))this.colorBufferHalfFloatExtension=up(this.gl,s);else if(Z().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Bs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Bs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=e4(this.gl),this.indexBuffer=t4(this.gl),this.framebuffer=_I(this.gl),this.textureConfig=nx(this.gl,this.textureHalfFloatExtension)}get debug(){return Z().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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Ie(t,()=>t.attachShader(s,this.vertexShader)),Ie(t,()=>t.attachShader(s,n)),NI(t,s),this.debug&&$m(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=i4(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&$m(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?FI(this.gl,e,t):OI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=up(this.gl,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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kY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=Hn("rc",t),s=St(t),r=SY(t,e,n),a=CY(t,e[e.length-1],e[e.length-2],n),o=TY(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${o}));
}
}
`}}};function IY(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function SY(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function CY(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function TY(e,t){let n=e.length,s=IY(n,t);return n===1?`getA(rc),
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cEdge ? 0. : getA(${s[1]}),
rEdge ? 0. : getA(${s[2]}),
rEdge || cEdge ? 0. : getA(${s[3]})`}var x4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
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${s>0?"}":""}
`}this.userCode=`
${NY(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?lx():ix(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function NY(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?HK(["r","c","d"],"inputShape"):xl(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var EY=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=v4(t,n),r=w4(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=b4(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Nn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Nn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Nn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Nn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Nn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=v4(n,s),a=w4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=b4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function RY(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function b4(e,t,n,s,r){let a=$Y(t,s),o;if(r){let[l,c]=ec(e[0],e[1]);o=l*c}else{let[l,c]=lp(e[0],e[1]);o=l*c}let i=RY(n,a);return o*i}function $Y(e,t){switch(e){case Nn.PACKED_2X2_FLOAT32:return hx(t);case Nn.PACKED_2X2_FLOAT16:return fx(t);case Nn.UNPACKED_FLOAT32:return cx(t);case Nn.UNPACKED_FLOAT16:return dx(t);case Nn.PACKED_4X1_UNSIGNED_BYTE:return px(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function DY(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Nn.PACKED_2X2_FLOAT32:Nn.UNPACKED_FLOAT32:e?Nn.PACKED_2X2_FLOAT16:Nn.UNPACKED_FLOAT16}function v4(e,t){if(e===Ls.UPLOAD)return Nn.PACKED_2X2_FLOAT32;if(e===Ls.RENDER||e==null)return DY(t);if(e===Ls.DOWNLOAD||e===Ls.PIXELS)return Nn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function w4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Oo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},br="if (isnan(x)) return x;",_Y="return x;",k4="return abs(x);",PY="return (x >= 0.0) ? x : (exp(x) - 1.0);",FY=br+`
return (x < 0.0) ? 0.0 : x;
`,OY=br+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Lm="return x;",MY="return 1.0 / (1.0 + exp(-1.0 * x));",zY="return x;",LY=`
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;
`,BY=`
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;
`,WY=`
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;
`,VY="return 1.0 / (1.0 + exp(-1.0 * x));",oc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},UY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Hn("rc",t),s=St(t),r=wY(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${o}));
}
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Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Rm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Z().getBool("WEBGL_PACK")&&s===!0,o=a?_m(t):t,i=a?new PZ(o):new _Z(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().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:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=XY){return Z().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return GY(e.shape,t)}packedUnaryOp(e,t,n){let s=new oc(e.shape,t),r=this.compileAndRun(s,[e],n);return ts().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=g4(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,k4,e.dtype);let t=new Oo(e.shape,k4),n=this.compileAndRun(t,[e]);return ts().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return ts().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new UY(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new kY(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yl(e.shape),...Al(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[yl(t),...Al(t)],a=new x4(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=_m(s),o,i=Rm(a);n?o=new DZ(a):o=new $Z(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===ip.DENSE){let m=Rm(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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if (isnan(a)) return a;
if (isnan(b)) return b;
`,lc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Ws(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Wm=`
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;
`,hp=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Ws(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${St(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Hn("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${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);
${a}
setOutput(result);
}
`}};function Cs(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var eJ={kernelName:Ba,backendName:"webgl",kernelFunc:Cs};function Mo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Cs({inputs:{x:s},backend:n}),l=Cs({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var tJ={kernelName:jc,backendName:"webgl",kernelFunc:Mo},C4="return (a < 0.) ? b * a : a;",T4=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function nJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(T4,r.shape,o.shape):new lc(C4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var sJ={kernelName:mi,backendName:"webgl",kernelFunc:nJ},N4="return (a < 0.) ? b * a : a;",E4=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function rJ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(E4,s.shape,r.shape):new lc(N4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var aJ={kernelName:Ja,backendName:"webgl",kernelFunc:rJ},R4="if (isnan(x)) return x;",oJ=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,iJ=`
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 it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new oc(o.shape,t):u=new Oo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function En({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,w]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new lc(e,l.shape,c.shape);return u.runWebGLProgram(N,[k,S],Ln(b.dtype,w.dtype))}),A=Mo({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),A}let d=a||Ln(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?E.fromUint8ToStringArray(f):f,y=l.dtype==="string"?E.fromUint8ToStringArray(m):m,[A,x]=r(l.shape,c.shape,g,y,d),b=u.makeTensorInfo(x,d),w=u.texData.get(b.dataId);return w.values=A,b}let p=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new hp(t,l.shape,c.shape,n):h=new lc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Vm(e,t=!1){if(e==="linear")return t?zY:_Y;if(e==="relu")return t?BY:FY;if(e==="elu")return t?LY:PY;if(e==="relu6")return t?WY:OY;if(e==="prelu")return t?E4:N4;if(e==="leakyrelu")return t?T4:C4;if(e==="sigmoid")return t?VY:MY;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var $4=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Ws(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${A};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},D4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},_4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},P4="return a * b;";function gx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=E.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new _4(D4.REAL,s.shape,r.shape),u=new _4(D4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Mo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=sY(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new hp(P4,s.shape,r.shape):o=new lc(P4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var lJ={kernelName:Ka,backendName:"webgl",kernelFunc:gx};function uJ(e,t,n){let s=[yl(e.shape),...Al(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[yl(t),...Al(t)],o=new x4(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function be(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!dp(r.shape,l)&&!(u.texture!==null&&dp(u.shape,l))?uJ(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var cJ={kernelName:Ti,backendName:"webgl",kernelFunc:be},F4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
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) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${o}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},dJ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,d=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(o="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(o="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${u===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${u===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${l});
}
`}};function pJ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function vl(e,t,n,s){let r=pJ(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:c}=r[o],u,d;n==="mean"?u=o===0?new F4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new F4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new dJ({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var hJ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=St(this.rank),r=fJ(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function fJ(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"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var mJ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=St(this.rank),r=A4("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${i}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${i}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Um(e,t,n){let s=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mJ(e.shape,t):new hJ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function gJ(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=E.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Um(e,l,s),i=E.getInnerMostAxes(i.length,a)),E.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=E.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,y=be({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),A=fd(e.dtype),x=vl(y,A,"sum",s),b=be({inputs:{x},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(x),c&&s.disposeIntermediateTensorInfo(u),b}function Gm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return gJ(r,a,o,n)}var yJ={kernelName:oo,backendName:"webgl",kernelFunc:Gm};function jn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=mx(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Um(r,a,o);return c}var AJ={kernelName:po,backendName:"webgl",kernelFunc:jn},O4=1e3;function Hm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=y===A||y===1||A===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[y,d,h]:[y,h,d],S=s?[A,f,p]:[A,p,f],N=be({inputs:{x:e},backend:r,attrs:{shape:k}}),R=be({inputs:{x:t},backend:r,attrs:{shape:S}}),P=[N,R],$=Math.max(y,A),D=n?N.shape[1]:N.shape[2],T=a!=null,O=o!=null,B=l==="leakyrelu",H=l!=null?Vm(l,!0):null,z=T||O||B||H!=null,X;if((h===1||f===1)&&D>O4&&z===!1){let J=N,Q=R;n&&(J=jn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),P.push(J)),s&&(Q=jn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),P.push(Q));let ne=f!==1,K=f===1,oe=J;ne&&(oe=be({inputs:{x:J},backend:r,attrs:{shape:[$,D,1]}}),P.push(oe));let ce=f===1?2:1,he=Q;K&&(he=be({inputs:{x:Q},backend:r,attrs:{shape:[$,1,D]}}),P.push(he));let Ae=gx({inputs:{a:oe,b:he},backend:r});X=Gm({inputs:{x:Ae},backend:r,attrs:{axis:ce,keepDims:!0}}),P.push(Ae)}else{let J=Ln(e.dtype,t.dtype),Q=new $4(k,S,[$,h,f],n,s,T,H,O,B),ne=[N,R];if(a!=null&&ne.push(a),O&&ne.push(o),B){let K=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ne.push(K),P.push(K)}X=r.runWebGLProgram(Q,ne,J)}let ee=be({inputs:{x:X},backend:r,attrs:{shape:w}});P.push(X);for(let J of P)r.disposeIntermediateTensorInfo(J);return ee}function xJ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Hm({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var bJ={kernelName:fo,backendName:"webgl",kernelFunc:xJ},M4="return abs(x);";function vJ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=g4(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new oc(s.shape,M4):r=new Oo(s.shape,M4),n.runWebGLProgram(r,[s],s.dtype)}var wJ={kernelName:ni,backendName:"webgl",kernelFunc:vJ},kJ=br+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,IJ=it({opSnippet:kJ}),SJ={kernelName:ql,backendName:"webgl",kernelFunc:IJ},CJ=br+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,TJ=it({opSnippet:CJ}),NJ={kernelName:Xl,backendName:"webgl",kernelFunc:TJ},z4="return a + b;",EJ=En({opSnippet:z4,packedOpSnippet:z4,supportsComplex:!0,cpuKernelImpl:zZ}),RJ={kernelName:qr,backendName:"webgl",kernelFunc:EJ},$J=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}},DJ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function jm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Cs({inputs:{x:s[0]},backend:n});if(s.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=jm({inputs:s.slice(0,l),backend:n}),u=jm({inputs:s.slice(l),backend:n});return jm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Ln(l,c)),a=s.map(l=>l.shape),i=Z().getBool("WEBGL_PACK")?new DJ(s[0].shape,a):new $J(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var _J={kernelName:wa,backendName:"webgl",kernelFunc:jm};function PJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("all",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=vl(m,m.dtype,"all",n),y;if(o){let A=E.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var FJ={kernelName:Kl,backendName:"webgl",kernelFunc:PJ};function OJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("any",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=vl(m,m.dtype,"any",n),y;if(o){let A=E.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var MJ={kernelName:Zl,backendName:"webgl",kernelFunc:OJ},zJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},LJ=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=St(i),c=Hn("coords",i),u,d;if(a===1){d=i+1;let S=St(d);u=`
${S} sourceLocR = ${S}(${c.join()}, 0);
++${c[i-1]};
${S} sourceLocG = ${S}(${c.join()}, 0);
++${c[i-2]};
${S} sourceLocA = ${S}(${c.join()}, 0);
--${c[i-1]};
${S} sourceLocB = ${S}(${c.join()}, 0);
--${c[i-2]};`}else d=i,u=`
${l} sourceLocR = coords;
++${c[i-1]};
${l} sourceLocG = coords;
++${c[i-2]};
${l} sourceLocA = coords;
--${c[i-1]};
${l} sourceLocB = coords;
--${c[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=Hn("sourceLocR",d-1).concat("inIdx.r"),g=Hn("sourceLocG",d-1).concat("inIdx.g"),y=Hn("sourceLocB",d-1).concat("inIdx.b"),A=Hn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${A.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,k=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${k}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${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 L4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=E.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new zJ(i,n,s==null),c=[t];s!=null&&c.push(s);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let d=L4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function B4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=E.computeOptimalWindowSize(a),i=new LJ(r,o,n,s==null),l=s==null?[t]:[t,s],c=e.runWebGLProgram(i,l,"int32");if(c.shape.length===t.shape.length){let u=B4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function W4(e,t,n,s){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=E.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=be({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=L4(e,p,s);a.push(h);let f=be({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return B4(e,t,s)}function BJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=W4(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var WJ={kernelName:ka,backendName:"webgl",kernelFunc:BJ};function VJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=W4(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var UJ={kernelName:Yl,backendName:"webgl",kernelFunc:VJ},GJ=br+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,HJ=it({opSnippet:GJ}),jJ={kernelName:Jl,backendName:"webgl",kernelFunc:HJ},qJ=br+"return log(x + sqrt(x * x + 1.0));",XJ=it({opSnippet:qJ}),KJ={kernelName:Ql,backendName:"webgl",kernelFunc:XJ},ZJ=br+`
return atan(x);
`,YJ=it({opSnippet:ZJ}),JJ={kernelName:eu,backendName:"webgl",kernelFunc:YJ},QJ=oJ+`
return atan(a, b);
`,eQ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+iJ+`
return result;
`,tQ=En({opSnippet:QJ,packedOpSnippet:eQ}),nQ={kernelName:nu,backendName:"webgl",kernelFunc:tQ},sQ=br+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,rQ=it({opSnippet:sQ}),aQ={kernelName:tu,backendName:"webgl",kernelFunc:rQ},fp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${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 < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${S} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${A}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
const float initializationValue = ${y};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC += 4) {
int xC = xCCorner + wC * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${k}
}
int xC = xCCorner + ${b};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${k}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${k}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${k}
}
}
setOutput(${x});
}
`}},yx=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${y});
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 < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${d}) {
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 ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?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} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,N=`
if (${A}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${y});
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 < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${d};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
);
${N}
}
int xC = xCCorner + ${k};
if (${S===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${N}
} else if (${S===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${N}
} else if (${S===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
initializationValue
);
${N}
}
}
setOutput(${w});
}
}
`}};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Cs({inputs:{x:r},backend:n});let d=new fp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var iQ={kernelName:Ia,backendName:"webgl",kernelFunc:oQ};function lQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new yx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var uQ={kernelName:Hc,backendName:"webgl",kernelFunc:lQ},cQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${d});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${i};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${s}.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+= ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},dQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${i}) {
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 < ${d};
wR += ${l}) {
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 < ${p};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new dQ(p);return n.runWebGLProgram(h,[r],o.dtype)}var hQ={kernelName:mh,backendName:"webgl",kernelFunc:pQ};function fQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;tc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=new cQ(u);return n.runWebGLProgram(d,[r],o.dtype)}var mQ={kernelName:fh,backendName:"webgl",kernelFunc:fQ};function gQ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Hm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var yQ={kernelName:Sa,backendName:"webgl",kernelFunc:gQ},AQ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},xQ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},bQ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=Z().getBool("WEBGL_PACK_NORMALIZATION")?new xQ(s.shape,r.shape,a.shape,u,d,l):new AQ(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},vQ={kernelName:za,backendName:"webgl",kernelFunc:bQ},wQ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=St(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=kQ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Ax[o]} = start[${o}] + coords.${Ax[o]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},Ax=["x","y","z","w","u","v"];function kQ(e){if(e===1)return"sourceLoc";if(e<=6)return Ax.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var IQ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=St(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${a};
--${s[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${a};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function SQ(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=yn.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function uc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=yn.parseSliceParams(r,a,o);if(yn.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=cY(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=yn.isSliceContinous(r.shape,i,l);if(c||!u){let d=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IQ(l):new wQ(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),SQ(r,i,l,n)}var CQ={kernelName:Di,backendName:"webgl",kernelFunc:uc},TQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=be({inputs:{x:r},backend:n,attrs:{shape:l}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=be({inputs:{x:m},backend:n,attrs:{shape:u}}),y=uc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},NQ={kernelName:si,backendName:"webgl",kernelFunc:TQ};function EQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=m4(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var RQ={kernelName:gh,backendName:"webgl",kernelFunc:EQ},$Q="return float(a != b);",V4=En({opSnippet:$Q,cpuKernelImpl:aY,dtype:"bool"}),DQ={kernelName:bi,backendName:"webgl",kernelFunc:V4};function mp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Cs({inputs:{x:r.complexTensorInfos.real},backend:n})}var _Q={kernelName:td,backendName:"webgl",kernelFunc:mp},PQ="return float(int(x));";function FQ(e,t){let n=new Oo(e.shape,PQ),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function xx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Cs({inputs:{x:r},backend:n});let o=jt(r.shape),i=xx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Mo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=mp({inputs:{input:r},backend:n}),i=xx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Cs({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return FQ(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=V4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var OQ={kernelName:Ca,backendName:"webgl",kernelFunc:xx},U4="return ceil(x);",MQ=it({opSnippet:U4,packedOpSnippet:U4,cpuKernelImpl:BZ}),zQ={kernelName:Ta,backendName:"webgl",kernelFunc:MQ},LQ=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},BQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function WQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Z().getBool("WEBGL_PACK_CLIP")?i=new BQ(r.shape):i=new LQ(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var VQ={kernelName:Xr,backendName:"webgl",kernelFunc:WQ},UQ=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function G4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function GQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new UQ(s.shape),o=[G4(s,r.complexTensorInfos.real),G4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var HQ={kernelName:qc,backendName:"webgl",kernelFunc:GQ},jQ=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},qQ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=St(s),a=Hn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
return getChannel(
getT${f}(${qm(o,l,m)}),
vec2(${qm(c,l,m)}));
}`}let p=i.length,h=i[i.length-1];d+=`
return getChannel(
getT${p}(${qm(o,l,h)}),
vec2(${qm(c,l,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${d}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function qm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Xm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Cs({inputs:{x:r.complexTensorInfos.imag},backend:n})}var XQ={kernelName:Yc,backendName:"webgl",kernelFunc:Xm};function cc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>mp({inputs:{input:m},backend:n})),d=e.map(m=>Xm({inputs:{input:m},backend:n})),p=cc(u,t,n),h=cc(d,t,n),f=Mo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return be({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=E.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=WZ(d,p,s,h),m=E.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=cc(e.slice(0,u),t,n),p=cc(e.slice(u),t,n),h=cc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new qQ(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=KQ(e,t,n),i=new jQ(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=be({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function KQ(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>be({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function H4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return Cs({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),cc(i,a,n)}var ZQ={kernelName:ri,backendName:"webgl",kernelFunc:H4},j4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";n&&(s?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}
}
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${A}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * 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 < ${d}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${m}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${f===1}) {
if (${m}) {
dotProd +=
getX(batch, xR, xC, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${b}
setOutput(result);
}
`}},YQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
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 < ${u}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},JQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Ws(this.outputShape.length);let{dataFormat:n}=t,s=Gn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=`
blockIndex = rc.y + ${u};
pos = rc.x + ${c};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${c*2+u}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+u}] = 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;
${l}
${s.output} = result;
}
`}};function q4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((d===1||p===1)&&u>O4)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(dp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let S=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let N=Hm({a:w,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(N.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=Cs({inputs:{x:N},backend:s}),g.shape=n.outShape,y.push(N)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=be({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Hm({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=be({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(k),y.push(S)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function X4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,y=[m,g],A=!0,x=!1,b=[],w=be({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let S=new JQ(y,n),N=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(S,[w],"float32",N),P=be({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let $=r!=null,D=a!=null,T=i==="leakyrelu",O=i?Vm(i,!0):null,B=new $4(P.shape,k.shape,[1,g,n.outChannels],A,x,$,O,D,T),H=[P,k];if(r&&H.push(r),D&&H.push(a),T){let J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(J),b.push(J)}let z=s.runWebGLProgram(B,H,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],ee=be({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return ee}function QQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=q4({x:r,filter:a,convInfo:p,backend:n});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=X4({x:r,filter:a,convInfo:p,backend:n});else{let m=new j4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=be({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var eee={kernelName:Na,backendName:"webgl",kernelFunc:QQ},tee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
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 >= ${e.inWidth}) {
continue;
}
if (${a}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},nee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.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 < ${e.outChannels}; d2++) {
if (${a}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},see=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},ree=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${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) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new tee(p);return n.runWebGLProgram(h,[r,a],"float32")}var oee={kernelName:yh,backendName:"webgl",kernelFunc:aee};function iee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new nee(p);return n.runWebGLProgram(h,[r,a],"float32")}var lee={kernelName:Ea,backendName:"webgl",kernelFunc:iee};function uee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new YQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var cee={kernelName:Xc,backendName:"webgl",kernelFunc:uee};function dee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=E.computeConv3DInfo(r.shape,l,o,1,i),u=new see(c);return n.runWebGLProgram(u,[r,a],"float32")}var pee={kernelName:Ah,backendName:"webgl",kernelFunc:dee};function hee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=E.computeConv3DInfo(l,a.shape,i,1,o),u=new ree(c);return n.runWebGLProgram(u,[r,a],"float32")}var fee={kernelName:xh,backendName:"webgl",kernelFunc:hee},mee=R4+`
return cos(x);
`,gee=it({opSnippet:mee}),yee={kernelName:Ra,backendName:"webgl",kernelFunc:gee},Aee=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,xee=it({opSnippet:Aee}),bee={kernelName:$a,backendName:"webgl",kernelFunc:xee},vee=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${A});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},wee=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new vee(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},kee={kernelName:oi,backendName:"webgl",kernelFunc:wee},K4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${Z4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${St(s)} coords = getOutputCoords();
int end = ${Y4(s,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${Y4(s,"coords")} = idx;
val += getX(${Z4(s,"coords")});
}
setOutput(val);
}
`}};function Z4(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 ${e} is not yet supported`)}function Y4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=Cs({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new K4(u.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new K4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var See={kernelName:ai,backendName:"webgl",kernelFunc:Iee};function Cee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=m4(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=LZ(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Tee={kernelName:bh,backendName:"webgl",kernelFunc:Cee},Nee=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 Eee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new Nee(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Ree={kernelName:ii,backendName:"webgl",kernelFunc:Eee},J4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ws(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${u}
${c}
setOutput(result);
}
`}},Q4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ws(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;for(let g=0;g<c;g++){for(let y=0;y<u;y++)p+=`
xTexelC${y*2} = vec4(0.0);
xTexelC${y*2}Ready = 0;
xTexelC${y*2+1} = vec4(0.0);
xTexelC${y*2+1}Ready = 0;
xC${y} = vec4(0.0);`;p+=`
xR = xRCorner + ${g} * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let y=0;y<(d+1)/2;y++){let A=y*2;if(p+=`
xC = xCCorner + ${A*l};
`,i===1){if(A<u&&(o%2==1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
`,l===1&&A>0?p+=`
xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.xy);
`:p+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${A} = vec4(previous.zw, xTexelC${A}.xy);
} else {
xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
xC${A} = xTexelC${A};
`,A+1<u)){let x=o%2==0?v.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
`,l>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
xTexelC${A}Ready = 1;
}
`),p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy);
`):x===1?p+=`
xC${A+1} = xTexelC${A};
`:p+=`
xCOffset = xC + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
xC${A+1} = xTexelC${A+1};
`}}else A<u&&(o%2==1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`,A+1<u&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${A+1} = vec4(xTexelC${A+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.);
}
xTexelC${A+1}Ready = 1;
}
xC${A} = vec4(
xTexelC${A}.xy, xTexelC${A+1}.xy);
`,A+1<u&&(p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`)));A<u&&(p+=`
wTexel = getW(${g}, ${A}, d1, q);
dotProd += xC${A} * vec4(wTexel.xz, wTexel.xz);
`,A+1<u&&(p+=`
wTexel = getW(${g}, ${A+1}, d1, q);
dotProd += xC${A+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`}let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function $ee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new Q4(d):p=new J4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var Dee={kernelName:Da,backendName:"webgl",kernelFunc:$ee},_ee=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${a} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${s};
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 >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Pee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.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 < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new _ee(d);return n.runWebGLProgram(p,[r,a],"float32")}var Oee={kernelName:vh,backendName:"webgl",kernelFunc:Fee};function Mee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Pee(d);return n.runWebGLProgram(p,[r,a],"float32")}var zee={kernelName:wh,backendName:"webgl",kernelFunc:Mee},Lee=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function Bee(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=be({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Lee(a),l=n.runWebGLProgram(i,[o],o.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var Wee={kernelName:kh,backendName:"webgl",kernelFunc:Bee},Vee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
const ivec2 strides = ivec2(${r}, ${a});
const ivec2 pads = ivec2(${u}, ${d});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${o}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Vee(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var Gee={kernelName:Kc,backendName:"webgl",kernelFunc:Uee};function Hee(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=a[g]:(x=jn({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=be({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=gx({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Gm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var jee={kernelName:Zc,backendName:"webgl",kernelFunc:Hee},qee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Xee=`
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;
`,Kee=it({opSnippet:qee,packedOpSnippet:Xee}),Zee={kernelName:Pa,backendName:"webgl",kernelFunc:Kee},Yee="return (b >= 1.0) ? a : a * (b + 1.0);",Jee=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Qee=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(Jee,s.shape,r.shape):new lc(Yee,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},ete={kernelName:Ch,backendName:"webgl",kernelFunc:Qee},tte=`
return vec4(equal(a, b));
`,nte="return float(a == b);",ste=En({opSnippet:nte,packedOpSnippet:tte,dtype:"bool",cpuKernelImpl:VZ}),rte={kernelName:li,backendName:"webgl",kernelFunc:ste},ate=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${E.ERF_P};
float a1 = ${E.ERF_A1};
float a2 = ${E.ERF_A2};
float a3 = ${E.ERF_A3};
float a4 = ${E.ERF_A4};
float a5 = ${E.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,ote=it({opSnippet:ate}),ite={kernelName:su,backendName:"webgl",kernelFunc:ote},eS="return exp(x);",tS=it({opSnippet:eS,packedOpSnippet:eS,cpuKernelImpl:UZ}),lte={kernelName:Fa,backendName:"webgl",kernelFunc:tS};function bx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),be({inputs:{x:a},backend:s,attrs:{shape:i}})}var ute={kernelName:ui,backendName:"webgl",kernelFunc:bx},nS="return exp(x) - 1.0;",cte=it({opSnippet:nS,packedOpSnippet:nS,cpuKernelImpl:GZ}),dte={kernelName:ci,backendName:"webgl",kernelFunc:cte},sS=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function rS(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=be({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new sS("real",l,t),u=new sS("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Mo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=be({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function pte(e){let{inputs:t,backend:n}=e,{input:s}=t;return rS(s,!1,n)}var hte={kernelName:Th,backendName:"webgl",kernelFunc:pte},fte=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function gp(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new fte(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var mte={kernelName:ru,backendName:"webgl",kernelFunc:gp},gte=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},yte={kernelName:di,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new gte(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},aS="return floor(x);",Ate=it({opSnippet:aS,packedOpSnippet:aS,cpuKernelImpl:HZ}),xte={kernelName:Oa,backendName:"webgl",kernelFunc:Ate},bte=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,vte=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,wte=En({opSnippet:bte,packedOpSnippet:vte,dtype:"int32"}),kte={kernelName:Ma,backendName:"webgl",kernelFunc:wte},Ite=class{constructor(e){this.variableNames=["A"];let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},Ste=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.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;
}
`}},Cte={kernelName:ad,backendName:"webgl",kernelFunc:Tte},dc;function Tte(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(dc==null&&(dc=document.createElement("canvas").getContext("2d")),dc.canvas.width=l,dc.canvas.height=c,dc.drawImage(r,0,0,l,c),r=dc.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=Ls.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Z().getBool("WEBGL_PACK")?new Ste(d):new Ite(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function Nte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=q4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=X4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",S=h?Vm(h,!1):null,N=new j4(g,b,S,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(P),A.push(P)}y=n.runWebGLProgram(N,R,"float32")}let x=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Ete={kernelName:mo,backendName:"webgl",kernelFunc:Nte};function Rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=E.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=p?Vm(p,y):null,x=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&x.push(o),w&&x.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));x.push(P),f.push(P)}let S;y?S=new Q4(g,b,A,w,k):S=new J4(g,b,A,w,k);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(S,x,"float32",N);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),R}var $te={kernelName:go,backendName:"webgl",kernelFunc:Rte},Dte=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=St(t.length),r=St(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${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 * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function _te(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),A=n.bufferSync(s),x=jZ(y,A,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new Dte(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=be({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Pte={kernelName:hi,backendName:"webgl",kernelFunc:_te},Fte=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=St(this.rank),s=Ote(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Ote(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function oS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=be({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=be({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let A=n.bufferSync(h),x=n.bufferSync(p),b=qZ(x,A,f);return d.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new Fte(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var Mte={kernelName:pi,backendName:"webgl",kernelFunc:oS},zte="return float(a > b);",Lte=`
return vec4(greaterThan(a, b));
`,Bte=En({opSnippet:zte,packedOpSnippet:Lte,cpuKernelImpl:XZ,dtype:"bool"}),Wte={kernelName:fi,backendName:"webgl",kernelFunc:Bte},Vte="return float(a >= b);",Ute=`
return vec4(greaterThanEqual(a, b));
`,Gte=En({opSnippet:Vte,packedOpSnippet:Ute,dtype:"bool",cpuKernelImpl:KZ}),Hte={kernelName:La,backendName:"webgl",kernelFunc:Gte};function jte(e){let{inputs:t,backend:n}=e,{input:s}=t;return rS(s,!0,n)}var qte={kernelName:Nh,backendName:"webgl",kernelFunc:jte},Xte="return float(!isnan(x) && !isinf(x));",Kte=it({opSnippet:Xte,dtype:"bool"}),Zte={kernelName:au,backendName:"webgl",kernelFunc:Kte},Yte="return float(isinf(x));",Jte=it({opSnippet:Yte,dtype:"bool"}),Qte={kernelName:ou,backendName:"webgl",kernelFunc:Jte},ene="return float(isnan(x));",tne=it({opSnippet:ene,dtype:"bool"}),nne={kernelName:iu,backendName:"webgl",kernelFunc:tne},sne="return float(a < b);",rne=`
return vec4(lessThan(a, b));
`,ane=En({opSnippet:sne,packedOpSnippet:rne,cpuKernelImpl:ZZ,dtype:"bool"}),one={kernelName:gi,backendName:"webgl",kernelFunc:ane},ine="return float(a <= b);",lne=`
return vec4(lessThanEqual(a, b));
`,une=En({opSnippet:ine,packedOpSnippet:lne,cpuKernelImpl:YZ,dtype:"bool"}),cne={kernelName:yi,backendName:"webgl",kernelFunc:une};function dne(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=JZ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var pne={kernelName:Eh,backendName:"webgl",kernelFunc:dne},hne=`if (x < 0.0) return NAN;
return log(x);`,fne=`
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;
`,mne=it({opSnippet:hne,packedOpSnippet:fne,cpuKernelImpl:QZ}),gne={kernelName:Wa,backendName:"webgl",kernelFunc:mne},yne="return log(1.0 + x);",Ane=it({opSnippet:yne}),xne={kernelName:lu,backendName:"webgl",kernelFunc:Ane},bne="return float(a >= 1.0 && b >= 1.0);",vne=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,wne=En({opSnippet:bne,packedOpSnippet:vne,dtype:"bool"}),kne={kernelName:Ai,backendName:"webgl",kernelFunc:wne},Ine="return float(!(x >= 1.0));",Sne=it({opSnippet:Ine}),Cne={kernelName:uu,backendName:"webgl",kernelFunc:Sne},Tne="return float(a >= 1.0 || b >= 1.0);",Nne=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,Ene=En({opSnippet:Tne,packedOpSnippet:Nne,dtype:"bool"}),Rne={kernelName:Jc,backendName:"webgl",kernelFunc:Ene},$ne=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${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 = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},Dne=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * 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 - ${a};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${i};
setOutput(result);
}
`}},_ne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Dne(r.shape,a,o,i,l):new $ne(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Pne={kernelName:Qc,backendName:"webgl",kernelFunc:_ne},Fne=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* 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);
}
`}},One=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Fne(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Mne={kernelName:Rh,backendName:"webgl",kernelFunc:One};function zne(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=vl(i,e.dtype,"max",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function iS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[u[S]];let w=mx(x,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=Um(r,u,n);c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("max",c,i);let[f,m]=E.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=E.expandShapeToKeepDim(f,l));let y;if(p){let x=n.texData.get(h.dataId).values,b=eY(x,v.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=b}else y=zne(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var Lne={kernelName:Va,backendName:"webgl",kernelFunc:iS},Bne=S4+`
return max(a, b);
`,Wne=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Wm+`
return result;
`,Vne=En({opSnippet:Bne,packedOpSnippet:Wne,cpuKernelImpl:tY}),Une={kernelName:Ua,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Cs({inputs:{x:r},backend:n});let d=new fp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Hne={kernelName:Ga,backendName:"webgl",kernelFunc:Gne};function jne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new yx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var qne={kernelName:ed,backendName:"webgl",kernelFunc:jne},Xne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${a}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Kne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${d}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${i};
wD += ${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 < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(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 * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Zne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new yx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Kne(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Yne={kernelName:Dh,backendName:"webgl",kernelFunc:Zne};function Jne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;tc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new fp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Xne(p),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var Qne={kernelName:$h,backendName:"webgl",kernelFunc:Jne};function ese(e,t,n,s){let r=new fp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new fp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var tse={kernelName:_h,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(E.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=E.computePool2DInfo(s.shape,r,a,c,o),[d,p]=ese(s,i,u,l);return[d,p]}};function nse(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=vl(i,"float32","mean",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var sse={kernelName:Ha,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let N=0;N<w.length;N++)w[N]=s.shape[u[N]];let k=mx(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=Um(s,u,o);h.push(f),c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=E.computeOutAndReduceShapes(f.shape,c),y=m;r&&(y=E.expandShapeToKeepDim(m,l));let A=nse(f,g,y,o);for(let x of h)o.disposeIntermediateTensorInfo(x);return A}};function rse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=vl(m,m.dtype,"min",n),y;if(o){let A=E.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var ase={kernelName:ja,backendName:"webgl",kernelFunc:rse},ose=S4+`
return min(a, b);
`,ise=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Wm+`
return result;
`,lse=En({opSnippet:ose,packedOpSnippet:ise,cpuKernelImpl:nY}),use={kernelName:qa,backendName:"webgl",kernelFunc:lse},cse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let s=e.length,r=St(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${r} coords = outC - start;
setOutput(getX(${i}));
}
`}},dse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=St(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}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 - ${d}) +
gte * ((end - 1) * 2 - source + ${d});
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},pse=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dse(s.shape,r,a):new cse(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},hse={kernelName:Xa,backendName:"webgl",kernelFunc:pse},fse=`if (b == 0.0) return NAN;
return mod(a, b);`,mse=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Wm+`
return result;
`,gse=En({opSnippet:fse,packedOpSnippet:mse}),yse={kernelName:cu,backendName:"webgl",kernelFunc:gse},Ase=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},xse=`
if (a == b) {
return 1.0;
};
return a / b;`,bse=`
// 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;
`,lS=En({opSnippet:xse,packedOpSnippet:bse,checkOutOfBounds:!0}),vse={kernelName:_a,backendName:"webgl",kernelFunc:lS},uS="return a - b;",cS=En({opSnippet:uS,packedOpSnippet:uS,supportsComplex:!0,cpuKernelImpl:AY}),wse={kernelName:uo,backendName:"webgl",kernelFunc:cS};function dS(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=iS({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=be({inputs:{x:i},backend:n,attrs:{shape:l}}),u=cS({inputs:{a:r,b:c},backend:n}),d=tS({inputs:{x:u},backend:n}),p=Gm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=be({inputs:{x:p},backend:n,attrs:{shape:l}}),f=lS({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var kse={kernelName:io,backendName:"webgl",kernelFunc:dS};function Ise(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:dS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new Ase(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Sse={kernelName:Ph,backendName:"webgl",kernelFunc:Ise},pS="return -x;";function Cse(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=rY(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new oc(s.shape,pS):r=new Oo(s.shape,pS),n.runWebGLProgram(r,[s],s.dtype)}var Tse={kernelName:xi,backendName:"webgl",kernelFunc:Cse},Nse=Zs.nonMaxSuppressionV3Impl;function Ese(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Nse(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Rse={kernelName:vi,backendName:"webgl",kernelFunc:Ese},$se=Zs.nonMaxSuppressionV4Impl;function Dse(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=$se(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var _se={kernelName:du,backendName:"webgl",kernelFunc:Dse},Pse=Zs.nonMaxSuppressionV5Impl;function Fse(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Pse(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Ose={kernelName:wi,backendName:"webgl",kernelFunc:Fse},Mse=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}},zse=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new Mse(l,a,o,i),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=be({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},Lse={kernelName:Ii,backendName:"webgl",kernelFunc:zse};function Km(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=Km({inputs:{x:r},backend:n}),o=Xm({inputs:{input:s},backend:n}),i=Km({inputs:{x:o},backend:n}),l=Mo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Bse={kernelName:Bi,backendName:"webgl",kernelFunc:Km};function hS(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=hS({inputs:{x:r},backend:n}),o=Xm({inputs:{input:s},backend:n}),i=Km({inputs:{x:o},backend:n}),l=Mo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Wse={kernelName:ki,backendName:"webgl",kernelFunc:hS};function Vse(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return bx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=bx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=H4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Use={kernelName:Si,backendName:"webgl",kernelFunc:Vse},Gse=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=St(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},Hse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=St(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
if(${c}) {
`,s===1?"":`}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},fS=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return gp({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hse(r.shape,a,o):new Gse(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},jse={kernelName:Za,backendName:"webgl",kernelFunc:fS},qse=`
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);
`,Xse=`
// 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));
`+Wm+`
return result;
`,Kse=En({opSnippet:qse,packedOpSnippet:Xse}),Zse={kernelName:Ya,backendName:"webgl",kernelFunc:Kse};function Yse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=E.getAxesPermutation(u,i),p=r;d!=null&&(p=jn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=E.getInnerMostAxes(u.length,i),l.push(p)),E.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=oY(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=E.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),y=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),A=fd(r.dtype),x=vl(y,A,"prod",n);h=be({inputs:{x},backend:n,attrs:{shape:f}}),l.push(y),l.push(x)}if(o){l.push(h);let f=E.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Jse={kernelName:Ci,backendName:"webgl",kernelFunc:Yse},mS=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=iY(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Qse={kernelName:pu,backendName:"webgl",kernelFunc:mS},ere="return 1.0 / x;",tre=it({opSnippet:ere}),nre={kernelName:hu,backendName:"webgl",kernelFunc:tre},sre=br+`
return (x < 0.0) ? 0.0 : x;
`,rre=`
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;
`,are=it({opSnippet:sre,packedOpSnippet:rre}),ore={kernelName:Qa,backendName:"webgl",kernelFunc:are},ire=br+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,lre=`
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;
`,ure=it({opSnippet:ire,packedOpSnippet:lre}),cre={kernelName:to,backendName:"webgl",kernelFunc:ure},dre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${d};
// Compute the 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);
}
`}},pre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${d};
// Compute the 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 < ${l-1};
bool hasNextRow = coords.z < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pre(r.shape,l,c,a,o):new dre(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var fre={kernelName:eo,backendName:"webgl",kernelFunc:hre},mre=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-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 gre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new mre(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var yre={kernelName:Oh,backendName:"webgl",kernelFunc:gre},Are=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},xre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function bre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new xre(r.shape,l,c,a,o):new Are(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var vre={kernelName:fu,backendName:"webgl",kernelFunc:bre},wre=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
const int winWidth = int(${f});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${a}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function kre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new wre(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Ire={kernelName:Fh,backendName:"webgl",kernelFunc:kre},Sre=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=St(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},Cre=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-${n} tensor is not yet supported`);this.outputShape=e;let s=Hn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=St(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${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() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(s.slice())};
if(${r}){
result.g = ${l(s.slice())};
}
if(${a}) {
result.b = ${c(s.slice())};
if(${r}) {
result.a = ${u(s.slice())};
}
}
setOutput(result);
}
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((y,A)=>p(A,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Tre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Cs({inputs:{x:r},backend:n});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Cre(r.shape,i):new Sre(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Nre={kernelName:Ni,backendName:"webgl",kernelFunc:Tre},Ere=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=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=`
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 < ${s} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},Rre={kernelName:Wi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Ere(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},$re=`
// 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;
}
}
`,Dre=it({opSnippet:$re}),_re={kernelName:Ei,backendName:"webgl",kernelFunc:Dre},Pre="return inversesqrt(x);",Fre=it({opSnippet:Pre,cpuKernelImpl:lY}),Ore={kernelName:no,backendName:"webgl",kernelFunc:Fre},gS=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=St(r.length),l=St(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
${i} strides = ${i}(${r});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=be({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new gS(l,i,h.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(g,[f,h,m],f.dtype),A=be({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),A}var zre={kernelName:Ri,backendName:"webgl",kernelFunc:Mre},Lre=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);s=i.join(),r=l.join()}let a=St(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function Bre(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Lre(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var Wre={kernelName:$i,backendName:"webgl",kernelFunc:Bre},Vre=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${E.SELU_SCALEALPHA};
float scale = ${E.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Ure=it({opSnippet:Vre}),Gre={kernelName:mu,backendName:"webgl",kernelFunc:Ure},yS="return 1.0 / (1.0 + exp(-1.0 * x));",Hre=it({opSnippet:yS,packedOpSnippet:yS,cpuKernelImpl:uY}),jre={kernelName:ro,backendName:"webgl",kernelFunc:Hre},qre=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Xre=it({opSnippet:qre}),Kre={kernelName:gu,backendName:"webgl",kernelFunc:Xre},Zre=R4+`
return sin(x);
`,Yre=it({opSnippet:Zre}),Jre={kernelName:so,backendName:"webgl",kernelFunc:Yre},Qre=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,eae=it({opSnippet:Qre}),tae={kernelName:_i,backendName:"webgl",kernelFunc:eae},nae=`
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;
`,sae=it({opSnippet:nae}),rae={kernelName:yu,backendName:"webgl",kernelFunc:sae},aae=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,A)=>y*A),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=fS({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=be({inputs:{x:u},backend:n,attrs:{shape:d}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=be({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},oae={kernelName:Pi,backendName:"webgl",kernelFunc:aae};function iae(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=dY(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var lae={kernelName:Mh,backendName:"webgl",kernelFunc:iae};function uae(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=pY(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var cae={kernelName:zh,backendName:"webgl",kernelFunc:uae};function dae(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=y4(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var pae={kernelName:Lh,backendName:"webgl",kernelFunc:dae};function hae(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=y4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var fae={kernelName:Bh,backendName:"webgl",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=new gS(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=be({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var gae={kernelName:nd,backendName:"webgl",kernelFunc:mae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=uc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Aae={kernelName:Fi,backendName:"webgl",kernelFunc:yae},AS="return sqrt(x);",xae=it({opSnippet:AS,packedOpSnippet:AS,cpuKernelImpl:hY}),bae={kernelName:ao,backendName:"webgl",kernelFunc:xae},vae="return x * x;",wae=it({opSnippet:vae}),kae={kernelName:Au,backendName:"webgl",kernelFunc:wae},xS="return (a - b) * (a - b);",Iae=En({opSnippet:xS,packedOpSnippet:xS}),Sae={kernelName:lo,backendName:"webgl",kernelFunc:Iae};function Cae({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=br+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Oo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Tae={kernelName:ho,backendName:"webgl",kernelFunc:Cae},Nae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=St(n.length),a=St(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function Eae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=yn.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=be({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=uc({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=be({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(k)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let N=n.texData.get(x.dataId).values,R=We(x.shape,x.dtype,N),P=fY(A,R,m,f);b=n.makeTensorInfo(A,x.dtype,P.values)}else{let S=new Nae(f,m,A);b=n.runWebGLProgram(S,[x],x.dtype)}let w=be({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),w}var Rae={kernelName:Oi,backendName:"webgl",kernelFunc:Eae};function $ae(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=mY(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Dae={kernelName:sd,backendName:"webgl",kernelFunc:$ae};function _ae(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=gY(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Pae={kernelName:Wh,backendName:"webgl",kernelFunc:_ae};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=yY(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Oae={kernelName:Vh,backendName:"webgl",kernelFunc:Fae},Mae="return tan(x);",zae=it({opSnippet:Mae}),Lae={kernelName:Mi,backendName:"webgl",kernelFunc:zae},Bae=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Wae=it({opSnippet:Bae}),Vae={kernelName:co,backendName:"webgl",kernelFunc:Wae},Uae=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=St(this.rank),r=Gae(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Gae(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function bS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=We(r.shape,r.dtype,c),d=xY(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Uae(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Hae={kernelName:Kr,backendName:"webgl",kernelFunc:bS},jae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},qae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function wl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function vS(e){let t=1;for(;t<e;)t*=2;return t}function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let P=n.readSync(r.dataId),[$,D]=bY(P,c,r.dtype,a,o);return[n.makeTensorInfo($.shape,$.dtype,$.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,gp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=be({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&wl(n,h);let y=vS(a),A=vS(u),x=null,b=()=>x===null?[g,g]:[g,x],w=(P,$,D)=>{let T=b(),O=new jae(D),H=[[u],[x===null?1:0],[Number.NEGATIVE_INFINITY],[P],[$]],z=x;x=n.runWebGLProgram(O,T,"int32",H),wl(n,z)};for(let P=1;P<y;P*=2){let $=P*2;for(let D=P;D>=1;D/=2)w($,D,[m,A])}for(let P=A;P>y;P/=2){let $=b(),D=new qae([m,P/2]),O=[[u],[x===null?1:0],[y]],B=x;x=n.runWebGLProgram(D,$,"int32",O),wl(n,B);let H=y/2,z=H*2;for(let X=H;X>=1;X/=2)w(z,X,x.shape)}let k=x;x=uc({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),wl(n,k);let S=oS({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});wl(n,g);let N=c.slice(0,-1);N.push(a),k=x,x=be({inputs:{x},attrs:{shape:N},backend:n}),wl(n,k);let R=S;return S=be({inputs:{x:S},attrs:{shape:N},backend:n}),wl(n,R),[S,x]}var Kae={kernelName:xu,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${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(${e}));
if (${o} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function Yae(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Zae(d,p,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var Jae={kernelName:zi,backendName:"webgl",kernelFunc:Yae};function Qae(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=vY(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var eoe={kernelName:Uh,backendName:"webgl",kernelFunc:Qae};function toe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=uc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=be({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var noe={kernelName:Li,backendName:"webgl",kernelFunc:toe},soe=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=`
sumValue += dot(values, segFilter);
`,p="";r%n>0&&(p=`
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 = ${i};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${d}
}
int inIdx = inOffset + ${c};
if (${u===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
);
${d}
} else if (${u===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
);
${d}
} else if (${u===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
);
${d}
}
setOutput(${l});
}
`}};function roe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=E.getInnerMostAxes(1,i)[0]);let p=E.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=be({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=fd(r.dtype),g=(b,w,k,S,N)=>{let R=b.shape[0],P=b.shape[1],$=E.segment_util.segOpComputeOptimalWindowSize(P,N),D={windowSize:$,inSize:P,batchSize:R,numSegments:N},T=new soe(D,w),O=n.compileAndRun(T,[b,k],S);if(l.push(O),O.shape[1]===N)return O;let B=mS({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),H=bS({inputs:{x:B},backend:n,attrs:{reps:[P/$]}});return l.push(B),l.push(H),g(O,w,H,S,N)},y=g(f,"unsortedSegmentSum",a,m,o),A=be({inputs:{x:y},backend:n,attrs:{shape:p}}),x=A;if(u!=null){l.push(A);let b=E.getUndoAxesPermutation(u);x=jn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var aoe={kernelName:rd,backendName:"webgl",kernelFunc:roe},ooe=[Pne,Mne,bJ,wJ,SJ,NJ,RJ,_J,FJ,MJ,WJ,UJ,jJ,KJ,nQ,JJ,aQ,uQ,iQ,hQ,mQ,yQ,vQ,NQ,RQ,OQ,zQ,VQ,HQ,tJ,ZQ,oee,lee,eee,pee,fee,cee,yee,bee,kee,See,Tee,Ree,Oee,zee,Dee,Wee,Gee,jee,Zee,ete,rte,ite,lte,ute,dte,hte,mte,yte,xte,kte,Cte,Ete,$te,Pte,Mte,Wte,Hte,eJ,qte,XQ,Zte,Qte,nne,sJ,one,cne,pne,xne,gne,kne,Cne,Rne,Lne,qne,Hne,Yne,Qne,tse,Une,sse,ase,use,hse,yse,Sse,lJ,Tse,Rse,_se,Ose,DQ,Lse,Wse,Use,jse,Zse,aJ,Jse,Qse,_Q,vse,nre,cre,ore,cJ,fre,yre,vre,Ire,Nre,Rre,_re,Ore,zre,Wre,Gre,jre,Kre,Jre,tae,CQ,kse,rae,oae,lae,cae,pae,fae,gae,Aae,bae,kae,Sae,Tae,Rae,Dae,Pae,Oae,wse,yJ,Lae,Vae,Hae,Kae,Jae,AJ,eoe,noe,aoe,Bse];for(let e of ooe)Yr(e);var ls;(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"})(ls||(ls={}));var yp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(yp||(yp={}));var wS;function ioe(e){wS=e.wasm.cwrap(fo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function loe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let N=n.dataIdMap.get(o.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=yp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the 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IS(u,h,l.shape.length,ls[l.dtype],d,p,a.length),c}function Aoe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function xoe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var boe={kernelName:po,backendName:"wasm",kernelFunc:pc,setupFunc:yoe};function zo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=E.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h<u.length;h++)u[h]=s[i[h]];o=E.getInnerMostAxes(o.length,r),l=pc({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(c=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:c}}var SS;function voe(e){SS=e.wasm.cwrap(Kl,null,["number, number, number"])}function 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f=c.shape.length;E.assertAxesAreInnerMostDims("any",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;CS(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Coe={kernelName:Zl,backendName:"wasm",setupFunc:Ioe,kernelFunc:Soe},TS;function Toe(e){TS=e.wasm.cwrap(ka,null,["number","number","number","number","number"])}function Noe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:c,axes:u,inputWasTransposed:d}=zo(a,r,t);if(d){let y=t.dataIdMap.get(c.dataId).id;y!==o&&(l=c,i=y)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[u[0]];return TS(i,ls[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var Eoe={kernelName:ka,backendName:"wasm",kernelFunc:Noe,setupFunc:Toe},NS;function Roe(e){NS=e.wasm.cwrap(Ia,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,A=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=s.makeOutput(u.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return NS(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,y,A,x,w),b}var Doe={kernelName:Ia,backendName:"wasm",setupFunc:Roe,kernelFunc:$oe};function us(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. 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Ap(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=yn.parseSliceParams(t,n,s),i=yn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),c=r.makeOutput(o,t.dtype),u=v.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(i){let f=yn.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(o))),c}if(t.dtype==="string"){let f=Cm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Moe(l,u[0],p,a,o);else if(h===3)zoe(l,u[0],u[1],p,a,o);else if(h===4)Loe(l,u[0],u[1],u[2],p,a,o);else{let f=Cm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function Moe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;c<l;c++){let u=c*t+i;n.set(e.subarray(u,u+r[1]),a),a+=r[1]}}function zoe(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],c=r[2],u=i+a[0],d=l+a[1];for(let p=i;p<u;p++)for(let h=l;h<d;h++){let f=p*t+h*n+c;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Loe(e,t,n,s,r,a,o){let i=0,l=a[0],c=a[1],u=a[2],d=l+o[0],p=c+o[1],h=u+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=c;g<p;g++)for(let y=u;y<h;y++){let A=m*t+g*n+y*s+f;r.set(e.subarray(A,A+o[3]),i),i+=o[3]}}var Boe={kernelName:Di,backendName:"wasm",kernelFunc:Ap};function Woe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((y,A)=>y*A),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=us({inputs:{x:r},backend:n,attrs:{shape:l}}),f=pc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=us({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Ap({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Voe={kernelName:si,backendName:"wasm",kernelFunc:Woe};function Ym(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Uoe={kernelName:Ca,backendName:"wasm",kernelFunc:Ym},Goe=Rn(Ta),RS;function Hoe(e){RS=e.wasm.cwrap(Xr,null,["number","number","number","number"])}function joe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return RS(i,a,o,c),l}var qoe={kernelName:Xr,backendName:"wasm",setupFunc:Hoe,kernelFunc:joe};function $S(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return Zm({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(E.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=v.sizeFromShape(x.shape.slice(s));return us({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=UA(f,r,t[0].dtype,m),y=E.computeOutShape(a.map(x=>x.shape),s);o.shape=y;let A=n.dataIdMap.get(o.dataId);return A.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=u[m],y=h*g,A=d[m].subarray(y,y+g);p.set(A,f),f+=g}}return o}var Xoe={kernelName:ri,backendName:"wasm",kernelFunc:$S},DS;function Koe(e){DS=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zoe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d,dataFormat:p}=n,h=E.convertConv2DDataFormat(p),f=E.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,N=f.strideWidth,R=f.inChannels,P=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',cce=Qo(dN()),xC=class extends Gl{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Vc(this,ts())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let c=t;this.dataIdMap.set(e,{id:a,stringBytes:c,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:s,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let a=this.wasm.HEAPU8.slice(t,t+v.sizeFromShape(s)*v.bytesPerElement(n));return hce(a.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in 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e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),bp!=null&&bp[s]!=null?bp[s]:n+s}async function pce(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let c=uce,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?bC(e,t,xp!=null?xp:l):l+i},Cx&&(r.instantiateWasm=dce(bC(e,t,xp!=null?xp:"")));let a=!1;r.onAbort=()=>{if(a||vp)return;vp=!0,s({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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let index = getGlobalIndex(globalId, localId);
`}function Me(){return`
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>, [[builtin(global_invocation_id)]] globalId : vec3<u32>)
`}function xce(e,t,n,s=!1){let r=`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=IC(t.shape),f=`
[[block]] struct Matrix0 {
numbers: array<${Qm(t.dtype,n.isVec4)}>;
};
[[block]] struct Uniform {
size : i32;
numChannels : i32;
outShapeStrides : vec2<i32>;
dispatchSize : vec3<u32>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
`;return[wC,f,r,kC,h,n.getUserCode()].join(`
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${ln(e[f].shape.length)}; `}),o+=`outShape : ${ln(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
outShapeStrides: ${ln(i)}; `,n.size!=null&&(o+="size : i32; "),o+="dispatchSize : vec3<u32>; ",n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),a.push(`
[[block]] struct Matrix0 {
numbers: array<${Qm(t.dtype,n.isVec4)}>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
`),n.variableNames.forEach((h,f)=>{a.push(`
[[block]] struct Matrix${1+f} {
numbers: array<${Qm(e[f].dtype,n.isVec4)}>;
};
[[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
`)}),o!==""&&a.push(`
[[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
`),a.push(r);let[l,c]=Ice(t.shape,n.dispatchLayout),u=IC(t.shape),d=[wC,a.join(`
`),kC,u,l,bce(t.shape,t.dtype,n.isVec4)];if(c===t.shape.length){let h=e.map(f=>vce(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
`)}var wC=`
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
fn isNanCustom(val : f32) -> bool {
if (val > 0.0) {
return false;
}
if (val < 0.0) {
return false;
}
if (val == 0.0) {
return false;
}
return true;
}
fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
var res = vec4<f32> (0.0);
for (var i = 0u; i < 4u; i = i + 1u) {
if (isNanCustom(val[i])) {
res[i] = 1.0;
} else {
res[i] = 0.0;
}
}
return res;
}
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) &&
all(coord < shape);
}
`,kC=`
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(shape.y), 1.0)));
}
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0)));
}
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0)));
}
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex(globalId : vec3<u32>, localId : vec3<u32>) -> i32 {
if (uniforms.dispatchSize.y == 1u && uniforms.dispatchSize.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * uniforms.dispatchSize.x * uniforms.dispatchSize.y +
workGroupID.y * uniforms.dispatchSize.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`;function bce(e,t,n){let s=e.length,r=Qm(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
result.numbers[flatIndex] = ${r}(value);
}`:a=`fn setOutputFlat(flatIndex : i32, value : f32) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : i32) {
result.numbers[flatIndex] = ${r}(value);
}`,s>=2){switch(s){case 2:a+=`
fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(uniforms.outShapeStrides), 1.0)));
}
`;break;case 3:a+=`
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0)));
}
`;break;case 4:a+=`
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0)));
}
`;break;default:v.assert(!1,()=>`Unsupported ${s}D shape`);break}let o=["d0","d1","d2","d3"].slice(0,s),i=ln(s);n?a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex / 4, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlatI32(flatIndex / 4, value);
}
`:a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlatI32(flatIndex, value);
}
`}return a}function vce(e,t,n,s){let r=wce(e,n);return e.shape.length<=t.length&&(r+=kce(e,t,n,s)),r}function wce(e,t){let n=e.name,s=e.shape.length,r=ln(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}.numbers[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}.numbers[0]);
}
`;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?`
fn ${a}(${i}) -> vec4<f32> {
return vec4<f32>(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l}) / 4]);
}
`:`
fn ${a}(${i}) -> f32 {
return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l})]);
}
`}function kce(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=ln(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return vec4<f32>(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
return f32(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> f32 {
return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]);
}
`;let u=E.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32{
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> f32{
return get${a}();
}
`;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(`
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=ln(i),y=e.shape.map((A,x)=>`coords[${x+d}]`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
fn ${o}ByCoords(coordsIn : ${c}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
}
fn ${o}ByCoords(coordsIn : ${c}) -> f32 {
var coords = coordsIn;
${p}
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
}
`}function Ice(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${ln(a)}{
return getCoordsFromFlatIndex(i32(globalIndex));
}
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Ace(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=ln(l),d=`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${u} {
${o}
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function IC(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=ln(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return`
fn getCoordsFromFlatIndex(index : i32) -> ${s} {
${a}
return ${s}(${r.join(",")});
}
`}var SC={};Le(SC,{ArrayBufferToTypedArray:()=>CC,GPUBytesPerElement:()=>Rx,computeDispatch:()=>Be,computeWorkGroupSizeForConv2d:()=>Tx,computeWorkGroupSizeForMatMul:()=>Nx,computeWorkPerThreadForConv2d:()=>Ex,flatDispatchLayout:()=>at,isWebGPUSupported:()=>$x,tilesFitEvenlyIntoShape:()=>ua});var hc=65535,kl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ua(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]==0)}function Be(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(kl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(kl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(kl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=hc&&a<=hc&&o<=hc)return[r,a,o];v.assert(r>hc&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>hc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=hc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function Tx(e,t){let n=kl(e.x.map(r=>t[r])),s=kl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Nx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Ex(e,t){let n=kl(e.x.map(r=>t[r])),s=kl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function at(e){return{x:e.map((t,n)=>n)}}function Rx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function CC(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function $x(){return!!navigator.gpu}var je;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(je||(je={}));var Sce="return a + b;",Cce="return areal * breal - aimag * bimag;",Tce="return areal * bimag + aimag * breal;",Nce="return a / b;",Ece="return a * b;",Rce="return (a - b) * (a - b);",$ce="return a - b;",Dce="return f32(a == b);",_ce="return vec4<f32>(a == b);",Pce="return f32(a > b);",Fce="return vec4<f32>(a > b);",Oce="return f32(a >= b);",Mce="return vec4<f32>(a >= b);",zce="return f32(a < b);",Lce="return vec4<f32>(a < b);",Bce="return f32(a <= b);",Wce="return vec4<f32>(a <= b);",Vce="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Uce=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,Gce=`
if (isNanCustom(a)) { return a; }
if (isNanCustom(b)) { return b; }
`,TC=`
if (isNaN.r > 0.) {
resultTemp.r = uniforms.NAN;
}
if (isNaN.g > 0.) {
resultTemp.g = uniforms.NAN;
}
if (isNaN.b > 0.) {
resultTemp.b = uniforms.NAN;
}
if (isNaN.a > 0.) {
resultTemp.a = uniforms.NAN;
}
`,Hce=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,jce=`
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`,qce="return f32(a != b);",Xce="return vec4<f32>(a != b);",Kce=`
if(a < 0.0 && floor(b) < b) {
return uniforms.NAN;
}
if (b == 0.0) {
return 1.0;
}
if (round(abs(b) % 2.0) != 1.0) {
return pow(abs(a), b);
}
return sign(a) * pow(abs(a), b);
`,Zce=`
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
${TC}
return resultTemp;
`,Yce="if (a < 0.0) { return b * a; } return a;",Jce=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function NC(e,t){let n=t?TC:Gce;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function wp(e,t){switch(e){case je.MUL:return Ece;case je.ADD:return Sce;case je.SUB:return $ce;case je.DIV:return Nce;case je.EQUAL:return t?_ce:Dce;case je.GREATER:return t?Fce:Pce;case je.GREATER_EQUAL:return t?Mce:Oce;case je.LESS:return t?Lce:zce;case je.LESS_EQUAL:return t?Wce:Bce;case je.LOGICAL_AND:return t?Uce:Vce;case je.NOT_EQUAL:return t?Xce:qce;case je.SQUARED_DIFFERENCE:return Rce;case je.INT_DIV:return t?jce:Hce;case je.PRELU:return t?Jce:Yce;case je.MAX:return NC("max",t);case je.MIN:return NC("min",t);case je.POW:return t?Zce:Kce;case je.COMPLEX_MULTIPLY_REAL:return Cce;case je.COMPLEX_MULTIPLY_IMAG:return Tce;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Fe;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(Fe||(Fe={}));var Qce="return abs(a);",ede="return ceil(a);",tde="return cos(a);",nde=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,sde="return exp(a) - 1.0;",rde="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",ade=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,ode="return exp(a);",ide="return floor(a);",lde="return a;",ude=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,cde="return f32(!(a >= 1.0));",dde="return -a;",pde="return (a < 0.0) ? b * a : a;",hde="return max(a, 0.0);",fde="return clamp(a, 0.0, 6.0);",mde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",gde=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isNan(a);
if (isNaN.r) {
resFloat.r = a.r;
}
if (isNaN.g) {
resFloat.g = a.g;
}
if (isNaN.b) {
resFloat.b = a.b;
}
if (isNaN.a) {
resFloat.a = a.a;
}
return resFloat;
`,yde="return 1.0/sqrt(a);",Ade="return 1.0 / (1.0 + exp(-1.0 * a));",xde="return sin(a);",bde=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,vde="return sqrt(a);",wde="return a * a;",kde=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Ide="return f32(i32((a)));";function fc(e,t){switch(e){case Fe.ABS:return Qce;case Fe.COS:return tde;case Fe.COSH:return nde;case Fe.CEIL:return ede;case Fe.ELU:return t?ade:rde;case Fe.EXP:return ode;case Fe.EXPM1:return sde;case Fe.FLOOR:return ide;case Fe.LINEAR:return lde;case Fe.LOG:return ude;case Fe.LOGICAL_NOT:return cde;case Fe.NEG:return dde;case Fe.PRELU:return pde;case Fe.RELU:return t?gde:hde;case Fe.RELU6:return t?mde:fde;case Fe.RSQRT:return yde;case Fe.SIGMOID:return Ade;case Fe.SIN:return xde;case Fe.SINH:return bde;case Fe.SQRT:return vde;case Fe.SQUARE:return wde;case Fe.TANH:return kde;case Fe.TO_INT:return Ide;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Lo(e,t=!1){if(e===null)return null;if(e==="linear")return fc(Fe.LINEAR);if(e==="relu")return fc(Fe.RELU,t);if(e==="elu")return fc(Fe.ELU,t);if(e==="relu6")return fc(Fe.RELU6,t);if(e==="prelu")return wp(je.PRELU,t);if(e==="sigmoid")return fc(Fe.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function EC(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;
let RowPerThread = ${n.RowPerThread};
let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
let TileAOuter = ${n.TileAOuter};
let TileBOuter = ${n.TileBOuter};
let TileInner = ${n.TileInner};
${Me()} {
let tileRow = i32(localId.y) * RowPerThread;
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y) * RowPerThread;
let globalCol = i32(globalId.x);
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, ${n.RowPerThread}>;
var ACached : vec4<f32>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
var globalColA = tileCol;
let RowPerThreadB = TileInner / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
}
globalColA = globalColA + TileInner / ColPerThread;
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
for (var i = 0; i < RowPerThread; i = i + 1) {
ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached[0] * ACached.x + acc[i];
acc[i] = BCached[1] * ACached.y + acc[i];
acc[i] = BCached[2] * ACached.z + acc[i];
acc[i] = BCached[3] * ACached.w + acc[i];
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
mm_write(globalRow + innerRow,
globalCol,
acc[innerRow], globalId);
}
}`}function Sde(e){return`
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
let tileSize = ${e[0]*4};
${Me()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
// Without this initialization strange values show up in acc.
var acc = vec4<f32>(0.0);
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * tileSize / 4 + tileCol;
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < tileSize / 4; k = k + 1) {
let rowB = t * tileSize + k * 4;
let BCached0 = mm_readB(rowB, globalCol, globalId);
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
let ACached = mm_Asub[k];
acc = acc + BCached0 * ACached.x;
acc = acc + BCached1 * ACached.y;
acc = acc + BCached2 * ACached.z;
acc = acc + BCached3 * ACached.w;
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var Cde=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Nx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ua(o,this.aShape.slice(1)),ua(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=Lo(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutput(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${this.outputShape[1]>1?EC([this.vecSize,this.workPerThread,1],this.workGroupSize):Sde(this.workGroupSize)}
`}};function Dx(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
${Me()} {
let tileRow = i32(localId.y) * ${e[1]};
let tileCol = i32(localId.x) * ${e[0]};
let globalRow = i32(globalId.y) * ${e[1]};
let globalCol = i32(globalId.x) * ${e[0]};
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
var ACached : f32;
var BCached : array<f32, ${e[0]}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let ColPerThreadA = ${r} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${r} / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileColA + innerCol;
mm_Asub[inputRow][inputCol] = mm_readA(
globalRow + innerRow,
t * ${r} + inputCol, globalId);
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(
t * ${r} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; k = k + 1) {
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
ACached = mm_Asub[tileRow + innerRow][k];
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
if ((globalCol + innerCol) < uniforms.dimBOuter &&
(globalRow + innerRow) < uniforms.dimAOuter) {
mm_write(globalRow + innerRow,
globalCol + innerCol,
acc[innerRow][innerCol], globalId);
}
}
}
}
`}function Tde(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Me()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId));
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var RC=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=Nx(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ua(r,this.aShape.slice(1)),ua(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let n="",s="";if(this.activation){let o=Lo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutput(batch, row, col, value);
}
${this.outputShape[1]>1?Dx([this.workPerThread,this.workPerThread,1],this.workGroupSize):Tde(this.workGroupSize)}
`}};function Nde(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Introduces two shared memory buffers, some logical threads could handle
// arithmetic operations and others handle IO operations between barrier api,
// makes ALUs and load/store units work simultaneously, could improves
// the performance.
${Me()} {
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = tileRow;
for (var t = 0; t < numTiles; t = t + 1) {
if (t == 0) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
}
} else {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
}
}
}
workgroupBarrier();
if (t != 0) {
t = t + 1;
}
if (t < numTiles) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub2[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
}
}
}
workgroupBarrier();
}
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
if (tileRow >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var Ede=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let o=Lo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutput(batch, row, col, value);
}
}
${Nde(this.workGroupSize)}
`}};function nt(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Rde={kernelName:Ti,backendName:"webgpu",kernelFunc:nt};function _x({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=y===A||y===1||A===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[y,d,h]:[y,h,d],S=s?[A,f,p]:[A,p,f],N=nt({inputs:{x:e},backend:r,attrs:{shape:k}}),R=nt({inputs:{x:t},backend:r,attrs:{shape:S}}),P=[N,R],$=Math.max(y,A),D=d%4==0&&f%4==0&&!n&&!s&&f>=32,T;!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?T=new Ede(k,S,[$,h,f],a,l,o):D?T=new Cde(k,[$,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):T=new RC(k,[$,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let O=[N,R];a&&O.push(a),o&&O.push(o);let B=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],H=r.runWebGPUProgram(T,O,e.dtype,B),z=nt({inputs:{x:H},backend:r,attrs:{shape:w}});P.push(H);for(let X of P)r.disposeData(X.dataId);return z}function $de(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return _x({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Dde={kernelName:fo,backendName:"webgpu",kernelFunc:$de},$C=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${wp(this.op,!1)}
}
${Me()} {
${He()}
if(index < uniforms.size) {
let areal = getARealAtOutCoordsByGlobalId(globalId, index);
let aimag = getAImagAtOutCoordsByGlobalId(globalId, index);
let breal = getBRealAtOutCoordsByGlobalId(globalId, index);
let bimag = getBImagAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},_de=class{constructor(e,t,n,s){this.variableNames=["A","B"];let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%(this.workGroupSize[0]*this.workPerThread)==0,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}_${this.sizeFit}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords);
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
let b = getBAtOutCoordsByCoords(coords);`,n=this.sizeFit?`let coords = getCoordsFromFlatIndex(flatIndex);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));`:`if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));
}`;return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${wp(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Me()} {
${He()}
// Fill in the shared memory buffer. Here we need a loop to make sure
// that all data in A|B are uploaded when |sharedMemorySize| is larger
// than work group size.
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
${n}
}
}
`}},Pde=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.fitShape=this.size%this.workGroupSize[0]==0,this.shaderKey=`binaryVec4_${e}_${this.fitShape}`,this.size=v.sizeFromShape(this.outputShape)/this.workPerThread}getUserCode(){let e,n=`fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${wp(this.op,this.isVec4)}
}`;return this.fitShape?e=`
${n}
${Me()} {
${He()}
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Me()} {
${He()}
if (index < uniforms.size) {
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
}
`,e}},DC=class{constructor(e,t,n){this.variableNames=["A","B"];let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%s==0,this.shapesFit=v.arraysEqual(t,n)&&this.sizeFit,this.workPerThread=this.sizeFit||this.shapesFit?1:2,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=`binary_${e}_${this.sizeFit}_${this.shapesFit}`,this.op=e}getUserCode(){let e,n=` fn binaryOperation(a : f32, b : f32) -> f32 {
${wp(this.op,!1)}
}`;return this.shapesFit?e=`
${n}
${Me()} {
${He()}
let a = f32(A[index]);
let b = f32(B[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:this.sizeFit?e=`
${n}
${Me()} {
${He()}
let coords = getCoordsFromFlatIndex(index);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Me()} {
${He()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1 ) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(flatIndex, binaryOperation(a, b));
}
}
}
`,e}};function _C(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new Pde(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new _de(e,t,n,a):new DC(e,t,n)}function tr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Fde={kernelName:Ba,backendName:"webgpu",kernelFunc:tr};function mc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=tr({inputs:{x:s},backend:n}),l=tr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Ode={kernelName:jc,backendName:"webgpu",kernelFunc:mc},e0=class{constructor(e,t){this.variableNames=["A"];let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.size=v.sizeFromShape(this.outputShape),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${fc(this.op,!1)}
}
${Me()} {
${He()}
if (index < uniforms.size) {
let a = getAAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, unaryOperation(a));
}
}
`}};function $n({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new e0(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function Xn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==je.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:A.dataId,dtype:A.dtype,shape:i.shape},w=_C(e,o.shape,i.shape);return l.runWebGPUProgram(w,[x,b],Ln(y.dtype,A.dtype))});else{let g=new $C(je.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new $C(je.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),A=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=mc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Ln(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?E.fromUint8ToStringArray(d):d,f=o.dtype==="string"?E.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=_C(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Mde,ceilImpl:zde,concatImpl:Lde,equalImpl:Bde,expImpl:Wde,expm1Impl:Vde,floorImpl:Ude,gatherNdImpl:Gde,gatherV2Impl:Hde,greaterEqualImpl:jde,greaterImpl:qde,lessEqualImpl:Xde,lessImpl:Kde,logImpl:Zde,maxImpl:Yde,maximumImpl:Jde,minimumImpl:Qde,multiplyImpl:epe,negImpl:tpe,notEqualImpl:npe,prodImpl:spe,rangeImpl:rpe,rsqrtImpl:ape,simpleAbsImpl:ope,sliceImpl:ipe,stridedSliceImpl:lpe,stringNGramsImpl:upe,subImpl:cpe,tileImpl:dpe,transposeImpl:ppe,uniqueImpl:pge}=BA,hpe=$n({opType:Fe.ABS,cpuKernelImpl:ope}),fpe={kernelName:ni,backendName:"webgpu",kernelFunc:hpe},mpe=Xn({opSnippet:je.ADD,cpuKernelImpl:Mde,supportsComplex:!0}),gpe={kernelName:qr,backendName:"webgpu",kernelFunc:mpe},ype=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Me()} {
${He()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
${e.join(`
`)}
setOutputFlat(flatIndex, ${t});
}
}
}
`}};function Ape(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return tr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Ln(i,l)),a=s.map(i=>i.shape),o=new ype(a);return n.runWebGPUProgram(o,s,r)}var xpe={kernelName:wa,backendName:"webgpu",kernelFunc:Ape},PC=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];E.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=E.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,n=`
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) {
workgroupBarrier();
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
let i = i32(localId.x) * ${this.reductionFactor} + w;
if (i < currentSize) {
let candidateIndex = xBestIndices[i];
let candidate = xBestValues[i];
if(candidate ${this.op} bestValue && !isNanCustom(candidate)) {
bestValue = candidate;
bestIndex = candidateIndex;
}
}
}
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
}
if (localId.x == 0u) {
setOutputFlatI32(flatOutputIndex, i32(bestIndex));
}
`,s=ln(this.outputShape.length),r=(i,l)=>this.outputShape.length===1?i:`${i}[${l}]`,a=i=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${i}]`;return`
fn DIV_CEIL(a : i32, b : i32) -> i32 {
return ((a - 1) / b + 1);
}
let WorkGroupSize = ${this.workGroupSize[0]};
${e?t:""}
// In order to get a flattened index into the input tensor, we need to
// add back the index along the reduced dimension to |outputCoords|.
// This function outputs the offset to the first value along
// |axis| and the stride to get the next value of the input along |axis|.
fn getInputCoordInfo(globalId : vec3<u32>, globalIndex : i32) -> vec2<i32>{
let outputCoords : ${s} = getOutputCoords(globalId, globalIndex);
var i = ${this.outputShape.length-1};
var stride = 1;
var inputStride = 1;
var offset = 0;
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
let length = ${a(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${r("outputCoords","i")} * stride;
i = i - 1;
}
stride = stride * length;
}
return vec2<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Me()} {
${He()}
let coordInfo = getInputCoordInfo(globalId, index);
var bestIndex = 0;
var bestValue = x.numbers[getInputIndex(coordInfo, bestIndex)];
let Length = ${a("uniforms.axis")};
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
for (var w = 0; w < WorkPerThread; w = w + 1) {
let i = i32(globalId.x) * WorkPerThread + w;
if (i < Length) {
let candidate = x.numbers[getInputIndex(coordInfo, i)];
if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) {
bestValue = candidate;
bestIndex = i;
}
}
}
let flatOutputIndex = i32(globalId.y);
${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"}
}
`}},bpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
let TILE_DIM = ${this.workGroupSize[0]};
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
${Me()} {
${He()}
let workGroupID = (globalId - localId)/vec3<u32>(${this.workGroupSize[0]}u, ${this.workGroupSize[1]}u, ${this.workGroupSize[2]}u);
var x = i32(workGroupID.x) * TILE_DIM + i32(localId.x);
var y = i32(workGroupID.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workGroupID.y) * TILE_DIM + i32(localId.x);
y = i32(workGroupID.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputFlat((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},vpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=ln(this.outputShape.length),t=wpe(this.newDim);return`
${Me()} {
${He()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromFlatIndex(flatIndex);
setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function wpe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function Il(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=ppe(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new bpe(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new vpe(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var kpe={kernelName:po,backendName:"webgpu",kernelFunc:Il};function Ipe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Il({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new PC(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Spe={kernelName:ka,backendName:"webgpu",kernelFunc:Ipe};function Cpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Il({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new PC(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Tpe={kernelName:Yl,backendName:"webgpu",kernelFunc:Cpe},FC=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, coords[3]);
${e}
}
}
setOutput(batch, coords[1], coords[2], coords[3], ${t});
}
}
`}},OC=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let d = coords[3];
if (all(coords < uniforms.outShape)) {
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutput(batch, coords[1], coords[2], d, value);
}
}
`}};function Npe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return tr({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new OC(u):(d=new FC(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Epe={kernelName:Ia,backendName:"webgpu",kernelFunc:Npe};function Rpe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return _x({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var $pe={kernelName:Sa,backendName:"webgpu",kernelFunc:Rpe},Dpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=t,this.rank=t.length,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ln(e.length)}; `,this.shaderKey="slice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=ln(this.rank),t=_pe(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Px[a]} = uniforms.start[${a}] + coords.${Px[a]};`),`
${Me()} {
${He()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getOutputCoords(globalId, index);
${n.join(`
`)}
setOutputFlat(index, getSource(${t}));
}
}
`}},Px=["x","y","z","w","u","v"];function _pe(e){if(e===1)return"sourceLoc";if(e<=6)return Px.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function kp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=yn.parseSliceParams(r,a,o);if(yn.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=ipe(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new Dpe(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var Ppe={kernelName:Di,backendName:"webgpu",kernelFunc:kp},Fpe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=nt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Il({inputs:{x:f},backend:n,attrs:{perm:c}}),g=nt({inputs:{x:m},backend:n,attrs:{shape:u}}),y=kp({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>n.disposeData(A.dataId)),y},Ope={kernelName:si,backendName:"webgpu",kernelFunc:Fpe},MC=Xn({opSnippet:je.NOT_EQUAL,dtype:"bool",cpuKernelImpl:npe}),Mpe={kernelName:bi,backendName:"webgpu",kernelFunc:MC};function Ip(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.real},backend:n})}var zpe={kernelName:td,backendName:"webgpu",kernelFunc:Ip};function Lpe(e,t){let n=new e0(e.shape,Fe.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Fx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return tr({inputs:{x:r},backend:n});let o=jt(r.shape),i=Fx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=mc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Ip({inputs:{input:r},backend:n}),i=Fx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=tr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Lpe(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=MC({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Bpe={kernelName:Ca,backendName:"webgpu",kernelFunc:Fx},Wpe=$n({opType:Fe.CEIL,cpuKernelImpl:zde}),Vpe={kernelName:Ta,backendName:"webgpu",kernelFunc:Wpe},Upe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4",this.size=v.sizeFromShape(this.outputShape)/4}getUserCode(){return`
${Me()} {
${He()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isNanCustom(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputFlat(index, clampedValue);
}
}
`}},Gpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Me()} {
${He()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
if (isNanCustom(value)) {
setOutputFlat(index, value);
return;
}
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Hpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new Upe(r.shape):i=new Gpe(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var jpe={kernelName:Xr,backendName:"webgpu",kernelFunc:Hpe},qpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;a<e.length;a++)e[a]=e[a-1]+this.shapes[a][1];t.push(`if (yC < ${e[0]}){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`);for(let a=1;a<e.length;a++){let o=e[a-1];t.push(`elseif (yC < ${e[a]}){ setOutput(coords.x, coords.y, getT${a}(yR, yC - ${o})); }`)}let s=e.length,r=e[e.length-1];t.push(`else { setOutput(coords.x, coords.y, getT${s}(yR, yC - ${r})); }`)}else t.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
${Me()} {
${He()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${t.join(`
`)}
}
}
}
`}};function t0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Xpe={kernelName:Yc,backendName:"webgpu",kernelFunc:t0};function Ox(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>Ip({inputs:{input:m},backend:n})),d=e.map(m=>t0({inputs:{input:m},backend:n})),p=Ox(u,t,n),h=Ox(d,t,n),f=mc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return nt({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=E.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=Lde(d,p,s,h),m=E.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeData(y.dataId)),g}let{tensors2D:a,outShape:o}=Kpe(e,t,n),i=new qpe(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=nt({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Kpe(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>nt({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function zC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return tr({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),Ox(i,a,n)}var Zpe={kernelName:ri,backendName:"webgpu",kernelFunc:zC},Ype=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${Me()} {
${He()}
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
let rc = getCoordsFromFlatIndex(flatIndex);
if(flatIndex < uniforms.size) {
let blockIndex = rc[0];
let pos = rc[1];
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
var value = 0.0;
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
uniforms.pad[0];
let d1 = offsetX + uniforms.dilation[0] * ((pos %
uniforms.itemsPerBlockRow) / uniforms.inChannels);
let ch = pos % uniforms.inChannels;
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
value = getA(d0, d1, ch);
}
}
setOutputFlat(flatIndex, value);
}
}
}
`}};function LC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=nt({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=nt({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=_x({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=nt({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Jpe({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=n,x=A==="channelsLast",b=l*c*u,w=m*f,k=[w,b],S=!1,N=!1,R=[],P=nt({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),$=nt({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(P),R.push($);let D=new Ype(k,x),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],O=s.runWebGPUProgram(D,[P],P.dtype,T),B=nt({inputs:{x:O},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(O),R.push(B);let H=[1,k[0],k[1]],z=new RC(H,[1,w,n.outChannels],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,N),X=H[1],ee=H[2],J=n.outChannels,Q=[{type:"int32",data:[X]},{type:"int32",data:[J]},{type:"int32",data:[ee]}],ne=s.runWebGPUProgram(z,[B,$],B.dtype,Q),K=x?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],oe=nt({inputs:{x:ne},backend:s,attrs:{shape:K}});R.push(ne);for(let ce of R)s.disposeData(ce.dataId);return oe}var BC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(r,[o,l]),ua(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape);
let divBy4Remainder${e} = flatIndex${e} % 4;
let divBy4Index${e} = flatIndex${e} / 4;
let curData${e} = x.numbers[divBy4Index${e}];
if (divBy4Remainder${e} == 0) {
temp = curData${e};
} else {
// TODO: This could end up being a redundant load with another one in
// the same shader invocation. Perhaps there's an opportunity for
// optimization
let nextData${e} = x.numbers[divBy4Index${e} + 1];
if (divBy4Remainder${e} == 1) {
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
} elseif (divBy4Remainder${e} == 2) {
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
} elseif (divBy4Remainder${e} == 3) {
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
}
}
`}getUserCode(){let t=EC([4,4,1],this.workGroupSize),r=`let outRow = r / uniforms.outShape[2];
let outCol = r % uniforms.outShape[2];
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
let inChCoord = c % uniforms.xShape[3];
var coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
inChCoord);
var resData = vec4<f32>(0.0);
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (coordsInBounds4D(coord, uniforms.xShape)) {
resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
} else {
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
${this.getSampleAWithRemainder(1)}
resData = temp;
if (WCol == (uniforms.filterDims[1] - 1)) {
coord = vec4<i32>(
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} elseif (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${r}
}
return vec4<f32>(0.0);
`,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,i="",l="";if(this.activation){let d=Lo(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${d}
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaAtOutCoords();
${d}
}`,new Error("Leakyrelu is not supported.");i=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${d}
}`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${a}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${o}
}
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col * 4);
${c}
${l}
setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${t}
`}},WC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ex(this.dispatchLayout,this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(s,[a,i]),ua(r,[i,o])]}getUserCode(){let e=Dx(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter + col];
}
return 0.0;
`,r="",a="";if(this.activation){let l=Lo(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${l}
}`:r=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${l}
}
`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
${o}
${a}
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},VC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Lo(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${r}
}
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${e}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
let coord = vec4<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutput(batch, row, col, chan, value);
}
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}};function Qpe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return LC({x:r,filter:a,convInfo:p,backend:s});if(Z().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return Jpe({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=Z().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new VC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new BC(p):h=new WC(p),!g){let y=p.outShape[1]*p.outShape[2],A=p.outShape[3],x=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[y]},{type:"int32",data:[A]},{type:"int32",data:[x]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var ehe={kernelName:Na,backendName:"webgpu",kernelFunc:Qpe},the=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ex(this.dispatchLayout,this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let coord = vec4<i32>(coordX, coordY, col,
row % uniforms.outBackprop[3]);
return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
}
return 0.0;
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${Dx(this.elementsPerThread,this.workGroupSize)}
`}},nhe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.outputShape=e.inShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let 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.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd);
}
}
`}};function she(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new nhe(p);else{f=new the(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var rhe={kernelName:Ea,backendName:"webgpu",kernelFunc:she},ahe=$n({opType:Fe.COS}),ohe={kernelName:Ra,backendName:"webgpu",kernelFunc:ahe},ihe=$n({opType:Fe.COSH}),lhe={kernelName:$a,backendName:"webgpu",kernelFunc:ihe},uhe=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1];let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
fn writeResult(coords : vec4<i32>, value : f32) {
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutput(coords[0], coords[1], coords[2], coords[3], value);
}
}
${Me()} {
${He()}
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let coords = getOutputCoords(globalId, index);
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${s};
let width_scale = ${o};
let in_y = ${r};
if( in_y < 0.0 || in_y > ${e} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let in_x = ${i};
if( in_x < 0.0 || in_x > ${t} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
writeResult(coords, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
writeResult(coords,newValue);
}
}
`}},che=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new uhe(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},dhe={kernelName:oi,backendName:"webgpu",kernelFunc:che},phe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.size=v.sizeFromShape(this.outputShape),this.dataFormat=t}getUserCode(){return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let b = coords[0];
let h = ${this.getHeightCoordString()};
let w = ${this.getWidthCoordString()};
let d = ${this.getDepthCoordString()};
let in_h = h / uniforms.blockSize;
let offset_h = h % uniforms.blockSize;
let in_w = w / uniforms.blockSize;
let offset_w = w % uniforms.blockSize;
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
${this.getOutputDepthSize()};
let in_d = d + offset_d;
let rlt = ${this.getInputSamplingString()};
setOutputFlat(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function hhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new phe(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var fhe={kernelName:ii,backendName:"webgpu",kernelFunc:hhe},UC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Lo(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, globalIndex);
${r}
}`:e=`
fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], globalId, index);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
${e}
${Me()} {
${He()}
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},GC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=Lo(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, index);
${a}
}`:t=`
fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
${a}
}
`,n="dotProd = activation(dotProd, globalId, index);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByGlobalId(globalId, index);":"";return`
${t}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutput(batch, row, col, chan, value);
}
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / ${e};
let q = d2 - d1 * ${e};
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0];
let inputColEnd = inputColStart + ${this.convInfo.filterWidth} * uniforms.dilation[1];
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) {
// Here using a constant value |this.convInfo.filterHeight| instead
// of uniform value is in order to loop unrolling.
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
}
${s}
${n}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function mhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new UC(d):p=new GC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var ghe={kernelName:Da,backendName:"webgpu",kernelFunc:mhe},HC=Xn({opSnippet:je.MUL,cpuKernelImpl:epe,supportsComplex:!0}),yhe={kernelName:Ka,backendName:"webgpu",kernelFunc:HC},Ahe=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=`
if (isNanCustom(candidate)) {
bestValue = uniforms.NAN;
} elseif (candidate ${this.reduceType==="min"?"<":">"}
bestValue)
{ bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,a=`
xBestValues[localId.x] = bestValue;
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "}
var currentSize = WorkGroupSize;
for(; currentSize > 1;) {
workgroupBarrier();
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
let i = i32(localId.x) * ${this.reductionFactor} + w;
if (i < currentSize) {
let candidate = xBestValues[i];
${t}
}
}
workgroupBarrier();
xBestValues[localId.x] = bestValue;
currentSize = DIV_CEIL(currentSize, ${this.reductionFactor});
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""}
}
if (localId.x == 0u) {
${s}
}
`;return`
fn DIV_CEIL(a : i32, b : i32) -> i32 {
return ((a - 1) / b + 1);
}
let WorkGroupSize = ${this.workGroupSize[0]};
${e?r:""}
fn getOffset(globalId : vec3<u32>, index : i32) -> i32 {
let outputCoords = getOutputCoords(globalId, index);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Me()} {
${He()}
let offset= getOffset(globalId, index);
var bestValue = ${n};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
for (var w = 0; w < WorkPerThread; w = w + 1) {
let i = i32(globalId.x) * WorkPerThread + w;
if (i < Length) {
let candidate = f32(x.numbers[offset + i]);
${t}
}
}
let flatOutputIndex = i32(globalId.y);
${e?a:s}
}
`}};function Sp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=Il({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=Yde(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=spe(u.shape,u.dtype,m,l);f=r.makeTensorInfo(A,x,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),y=v.sizeFromShape(u.shape)/m,A={windowSize:m,inSize:m,batchSize:y,outSize:1},x=s==="mean"?"float32":fd(e.dtype),b=[{type:"int32",data:[m]}],w=new Ahe(A,s,x),k=r.runWebGPUProgram(w,[u],x,b);o.push(k),f=nt({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Mx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"sum",n)}var xhe={kernelName:oo,backendName:"webgpu",kernelFunc:Mx};function bhe(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=a[g]:(x=Il({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=nt({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=HC({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Mx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var vhe={kernelName:Zc,backendName:"webgpu",kernelFunc:bhe},whe=$n({opType:Fe.ELU}),khe={kernelName:Pa,backendName:"webgpu",kernelFunc:whe},Ihe=Xn({opSnippet:je.EQUAL,dtype:"bool",cpuKernelImpl:Bde}),She={kernelName:li,backendName:"webgpu",kernelFunc:Ihe},jC=$n({opType:Fe.EXP,cpuKernelImpl:Wde,dtype:"float32"}),Che={kernelName:Fa,backendName:"webgpu",kernelFunc:jC};function zx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),nt({inputs:{x:a},backend:s,attrs:{shape:i}})}var The={kernelName:ui,backendName:"webgpu",kernelFunc:zx},Nhe=$n({opType:Fe.EXPM1,cpuKernelImpl:Vde}),Ehe={kernelName:ci,backendName:"webgpu",kernelFunc:Nhe},Rhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workPerThread=4,this.workGroupSize=[16,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="fill",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Me()} {
${He()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
setOutputFlat(flatIndex, uniforms.value);
}
}
}
`}};function n0(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new Rhe(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var $he={kernelName:ru,backendName:"webgpu",kernelFunc:n0},Dhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputFlat(index, outputValue);
}
}
`}},_he={kernelName:di,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Dhe(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Phe=$n({opType:Fe.FLOOR,cpuKernelImpl:Ude}),Fhe={kernelName:Oa,backendName:"webgpu",kernelFunc:Phe},Ohe=Xn({opSnippet:je.INT_DIV,dtype:"int32"}),Mhe={kernelName:Ma,backendName:"webgpu",kernelFunc:Ohe},zhe=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},qC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=xce(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function XC(e,t,n,s="",r=""){return(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+e.shaderKey}function KC(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=XC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>qC(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let y=[i,o,...l,...u.dispatch];u.setUniform(n.device,y);let A;if(a){let x={source:t};A=n.device.importExternalTexture(x)}else A=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,A,c.dataId),c}var Lhe={kernelName:ad,backendName:"webgpu",kernelFunc:Bhe},gc;function Bhe(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(Z().getBool("WEBGPU_USE_IMPORT")&&o)return KC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(gc==null&&(gc=document.createElement("canvas").getContext("2d")),gc.canvas.width=u,gc.canvas.height=d,gc.drawImage(r,0,0,u,d),r=gc.canvas),c||l||o||i)return KC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let y=h.length,A=0;for(let x=0;x<y;x++)x%4<a&&(f[A++]=h[x])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Whe=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalId(globalId, index)");let t="1.0";this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalId(globalId, index)");let n=this.outputShape.length,s=ln(n),r="setOutput(coords[0], coords[1], coords[2], coords[3], value);";return n===2&&(r="setOutput(coords[0], coords[1], value);"),n===3&&(r="setOutput(coords[0], coords[1], coords[2], value);"),`
fn writeResult(coords : ${s}, value : f32) {
if (coordsInBounds${n}D(coords, uniforms.outShape)) {
${r}
}
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let xValue = getXAtOutCoordsByGlobalId(globalId, index);
let meanValue = getMeanAtOutCoordsByGlobalId(globalId, index);
let varianValue = getVarianceAtOutCoordsByGlobalId(globalId, index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
writeResult(coords,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
`}},Vhe={kernelName:za,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new Whe(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function Uhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y=o!=null,A=i!=null,x;if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))return LC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=Z().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],S=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)x=new VC(g,y,h,A);else{w?x=new BC(g,y,h,A):x=new WC(g,y,h,A);let R=g.outShape[1]*g.outShape[2],P=g.outShape[3],$=g.filterHeight*g.filterWidth*g.inShape[3];S.push({type:"int32",data:[R]},{type:"int32",data:[P]},{type:"int32",data:[$]})}let N=[r,a];return y&&N.push(o),A&&N.push(i),n.runWebGPUProgram(x,N,r.dtype,S)}var Ghe={kernelName:mo,backendName:"webgpu",kernelFunc:Uhe};function Hhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=E.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,y=i!=null;g&&m.push(o),y&&m.push(i);let A;f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?A=new UC(f,g,p,y):A=new GC(f,g,p,y);let x=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}];return n.runWebGPUProgram(A,m,"float32",x)}var jhe={kernelName:go,backendName:"webgpu",kernelFunc:Hhe},qhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.size=v.sizeFromShape(this.outputShape),this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${ln(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
var flattenIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexTemp = i32(round(getIndices(coords[0], j)));
let strideNum = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
if (index < uniforms.size) {
setOutputFlat(index, getA(flattenIndex, coords[1]));
}
}
`}};function Xhe(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=nt({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=nt({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),b=Gde(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new qhe(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),y=nt({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var Khe={kernelName:hi,backendName:"webgpu",kernelFunc:Xhe},Zhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Yhe(this.aShape,"i32");return`
${Me()} {
${He()}
let resRC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
setOutputFlat(index, getA(${e}));
}
}
`}};function Yhe(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function Jhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=nt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=nt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let w=n.tensorMap.get(m.dataId).values,k=We(m.shape,m.dtype,w),N=n.tensorMap.get(f.dataId).values,R=We(f.shape,f.dtype,N),P=Hde(R,k,g);return h.forEach($=>n.disposeData($.dataId)),n.makeTensorInfo(d.outputShape,P.dtype,P.values)}let y=new Zhe(f.shape,g),A=n.runWebGPUProgram(y,[f,m],f.dtype);h.push(A);let x=nt({inputs:{x:A},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeData(b.dataId)),x}var Qhe={kernelName:pi,backendName:"webgpu",kernelFunc:Jhe},efe=Xn({opSnippet:je.GREATER,cpuKernelImpl:qde,dtype:"bool"}),tfe={kernelName:fi,backendName:"webgpu",kernelFunc:efe},nfe=Xn({opSnippet:je.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:jde}),sfe={kernelName:La,backendName:"webgpu",kernelFunc:nfe},rfe=Xn({opSnippet:je.LESS,dtype:"bool",cpuKernelImpl:Kde}),afe={kernelName:gi,backendName:"webgpu",kernelFunc:rfe},ofe=Xn({opSnippet:je.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Xde}),ife={kernelName:yi,backendName:"webgpu",kernelFunc:ofe},lfe=$n({opType:Fe.LOG,cpuKernelImpl:Zde}),ufe={kernelName:Wa,backendName:"webgpu",kernelFunc:lfe},cfe=Xn({opSnippet:je.LOGICAL_AND,dtype:"bool"}),dfe={kernelName:Ai,backendName:"webgpu",kernelFunc:cfe},pfe=$n({opType:Fe.LOGICAL_NOT}),hfe={kernelName:uu,backendName:"webgpu",kernelFunc:pfe};function ZC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Sp(r,a,o,"max",n)}var ffe={kernelName:Va,backendName:"webgpu",kernelFunc:ZC},mfe=Xn({opSnippet:je.MAX,cpuKernelImpl:Jde}),gfe={kernelName:Ua,backendName:"webgpu",kernelFunc:mfe};function yfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return tr({inputs:{x:r},backend:n});d=new OC(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new FC(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var Afe={kernelName:Ga,backendName:"webgpu",kernelFunc:yfe};function xfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Sp(r,o,a,"mean",n)}var bfe={kernelName:Ha,backendName:"webgpu",kernelFunc:xfe};function vfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"min",n)}var wfe={kernelName:ja,backendName:"webgpu",kernelFunc:vfe},kfe=Xn({opSnippet:je.MIN,cpuKernelImpl:Qde}),Ife={kernelName:qa,backendName:"webgpu",kernelFunc:kfe},Sfe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=ln(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Me()} {
${He()}
let start = ${o}(${t});
let end = ${o}(${n});
var outC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${s}) {
${a} = ${s} * 2 - ${a} - ${this.offset};
} elseif(${a} >= ${r}) {
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputFlat(index, getX(${i}));
}
}
`}},Cfe={kernelName:Xa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Sfe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function Tfe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=tpe(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new e0(s.shape,Fe.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var Nfe={kernelName:xi,backendName:"webgpu",kernelFunc:Tfe};function Efe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Zs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Rfe={kernelName:vi,backendName:"webgpu",kernelFunc:Efe};function $fe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Zs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Dfe={kernelName:wi,backendName:"webgpu",kernelFunc:$fe};function s0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Ip({inputs:{input:s},backend:n}),a=s0({inputs:{x:r},backend:n}),o=t0({inputs:{input:s},backend:n}),i=s0({inputs:{x:o},backend:n}),l=mc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return n0({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var _fe={kernelName:Bi,backendName:"webgpu",kernelFunc:s0};function YC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Ip({inputs:{input:s},backend:n}),a=YC({inputs:{x:r},backend:n}),o=t0({inputs:{input:s},backend:n}),i=s0({inputs:{x:o},backend:n}),l=mc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return n0({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Pfe={kernelName:ki,backendName:"webgpu",kernelFunc:YC};function Ffe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return zx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=zx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=zC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Ofe={kernelName:Si,backendName:"webgpu",kernelFunc:Ffe},Mfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=ln(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Me()} {
${He()}
let start = ${r};
let end = ${a};
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
if (${o} || ${i}) {
setOutputFlat(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputFlat(index, getX(${l}));
}
}
}
`}},JC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return tr({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return n0({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Mfe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},zfe={kernelName:Za,backendName:"webgpu",kernelFunc:JC},Lfe=Xn({opSnippet:je.POW}),Bfe={kernelName:Ya,backendName:"webgpu",kernelFunc:Lfe};function Wfe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new DC(je.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Vfe={kernelName:Ja,backendName:"webgpu",kernelFunc:Wfe};function Ufe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"prod",n)}var Gfe={kernelName:Ci,backendName:"webgpu",kernelFunc:Ufe},Hfe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=rpe(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},jfe={kernelName:pu,backendName:"webgpu",kernelFunc:Hfe},QC=Xn({opSnippet:je.DIV}),qfe={kernelName:_a,backendName:"webgpu",kernelFunc:QC},Xfe=$n({opType:Fe.RELU}),Kfe={kernelName:Qa,backendName:"webgpu",kernelFunc:Xfe},Zfe=$n({opType:Fe.RELU6}),Yfe={kernelName:to,backendName:"webgpu",kernelFunc:Zfe},Jfe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (all(coords < uniforms.outShape)) {
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
let effectiveOutSize = vec2<f32>(
${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function Qfe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new Jfe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var eme={kernelName:eo,backendName:"webgpu",kernelFunc:Qfe},tme=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":t="vec2<f32>(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (all(coords < uniforms.outShape)) {
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
let effectiveOutSize = vec2<f32>(
${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${t};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${e})));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function nme(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new tme(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var sme={kernelName:fu,backendName:"webgpu",kernelFunc:nme},rme=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
cosRadians : f32;`,this.shaderKey="rotate",this.size=v.sizeFromShape(this.outputShape),this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
uniforms.sinRadians;
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
uniforms.cosRadians;
let coordX = i32(round(coordXFloat + uniforms.centerX));
let coordY = i32(round(coordYFloat + uniforms.centerY));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputFlat(index, outputValue);
}
}
`}},ame={kernelName:Wi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new rme(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},ome=$n({opType:Fe.RSQRT,cpuKernelImpl:ape}),ime={kernelName:no,backendName:"webgpu",kernelFunc:ome},e6=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.outputShape=a,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`,this.size=v.sizeFromShape(this.outputShape);let l=ln(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Me()} {
${He()}
let globalIndex = index * ${this.workPerThread};
if (globalIndex < uniforms.size) {
var sum = vec4<f32>(0.0);
var found = vec4<bool>(false);
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${this.indicesSnippet}));
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
let coords = getCoordsFromFlatIndex(curIndex);
if (flattenedIndex == coords[0]) {
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
found[innerIndex] = true;
}
}
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function lme(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=nt({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=nt({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=[{type:"int32",data:[l]},{type:"int32",data:[i]},{type:"int32",data:u}],y=new e6(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGPUProgram(y,[f,h,m],f.dtype,g),x=nt({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(A.dataId),n.disposeData(m.dataId),x}var ume={kernelName:Ri,backendName:"webgpu",kernelFunc:lme},cme=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
${Me()} {
${He()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputFlat(index, getA(${t}));
} else {
setOutputFlat(index, getB(${t}));
}
}
}
`}};function dme(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new cme(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var pme={kernelName:$i,backendName:"webgpu",kernelFunc:dme},hme=$n({opType:Fe.SIGMOID}),fme={kernelName:ro,backendName:"webgpu",kernelFunc:hme},mme=$n({opType:Fe.SIN}),gme={kernelName:so,backendName:"webgpu",kernelFunc:mme},yme=$n({opType:Fe.SINH}),Ame={kernelName:_i,backendName:"webgpu",kernelFunc:yme},t6=Xn({opSnippet:je.SUB,cpuKernelImpl:cpe,supportsComplex:!0}),xme={kernelName:uo,backendName:"webgpu",kernelFunc:t6};function bme(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=ZC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=nt({inputs:{x:i},backend:n,attrs:{shape:l}}),u=t6({inputs:{a:r,b:c},backend:n}),d=jC({inputs:{x:u},backend:n}),p=Mx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=nt({inputs:{x:p},backend:n,attrs:{shape:l}}),f=QC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var vme={kernelName:io,backendName:"webgpu",kernelFunc:bme},wme=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,A)=>y*A),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=JC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=nt({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Il({inputs:{x:f},backend:n,attrs:{perm:p}}),g=nt({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeData(y.dataId)),g},kme={kernelName:Pi,backendName:"webgpu",kernelFunc:wme};function Ime(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new e6(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=nt({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var Sme={kernelName:nd,backendName:"webgpu",kernelFunc:Ime};function Cme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=kp({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Tme={kernelName:Fi,backendName:"webgpu",kernelFunc:Cme},Nme=$n({opType:Fe.SQRT}),Eme={kernelName:ao,backendName:"webgpu",kernelFunc:Nme},Rme={kernelName:Au,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new e0(n.shape,Fe.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},$me=Xn({opSnippet:je.SQUARED_DIFFERENCE}),Dme={kernelName:lo,backendName:"webgpu",kernelFunc:$me},_me=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=ln(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
setOutputFlat(index, getX(${t}));
}
}
`}};function Pme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=yn.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=nt({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=kp({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=nt({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeData(k.dataId)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let N=n.tensorMap.get(x.dataId).values,R=We(x.shape,x.dtype,N),P=lpe(A,R,m,f);b=n.makeTensorInfo(A,x.dtype,P.values)}else{let S=new _me(A),N=[{type:"int32",data:f},{type:"int32",data:m}];b=n.runWebGPUProgram(S,[x],x.dtype,N)}let w=nt({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeData(x.dataId),n.disposeData(b.dataId),w}var Fme={kernelName:Oi,backendName:"webgpu",kernelFunc:Pme};function Ome(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=upe(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Mme={kernelName:sd,backendName:"webgpu",kernelFunc:Ome},zme=$n({opType:Fe.TANH}),Lme={kernelName:co,backendName:"webgpu",kernelFunc:zme},Bme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.size=v.sizeFromShape(this.outputShape),this.shaderKey="tile"}getUserCode(){let e=Wme(this.rank,"uniforms.");return`
${Me()} {
${He()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
setOutputFlat(index, getA(${e}));
}
}
`}};function Wme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Vme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=We(r.shape,r.dtype,c),d=dpe(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Bme(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Ume={kernelName:Kr,backendName:"webgpu",kernelFunc:Vme},Gme=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
fn mapCoord(outCoord : f32, len : f32) -> f32{
var inCoord = outCoord;
if(uniforms.fillModeId == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
inCoord;
}
if (inCoord < -len) {
inCoord = inCoord + sz2;
} else {
inCoord = -inCoord - 1.0;
}
}
} elseif (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} elseif (uniforms.fillModeId == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
}
} elseif (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} elseif (uniforms.fillModeId == 4) {
return clamp(outCoord, 0.0, len - 1.0);
}
return outCoord;
}
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
channel : i32) -> f32 {
var outputValue : f32;
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = uniforms.fillValue;
}
return outputValue;
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
var outputValue : f32;
let batch = coords[0];
let x = coords[2];
let y = coords[1];
let channel = coords[3];
let xf = f32(x);
let yf = f32(y);
let a1 = getTransforms(batch, 0);
let a2 = getTransforms(batch, 1);
let a3 = getTransforms(batch, 2);
let b1 = getTransforms(batch, 3);
let b2 = getTransforms(batch, 4);
let b3 = getTransforms(batch, 5);
let c1 = getTransforms(batch, 6);
let c2 = getTransforms(batch, 7);
let projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = uniforms.fillValue;
} else {
let inX = (a1 * xf + a2 * yf + a3) / projection;
let inY = (b1 * xf + b2 * yf + b3) / projection;
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
if (uniforms.interpolationModeId == 1) {
let coordY = i32(round(mapY));
let coordX = i32(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
let yFloor = floor(mapY);
let xFloor = floor(mapX);
let yCeil = yFloor + 1.0;
let xCeil = xFloor + 1.0;
let valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
let valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], outputValue);
}
}
`}};function Hme(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Gme(g),A=o==="nearest"?1:2,x;switch(i){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var jme={kernelName:zi,backendName:"webgpu",kernelFunc:Hme};function qme(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=kp({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=nt({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var Xme={kernelName:Li,backendName:"webgpu",kernelFunc:qme},Kme=[Dde,fpe,gpe,xpe,Spe,Tpe,Epe,$pe,Ope,Bpe,Vpe,jpe,Ode,Zpe,ehe,rhe,ohe,lhe,dhe,fhe,ghe,vhe,khe,She,The,Che,Ehe,$he,_he,Lhe,Fhe,Mhe,Vhe,Ghe,jhe,Khe,Qhe,tfe,sfe,Fde,Xpe,afe,ife,ufe,dfe,hfe,ffe,gfe,Afe,bfe,wfe,Ife,Cfe,yhe,Nfe,Rfe,Dfe,Mpe,Pfe,Ofe,zfe,Vfe,Gfe,Bfe,jfe,zpe,qfe,Kfe,Yfe,Rde,eme,sme,ame,ime,ume,pme,fme,gme,Ame,Ppe,Fme,Mme,vme,kme,Tme,Sme,Eme,Rme,Dme,xme,xhe,Lme,Ume,jme,kpe,Xme,_fe];for(let e of Kme)Yr(e);var Zme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=n6(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=n6(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function n6(e,t){return`${e}_${t}`}var s6=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
[[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Me()} {
${He()}
let flatIndexBase = index * uniforms.numChannels;
let coords = getCoordsFromFlatIndex(flatIndexBase);
let values = ${e};
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
let flatIndex = flatIndexBase + i;
if (flatIndex < uniforms.size) {
result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Yme=class extends s6{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Jme=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),r0=class extends Gl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!$x())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Zme(this.device),this.tensorMap=new Vc(this,ts()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return r0.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*Rx(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*Rx(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new s6),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Yme),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=E.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=CC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;l<n;++l)r.push({type:a.type,data:[0]}),s++;r.push({type:a.type,data:a.data}),s=s+a.data.length,t+=a.data.length+n}),this.arrayToDataView(r,s)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s){let r=this.makeTensorInfo(e.outputShape,n),a=this.tensorMap.get(r.dataId);if(v.sizeFromShape(r.shape)===0)return a.values=v.getTypedArrayFromDType(r.dtype,0),r;let o=[{type:"float32",data:[NaN]}],i=t.concat(r).map(R=>R.shape),l="int32";i.map(R=>{o.push({type:l,data:R})});let c=v.computeStrides(r.shape);o.push({type:l,data:c}),e.size!=null&&o.push({type:l,data:[e.size]}),o.push({type:"uint32",data:e.dispatch}),s&&(o=[...o,...s]);let u=null,d=this.computePadding(o),p=d.byteLength;u=this.makeUniformsDataView(d);let h=t.map((R,P)=>{if(R.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(R.dataId),{dtype:this.tensorMap.get(R.dataId).dtype,shape:R.shape,name:e.variableNames[P]}});this.uploadToGPU(r.dataId);let f=h.map(R=>R.dtype).concat(r.dtype),m=h.map(R=>E.getBroadcastDims(R.shape,r.shape)),g=h.map(R=>v.arraysEqual(R.shape,r.shape)).join("_"),y=m.map(R=>R.join("_")).join(";"),A=XC(e,i,f,y,g),{bindGroupLayout:x,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),w=this.getAndSavePipeline(A,()=>qC(this.device,e,b,h,r)),k=this.activeTimers!=null,S=zhe(this.device,x,t.map(R=>this.tensorToBinding(R)),this.tensorToBinding(r),u);this.ensureCommandEncoderReady();let N=this.getComputePass();if(k&&this.supportTimeQuery&&N.writeTimestamp(this.querySet,0),N.setPipeline(w),N.setBindGroup(0,S),N.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),k&&this.supportTimeQuery&&N.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(R=>{this.commandQueueOwnedIds.add(R.dataId)}),this.commandQueueOwnedIds.add(r.dataId),u){let R={byteSize:p,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};this.uniformDisposalQueue.push(R)}return Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),k&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Jme){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}};r0.nextDataId=0;var r6={};Le(r6,{WebGPUBackend:()=>r0,webgpu_util:()=>SC});wu.isBrowser()&&$x()&&Xi("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new r0(r,s)},3);var Qme="3.9.0",e0e="3.9.0",t0e="3.9.0",n0e="3.9.0",s0e="3.9.0",r0e="3.9.0",a0e="3.9.0",o0e="3.9.0",i0e={tfjs:Qme,"tfjs-core":e0e,"tfjs-data":t0e,"tfjs-layers":n0e,"tfjs-converter":s0e,"tfjs-backend-cpu":r0e,"tfjs-backend-webgl":a0e,"tfjs-backend-wasm":o0e};var Lx="2.3.2";var a6=`
precision highp float;
attribute vec2 pos;
attribute vec2 uv;
varying vec2 vUv;
uniform float flipY;
void main(void) {
vUv = uv;
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
}
`;var o6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
}
`,i6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform float m[20];
void main(void) {
vec4 c = texture2D(texture, vUv);
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
gl_FragColor.a = c.a;
}
`,l6=`
precision highp float;
varying vec2 vUv;
uniform vec2 size;
uniform sampler2D texture;
vec2 pixelate(vec2 coord, vec2 size) {
return floor( coord / size ) * size;
}
void main(void) {
gl_FragColor = vec4(0.0);
vec2 coord = pixelate(vUv, size);
gl_FragColor += texture2D(texture, coord);
}
`,u6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
void main(void) {
gl_FragColor = vec4(0.0);
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
}
`,c6=`
precision highp float;
varying vec2 vUv;
uniform sampler2D texture;
uniform vec2 px;
uniform float m[9];
void main(void) {
vec4 c11 = texture2D(texture, vUv - px); // top left
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
vec4 c22 = texture2D(texture, vUv); // mid center
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
gl_FragColor =
c11 * m[0] + c12 * m[1] + c22 * m[2] +
c21 * m[3] + c22 * m[4] + c23 * m[5] +
c31 * m[6] + c32 * m[7] + c33 * m[8];
gl_FragColor.a = c22.a;
}
`;var d6=class{constructor(t,n,s){ve(this,"uniform",{});ve(this,"attribute",{});ve(this,"gl");ve(this,"id");ve(this,"collect",(t,n,s)=>{let r=new RegExp("\\b"+n+" \\w+ (\\w+)","ig");t.replace(r,(a,o)=>(s[o]=0,a))});ve(this,"compile",(t,n)=>{let s=this.gl.createShader(n);if(this.gl.shaderSource(s,t),this.gl.compileShader(s),!this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS))throw new Error(`filter: gl compile failed: ${this.gl.getShaderInfoLog(s)}`);return s});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),a=this.compile(s,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,a),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS))throw new Error(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);this.gl.useProgram(this.id),this.collect(n,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=this.gl.getAttribLocation(this.id,o);this.collect(n,"uniform",this.uniform),this.collect(s,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=this.gl.getUniformLocation(this.id,o)}};function p6(e){e||(e={});let t=0,n=null,s=!1,r=-1,a=[null,null],o=[],i=-1,l=-1,c=null,u=null,d=e.canvas||typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(100,100):document.createElement("canvas"),p={},h={INTERMEDIATE:1},f=d.getContext("webgl");if(!f)throw new Error("filter: cannot get webgl context");this.addFilter=function(w){let k=Array.prototype.slice.call(arguments,1),S=b[w];o.push({func:S,args:k})},this.reset=function(){o=[]};let m=function(w,k){if(!(w===i&&k===l)){if(d.width=w,i=w,d.height=k,l=k,!c){let S=new 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a[w]=a[w]||g(i,l),a[w]},A=function(w=0){var R,P;if(!u)return;let k=null,S=null,N=!1;t===0?k=n:k=(R=y(r))==null?void 0:R.texture,t++,s&&!(w&h.INTERMEDIATE)?(S=null,N=t%2==0):(r=(r+1)%2,S=(P=y(r))==null?void 0:P.fbo),f.bindTexture(f.TEXTURE_2D,k),f.bindFramebuffer(f.FRAMEBUFFER,S),f.uniform1f(u.uniform.flipY,N?-1:1),f.drawArrays(f.TRIANGLES,0,6)};this.apply=function(w){if(m(w.width,w.height),t=0,n||(n=f.createTexture()),f.bindTexture(f.TEXTURE_2D,n),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_S,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_WRAP_T,f.CLAMP_TO_EDGE),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MIN_FILTER,f.NEAREST),f.texParameteri(f.TEXTURE_2D,f.TEXTURE_MAG_FILTER,f.NEAREST),f.texImage2D(f.TEXTURE_2D,0,f.RGBA,f.RGBA,f.UNSIGNED_BYTE,w),o.length===0)A();else for(let k=0;k<o.length;k++){s=k===o.length-1;let S=o[k];S.func.apply(this,S.args||[])}return d};let x=function(w){if(p[w])return u=p[w],f.useProgram(u==null?void 0:u.id),u;u=new d6(f,a6,w);let k=Float32Array.BYTES_PER_ELEMENT,S=4*k;return f.enableVertexAttribArray(u.attribute.pos),f.vertexAttribPointer(u.attribute.pos,2,f.FLOAT,!1,S,0*k),f.enableVertexAttribArray(u.attribute.uv),f.vertexAttribPointer(u.attribute.uv,2,f.FLOAT,!1,S,2*k),p[w]=u,u},b={colorMatrix:w=>{let k=new Float32Array(w);k[4]/=255,k[9]/=255,k[14]/=255,k[19]/=255;let S=k[18]===1&&k[3]===0&&k[8]===0&&k[13]===0&&k[15]===0&&k[16]===0&&k[17]===0&&k[19]===0?i6:o6,N=x(S);f.uniform1fv(N==null?void 0:N.uniform.m,k),A()},brightness:w=>{let k=(w||0)+1;b.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},saturation:w=>{let k=(w||0)*2/3+1,S=(k-1)*-.5;b.colorMatrix([k,S,S,0,0,S,k,S,0,0,S,S,k,0,0,0,0,0,1,0])},desaturate:()=>{b.saturation(-1)},contrast:w=>{let k=(w||0)+1,S=-128*(k-1);b.colorMatrix([k,0,0,0,S,0,k,0,0,S,0,0,k,0,S,0,0,0,1,0])},negative:()=>{b.contrast(-2)},hue:w=>{w=(w||0)/180*Math.PI;let k=Math.cos(w),S=Math.sin(w),N=.213,R=.715,P=.072;b.colorMatrix([N+k*(1-N)+S*-N,R+k*-R+S*-R,P+k*-P+S*(1-P),0,0,N+k*-N+S*.143,R+k*(1-R)+S*.14,P+k*-P+S*-.283,0,0,N+k*-N+S*-(1-N),R+k*-R+S*R,P+k*(1-P)+S*P,0,0,0,0,0,1,0])},desaturateLuminance:()=>{b.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{b.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{b.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{b.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{b.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{b.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{b.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{b.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:w=>{let k=new Float32Array(w),S=1/i,N=1/l,R=x(c6);f.uniform1fv(R==null?void 0:R.uniform.m,k),f.uniform2f(R==null?void 0:R.uniform.px,S,N),A()},detectEdges:()=>{b.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{b.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{b.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:w=>{let k=w||1;b.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},emboss:w=>{let k=w||1;b.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},blur:w=>{let k=w/7/i,S=w/7/l,N=x(u6);f.uniform2f(N==null?void 0:N.uniform.px,0,S),A(h.INTERMEDIATE),f.uniform2f(N==null?void 0:N.uniform.px,k,0),A()},pixelate:w=>{let k=w/i,S=w/l,N=x(l6);f.uniform2f(N==null?void 0:N.uniform.size,k,S),A()}}}var ie={browser:void 0,node:void 0,worker:void 0,platform:void 0,agent:void 0,initial:!0,backends:[],offscreen:void 0,filter:void 0,tfjs:{version:void 0},wasm:{supported:void 0,backend:void 0,simd:void 0,multithread:void 0},webgl:{supported:void 0,backend:void 0,version:void 0,renderer:void 0},webgpu:{supported:void 0,backend:void 0,adapter:void 0},kernels:[],Canvas:void 0,Image:void 0,ImageData:void 0};async function u0e(){var n;ie.backends=Object.keys(ts().registryFactory),ie.wasm.supported=typeof WebAssembly!="undefined",ie.wasm.backend=ie.backends.includes("wasm"),ie.wasm.supported&&ie.wasm.backend&&lr()==="wasm"&&(ie.wasm.simd=await Z().getAsync("WASM_HAS_SIMD_SUPPORT"),ie.wasm.multithread=await Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let e=Ts(100,100),t=e?e.getContext("webgl2"):void 0;if(ie.webgl.supported=typeof t!="undefined",ie.webgl.backend=ie.backends.includes("webgl"),ie.webgl.supported&&ie.webgl.backend&&(lr()==="webgl"||lr()==="humangl")){let s=Tr().gpgpu!=="undefined"?await Tr().getGPGPUContext().gl:null;s&&(ie.webgl.version=s.getParameter(s.VERSION),ie.webgl.renderer=s.getParameter(s.RENDERER))}ie.webgpu.supported=ie.browser&&typeof navigator.gpu!="undefined",ie.webgpu.backend=ie.backends.includes("webgpu"),ie.webgpu.supported&&(ie.webgpu.adapter=(n=await navigator.gpu.requestAdapter())==null?void 0:n.name),ie.kernels=Zr(lr()).map(s=>s.kernelName.toLowerCase())}async function a0(){if(ie.browser=typeof navigator!="undefined",ie.node=typeof process!="undefined",ie.tfjs.version=Jh,ie.offscreen=typeof ie.offscreen=="undefined"?typeof OffscreenCanvas!="undefined":ie.offscreen,typeof navigator!="undefined"){let e=navigator.userAgent.match(/\(([^()]+)\)/g);if(e&&e[0]){let t=e[0].match(/\(([^()]+)\)/g);ie.platform=t&&t[0]?t[0].replace(/\(|\)/g,""):"",ie.agent=navigator.userAgent.replace(e[0],""),ie.platform[1]&&(ie.agent=ie.agent.replace(e[1],"")),ie.agent=ie.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(ie.platform=`${process.platform} ${process.arch}`,ie.agent=`NodeJS ${process.version}`);ie.worker=ie.browser&&ie.offscreen?typeof WorkerGlobalScope!="undefined":void 0,await u0e()}async function h6(e){ie=fn(ie,e)}var o0=2048,ft=null,Xt=null,Bo=null,Ut;function Ts(e,t){let n;if(ie.browser)if(ie.offscreen)n=new OffscreenCanvas(e,t);else{if(typeof document=="undefined")throw new Error("attempted to run in web worker but offscreenCanvas is not supported");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof ie.Canvas!="undefined"?n=new ie.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Bx(e,t){let n=t||Ts(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}function yc(e,t,n=!0){if(!e)return t.debug&&ae("input is missing"),{tensor:null,canvas:null};if(!(e instanceof Ke)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ie.Canvas!="undefined"&&e instanceof ie.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input type is not recognized");if(e instanceof Ke){if(e.isDisposedInternal)throw new Error("input tensor is disposed");if(!e.shape||e.shape.length!==4||e.shape[0]!==1||e.shape[3]!==3)throw new Error(`input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);return{tensor:ir(e),canvas:t.filter.return?Xt:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&ae("input stream is not ready"),{tensor:null,canvas:ft};let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!r)return t.debug&&ae("cannot determine input dimensions"),{tensor:null,canvas:ft};let a=s,o=r;if(a>o0&&(a=o0,o=Math.trunc(a*r/s)),o>o0&&(o=o0,a=Math.trunc(o*s/r)),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("input cannot determine dimension");(!ft||(ft==null?void 0:ft.width)!==a||(ft==null?void 0:ft.height)!==o)&&(ft=Ts(a,o));let i=ft.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,ft==null?void 0:ft.width,ft==null?void 0:ft.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,ft==null?void 0:ft.width,ft==null?void 0:ft.height),(!Xt||ft.width!==Xt.width||(ft==null?void 0:ft.height)!==(Xt==null?void 0:Xt.height))&&(Xt=Ts(ft.width,ft.height)),t.filter.enabled&&ie.webgl.supported){if(Ut||(Ut=ie.browser?new p6({canvas:Xt}):null),ie.filter=!!Ut,!Ut)return{tensor:null,canvas:ft};Ut.reset(),Ut.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Ut.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ut.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ut.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Ut.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Ut.addFilter("hue",t.filter.hue),t.filter.negative&&Ut.addFilter("negative"),t.filter.sepia&&Ut.addFilter("sepia"),t.filter.vintage&&Ut.addFilter("brownie"),t.filter.sepia&&Ut.addFilter("sepia"),t.filter.kodachrome&&Ut.addFilter("kodachrome"),t.filter.technicolor&&Ut.addFilter("technicolor"),t.filter.polaroid&&Ut.addFilter("polaroid"),t.filter.pixelate!==0&&Ut.addFilter("pixelate",t.filter.pixelate),Xt=Ut.apply(ft)}else 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d0e=[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],p0e=[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],h0e=[33,133,362,263,1,78,308],Cge=d0e.map(e=>Tp[e]),Tge=p0e.map(e=>Tp[e]),Nge=h0e.map(e=>Tp[e]);var m6=e=>({startPoint:_e(e,[0,0],[-1,2]),endPoint:_e(e,[0,2],[-1,2])});var Np=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],i0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],jx=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],qx=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],g6=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s}},Xx=(e,t,n)=>{let s=t.shape[1],r=t.shape[2];return $e.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n)},Ep=(e,t=1.5)=>{let n=i0(e),s=Np(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks}},Rp=e=>{let t=i0(e),n=Np(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks}},l0=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},u0=[[1,0,0],[0,1,0],[0,0,1]],f0e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),m0e=(e,t)=>f0e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var y6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Tl=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},g0e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},A6=(e,t)=>{let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Tl(e[r],g0e(t,a)))}return n},x6=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=y6(t[0],t[1]),o=A6(a,r),i=y6(-t[0],-t[1]);return A6(o,i)},y0e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Tl(t[0],n),-Tl(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},A0e=(e,t)=>[Tl(e,t[0]),Tl(e,t[1])];function b6(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let c=r*(l+.5);for(let u=0;u<o;u++){let d=r*(u+.5);for(let p=0;p<i;p++)n.push([d,c])}}}return n}function v6(e,t,n,s,r){let a=Np({startPoint:t.startPoint,endPoint:t.endPoint}),o=e.map(d=>[a[0]/r*(d[0]-r/2),a[1]/r*(d[1]-r/2),d[2]||0]),i=n!==0?x6(n,[0,0]):u0,l=n!==0?o.map(d=>[...A0e(d,i),d[2]]):o,c=n!==0?y0e(s):u0,u=[...i0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return l.map(d=>[Math.round(d[0]+Tl(u,c[0])),Math.round(d[1]+Tl(u,c[1])),Math.round(d[2]||0)])}function Kx(e,t,n){let s=e.landmarks.length>=Gx.count?Gx.symmetryLine:Cp.symmetryLine,r=m0e(e.landmarks[s[0]],e.landmarks[s[1]]),a=i0({startPoint:e.startPoint,endPoint:e.endPoint}),o=[a[0]/t.shape[2],a[1]/t.shape[1]],i=$e.rotateWithOffset(t,r,0,o),l=x6(-r,a),c=Xx({startPoint:e.startPoint,endPoint:e.endPoint},i,[n,n]),u=fe(c,255);return te(c),te(i),[r,l,u]}var w6=6,Vs,Zx=[],k6=null,Us=0,$p=()=>Us;async function I6(e){var t;return ie.initial&&(Vs=null),Vs?e.debug&&ae("cached model:",Vs.modelUrl):(Vs=await ut(ct(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Vs||!Vs.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",Vs.modelUrl)),Us=Vs.inputs[0].shape?Vs.inputs[0].shape[2]:0,Us===-1&&(Us=64),Zx=b6(Us),k6=dr(Zx),Vs}function x0e(e){let t=_e(e,[0,1],[-1,2]),n=ue(t,k6),s=_e(e,[0,3],[-1,2]),r=fe(s,Us),a=fe(n,Us),o=fe(r,2),i=xe(a,o),l=ue(a,o),c=L(i,Us),u=L(l,Us);return Ru([c,u],1)}async function S6(e,t){var c,u,d,p;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let[n,s,r]=j(()=>{let h=$e.resizeBilinear(e,[Us,Us]),f=xe(fe(h,127.5),.5),m=Vs==null?void 0:Vs.execute(f),g;if(Array.isArray(m)){let b=m.sort((N,R)=>N.size-R.size),w=kt([b[0],b[2]],2),k=kt([b[1],b[3]],2),S=kt([k,w],1);g=dt(S,0)}else g=dt(m);let y=x0e(g),A=_e(g,[0,0],[-1,1]),x=dt(ns(A));return[g,y,x]}),a=await $e.nonMaxSuppressionAsync(s,r,((c=t.face.detector)==null?void 0:c.maxDetected)||0,((u=t.face.detector)==null?void 0:u.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0),o=await a.array();te(a);let i=[],l=await r.data();for(let h=0;h<o.length;h++){let f=l[o[h]];if(f>(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m=_e(s,[o[h],0],[1,-1]),g=j(()=>G(dt(_e(n,[o[h],w6-1],[1,-1])),[w6,-1]));i.push({box:m6(m),landmarks:g,anchor:Zx[o[h]],confidence:f}),te(m)}}return te(n),te(s),te(r),{boxes:i,scaleFactor:[e.shape[2]/Us,e.shape[1]/Us]}}var nr,Wo=0,b0e=2.3,Yx=Lr.leftEyeLower0,Jx=Lr.rightEyeLower0,Ac={leftBounds:[Yx[0],Yx[Yx.length-1]],rightBounds:[Jx[0],Jx[Jx.length-1]]},xc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function C6(e){var t;return ie.initial&&(nr=null),nr?e.debug&&ae("cached model:",nr.modelUrl):(nr=await ut(ct(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!nr||!nr.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",nr.modelUrl)),Wo=nr.inputs[0].shape?nr.inputs[0].shape[2]:0,Wo===-1&&(Wo=64),nr}function c0(e,t,n,s){for(let r=0;r<Hx.length;r++){let{key:a,indices:o}=Hx[r],i=Lr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let c=o[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var v0e=e=>{let t=e[Ac.leftBounds[0]][2],n=e[Ac.rightBounds[0]][2];return t-n},T6=(e,t,n,s,r=!1,a)=>{let o=Rp(Ep(l0([e[n],e[s]]),b0e)),i=Np(o),l=$e.cropAndResize(t,[[o.startPoint[1]/a,o.startPoint[0]/a,o.endPoint[1]/a,o.endPoint[0]/a]],[0],[Wo,Wo]);if(r&&ie.kernels.includes("flipleftright")){let c=$e.flipLeftRight(l);te(l),l=c}return{box:o,boxSize:i,crop:l}},N6=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<xc.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/Wo:o/Wo)*n[0]+t.startPoint[0],i/Wo*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(xc.index)}},E6=(e,t,n)=>{let s=e[Lr[`${n}EyeUpper0`][xc.upperCenter]][2],r=e[Lr[`${n}EyeLower0`][xc.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function R6(e,t,n,s){if(!nr)return n.debug&&ae("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=T6(e,t,Ac.leftBounds[0],Ac.leftBounds[1],!0,s),{box:i,boxSize:l,crop:c}=T6(e,t,Ac.rightBounds[0],Ac.rightBounds[1],!0,s),u=kt([o,c]);te(o),te(c);let d=nr.predict(u);te(u);let p=await d.data();te(d);let h=p.slice(0,xc.numCoordinates*3),{rawCoords:f,iris:m}=N6(h,r,a,!0),g=p.slice(xc.numCoordinates*3),{rawCoords:y,iris:A}=N6(g,i,l),x=v0e(e);Math.abs(x)<30?(c0(e,f,"left",null),c0(e,y,"right",null)):x<1?c0(e,f,"left",["EyeUpper0","EyeLower0"]):c0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=E6(e,m,"left"),w=E6(e,A,"right");return e.concat(b).concat(w)}var Br=[],sr=null,vr=0,Qx=Number.MAX_SAFE_INTEGER,$6=0;async function D6(e,t){var a,o,i,l,c,u,d,p,h,f,m,g;if(!t.skipFrame||($6!==((a=t.face.detector)==null?void 0:a.maxDetected)||!((o=t.face.mesh)==null?void 0:o.enabled))&&Qx>(((i=t.face.detector)==null?void 0:i.skipFrames)||0)){let y=await S6(e,t);Br=[];for(let A of y.boxes){let x=await A.box.startPoint.data(),b=await A.box.endPoint.data(),w=await A.landmarks.array();Br.push({startPoint:x,endPoint:b,landmarks:w,confidence:A.confidence})}y.boxes.forEach(A=>te([A.box.startPoint,A.box.endPoint,A.landmarks]));for(let A=0;A<Br.length;A++){let x=g6({startPoint:Br[A].startPoint,endPoint:Br[A].endPoint},y.scaleFactor),b=Ep(x),w=Rp(b);Br[A]={...w,confidence:Br[A].confidence,landmarks:Br[A].landmarks}}Qx=0}else Qx++;let n=[],s=[],r=0;for(let y of Br){let A=0,x,b={id:r++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if(((l=t.face.detector)==null?void 0:l.rotation)&&((c=t.face.mesh)==null?void 0:c.enabled)&&ie.kernels.includes("rotatewithoffset"))[A,x,b.tensor]=Kx(y,e,vr);else{x=u0;let w=Xx({startPoint:y.startPoint,endPoint:y.endPoint},e,((u=t.face.mesh)==null?void 0:u.enabled)?[vr,vr]:[$p(),$p()]);b.tensor=fe(w,255),te(w)}if(b.boxScore=Math.round(100*y.confidence)/100,(d=t.face.mesh)==null?void 0:d.enabled)if(!sr)t.debug&&ae("face mesh detection requested, but model is not loaded");else{let[w,k,S]=sr.execute(b.tensor);te(w);let N=(await k.data())[0];te(k);let R=G(S,[-1,3]),P=await R.array();if(te(S),te(R),N<(((p=t.face.detector)==null?void 0:p.minConfidence)||1))y.confidence=N;else{((h=t.face.iris)==null?void 0:h.enabled)&&(P=await R6(P,b.tensor,t,vr)),b.mesh=v6(P,y,A,x,vr),b.meshRaw=b.mesh.map($=>[$[0]/(e.shape[2]||0),$[1]/(e.shape[1]||0),($[2]||0)/vr]),y={...Ep(l0(b.mesh),1.5),confidence:y.confidence};for(let $ of Object.keys(Lr))b.annotations[$]=Lr[$].map(D=>b.mesh[D]);((f=t.face.detector)==null?void 0:f.rotation)&&t.face.mesh.enabled&&((m=t.face.description)==null?void 0:m.enabled)&&ie.kernels.includes("rotatewithoffset")&&(te(b.tensor),[A,x,b.tensor]=Kx(y,e,vr)),b.box=jx(y,e),b.boxRaw=qx(y,e),b.score=Math.round(100*N||100*y.confidence||0)/100,b.faceScore=Math.round(100*N)/100,y={...Rp(y),confidence:y.confidence,faceConfidence:N}}}else{b.box=jx(y,e),b.boxRaw=qx(y,e),b.score=Math.round(100*y.confidence||0)/100,b.mesh=y.landmarks.map(w=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*w[0]/$p(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*w[1]/$p()]),b.meshRaw=b.mesh.map(w=>[w[0]/(e.shape[2]||0),w[1]/(e.shape[1]||0),(w[2]||0)/vr]);for(let w of Object.keys(Cp))b.annotations[w]=[b.mesh[Cp[w]]]}n.push(b),s.push(y)}return((g=t.face.mesh)==null?void 0:g.enabled)&&(Br=s.filter(y=>{var A;return y.confidence>(((A=t.face.detector)==null?void 0:A.minConfidence)||0)})),$6=n.length,n}async function _6(e){var t;return ie.initial&&(sr=null),sr?e.debug&&ae("cached model:",sr.modelUrl):(sr=await ut(ct(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!sr||!sr.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",sr.modelUrl)),vr=sr.inputs[0].shape?sr.inputs[0].shape[2]:0,vr===-1&&(vr=64),sr}var P6=Cl,F6=Tp;var Kn,d0=[],O6=0,eb=Number.MAX_SAFE_INTEGER;async function M6(e){var n,s;let t=ct(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return ie.initial&&(Kn=null),Kn?e.debug&&ae("cached model:",t):(Kn=await ut(t),Kn?e.debug&&ae("load model:",t):ae("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),Kn}function tb(e){return j(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Ke))return null;let s=[[.05,.15,.85,.85]];if(!(Kn==null?void 0:Kn.inputs[0].shape))return null;let r=n.shape.length===3?$e.cropAndResize(Ht(n,0),s,[0],[Kn.inputs[0].shape[2],Kn.inputs[0].shape[1]]):$e.cropAndResize(n,s,[0],[Kn.inputs[0].shape[2],Kn.inputs[0].shape[1]]);return L(r,255)})}async function nb(e,t,n,s){var r,a,o;return Kn?eb<(((r=t.face.description)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&O6===s&&((a=d0[n])==null?void 0:a.age)&&((o=d0[n])==null?void 0:o.age)>0?(eb++,d0[n]):(eb=0,new Promise(async i=>{var d,p;let l=tb(e),c,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(((d=t.face.description)==null?void 0:d.enabled)&&(c=await(Kn==null?void 0:Kn.predict(l))),te(l),c){let h=await c.find(b=>b.shape[1]===1).data(),f=Math.trunc(200*Math.abs(h[0]-.5))/100;f>(((p=t.face.description)==null?void 0:p.minConfidence)||0)&&(u.gender=h[0]<=.5?"female":"male",u.genderScore=Math.min(.99,f));let m=Fs(c.find(b=>b.shape[1]===100),1),g=(await m.data())[0];te(m);let y=await c.find(b=>b.shape[1]===100).data();u.age=Math.round(y[g-1]>y[g+1]?10*g-100*y[g-1]:10*g+100*y[g+1])/10;let x=await c.find(b=>b.shape[1]===1024).data();u.descriptor=[...x],c.forEach(b=>te(b))}d0[n]=u,O6=s,i(u)})):null}var w0e=["angry","disgust","fear","happy","sad","surprise","neutral"],un,p0=[],z6=0,sb=Number.MAX_SAFE_INTEGER,rb=[.2989,.587,.114];async function L6(e){var t;return ie.initial&&(un=null),un?e.debug&&ae("cached model:",un.modelUrl):(un=await ut(ct(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!un||!un.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",un.modelUrl)),un}async function ab(e,t,n,s){var r;return un?sb<(((r=t.face.emotion)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&z6===s&&p0[n]&&p0[n].length>0?(sb++,p0[n]):(sb=0,new Promise(async a=>{var g,y;let o=$e.resizeBilinear(e,[(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[2]:0,(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[1]:0],!1),[i,l,c]=xn(o,3,3);te(o);let u=L(i,rb[0]),d=L(l,rb[1]),p=L(c,rb[2]);te(i),te(l),te(c);let h=nf([u,d,p]);te(u),te(d),te(p);let f=j(()=>L(xe(h,.5),2));te(h);let m=[];if((g=t.face.emotion)==null?void 0:g.enabled){let A=await(un==null?void 0:un.predict(f)),x=await A.data();te(A);for(let b=0;b<x.length;b++)x[b]>(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&m.push({score:Math.min(.99,Math.trunc(100*x[b])/100),emotion:w0e[b]});m.sort((b,w)=>w.score-b.score)}te(f),p0[n]=m,z6=s,a(m)})):null}var Dp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],k0e=Dp.length,_p=Dp.reduce((e,t,n)=>(e[t]=n,e),{}),I0e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],o2e=I0e.map(([e,t])=>[_p[e],_p[t]]),B6=[["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 W6(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function V6(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]}))});return e.map((c,u)=>i(c,u))}var ob=class{constructor(t,n){ve(this,"priorityQueue");ve(this,"numberOfElements");ve(this,"getElementValue");this.priorityQueue=new 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s={};s.batched=this.model.predict(t),s.predictions=dt(s.batched),s.scores=j(()=>dt(ns(_e(s.predictions,[0,0],[-1,1]))));let r=await s.scores.data();s.boxes=_e(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await $e.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=_e(s.norm,[i,0],[1,-1]),c=j(()=>G(this.normalizeLandmarks(_e(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:c,confidence:r[i]})}for(let i of Object.keys(s))te(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=j(()=>xe(fe($e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);te(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array();te(l.box),te(l.palmLandmarks),i.push(X6({startPoint:u,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function D0e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Z6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return D0e(n)}var Y6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Vo(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function _0e(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function J6(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Vo(e[r],_0e(t,a)))}return n}function hb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=Y6(t[0],t[1]),o=J6(a,r),i=Y6(-t[0],-t[1]);return J6(o,i)}function Q6(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Vo(t[0],n),-Vo(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function fb(e,t){return[Vo(e,t[0]),Vo(e,t[1])]}var 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g=Math.sqrt(r*r+i*i),y=Math.sqrt(a*a+l*l),A=Math.sqrt(o*o+c*c),x=Math.max(g,y,A),b=e[0],w=e[1],k=n[0],S=n[1];x===g?(k=n[0],S=n[1]):x===A&&(b=t[0],w=t[1]);let P=r8([b,w],[k,S]),$=a8(P,Nl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],h+=$[1],f+=$[2];for(let T of s){let O=a8(T,Nl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let D;return p===Math.max(p,h,f)?D=i8(l,i,c,d):f===Math.max(h,f)?D=o8(a,r,o,u):D=L0e(l,i,c,d,a,r,o,u),D}function l8(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Je.all){let o=Je.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=r8(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Je.all){let o=a===Je.thumb?1:0,i=Je.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=z0e(l,c,u),p=B0e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function A0(e){if(!e||e.length===0)return null;let t=l8(e),n={};for(let s of 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i=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]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=A0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function yb(e){var n,s,r,a,o,i;ie.initial&&(ca=null,da=null),!ca||!da?([ca,da]=await Promise.all([e.hand.enabled?ut(ct(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 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n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function vc(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function Fp(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var Rt=[null,null],W0e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Go=[[0,0],[0,0]],V0e=["hand","fist","pinch","point","face","tip","pinchtip"],h8=4,f8=1.6,U0e=512,G0e=1.4,x0=0,Ho=[0,0],ds={boxes:[],hands:[]},m8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function g8(e){var t;if(ie.initial&&(Rt[0]=null),Rt[0])e.debug&&ae("cached model:",Rt[0].modelUrl);else{wc(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Rt[0]=await ut(ct(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let n=Object.values(Rt[0].modelSignature.inputs);Go[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Go[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Rt[0]||!Rt[0].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Rt[0].modelUrl)}return Rt[0]}async function y8(e){var t;if(ie.initial&&(Rt[1]=null),Rt[1])e.debug&&ae("cached model:",Rt[1].modelUrl);else{Rt[1]=await ut(ct(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let n=Object.values(Rt[1].modelSignature.inputs);Go[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Go[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Rt[1]||!Rt[1].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Rt[1].modelUrl)}return Rt[1]}async function H0e(e,t){let n=[];if(!e||!Rt[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,U0e),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Rt[0].executeAsync(s.cast,W0e),s.boxes=dt(s.rawBoxes,[0,2]),s.scores=dt(s.rawScores,[0]);let i=Wn(s.scores,1);te(i[h8]),i.splice(h8,1),s.filtered=Tn(i,1),te(i),s.max=Bn(s.filtered,1),s.argmax=Fs(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=_e(s.boxes,p,1),f=await h.data();te(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=vc(m,G0e),y=Fp(g),A=[Math.trunc(m[0]*Ho[0]),Math.trunc(m[1]*Ho[1]),Math.trunc(m[2]*Ho[0]),Math.trunc(m[3]*Ho[1])],x=u[p],b=V0e[d[p]],w={id:l++,score:x,box:A,boxRaw:g,boxCrop:y,label:b};n.push(w)}return Object.keys(s).forEach(p=>te(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function A8(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Rt[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[Go[1][0],Go[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=fe(r.cast,255),[r.score,r.keypoints]=Rt[1].execute(r.div);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=G(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/Go[1][1],u[1]/Go[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[Ho[0]*(u[0]+t.boxRaw[0]),Ho[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=A0(s.keypoints);for(let u of Object.keys(m8))s.annotations[u]=m8[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>te(r[i]))}return s}async function xb(e,t){var n,s;return!Rt[0]||!Rt[1]||!((n=Rt[0])==null?void 0:n.inputs[0].shape)||!((s=Rt[1])==null?void 0:s.inputs[0].shape)?[]:(Ho=[e.shape[2]||0,e.shape[1]||0],x0++,t.skipFrame&&x0<=(t.hand.skipFrames||0)?ds.hands:new Promise(async r=>{t.skipFrame&&x0<5*(t.hand.skipFrames||0)&&ds.hands.length>0?ds.hands=await Promise.all(ds.boxes.map(o=>A8(e,o,t))):(ds.boxes=await H0e(e,t),ds.hands=await Promise.all(ds.boxes.map(o=>A8(e,o,t))),x0=0);let a=[...ds.boxes];if(ds.boxes.length=0,t.cacheSensitivity>0)for(let o=0;o<ds.hands.length;o++){let i=p8(ds.hands[o].keypoints,Ho);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&ds.hands[o].fingerScore&&ds.hands[o].fingerScore>(t.hand.minConfidence||0)){let l=vc(i.box,f8),c=vc(i.boxRaw,f8),u=Fp(c);ds.boxes.push({...a[o],box:l,boxRaw:c,boxCrop:u})}}r(ds.hands)}))}var wb={};Mc(wb,{connected:()=>vb,kpt:()=>bb});var bb=["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","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],vb={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var x8={initial:!0},cn=[null,null],jo=[[0,0],[0,0]],kb=Number.MAX_SAFE_INTEGER,Ib,Sb=null,qo=[[0,0],[0,0],[0,0],[0,0]];async function b8(e){var t,n;if(x8.initial&&(cn[0]=null),!cn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){cn[0]=await ut(ct(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let s=Object.values(cn[0].modelSignature.inputs);jo[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,jo[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!cn[0]||!cn[0].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",cn[0].modelUrl)}else e.debug&&cn[0]&&ae("cached model:",cn[0].modelUrl);return cn[0]}async function v8(e){var t;if(x8.initial&&(cn[1]=null),cn[1])e.debug&&ae("cached model:",cn[1].modelUrl);else{cn[1]=await ut(ct(e.modelBasePath,e.body.modelPath||""));let n=Object.values(cn[1].modelSignature.inputs);jo[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,jo[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 0:t.includes("lite"))?Ib=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:Ib=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!cn[1]||!cn[1].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",cn[1].modelUrl)}return cn[1]}function j0e(e,t){let n=e.map(o=>o.position[0]),s=e.map(o=>o.position[1]),r=[Math.min(...n),Math.min(...s),Math.max(...n)-Math.min(...n),Math.max(...s)-Math.min(...s)],a=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{keypointsBox:r,keypointsBoxRaw:a}}async function q0e(e){let t={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;qo=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],t.pad=ur(e,qo),t.resize=$e.resizeBilinear(t.pad,[jo[1][0],jo[1][1]]);let n=fe(t.resize,255);return Object.keys(t).forEach(s=>te(t[s])),n}function X0e(e,t){for(let n of e)n.position=[n.position[0]*(t[0]+qo[2][0]+qo[2][1])/t[0]-qo[2][0],n.position[1]*(t[1]+qo[1][0]+qo[1][1])/t[1]-qo[1][0],n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];return e}async function K0e(e,t,n){var d;let s={};s.input=await q0e(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=await((d=cn[1])==null?void 0:d.execute(s.input,Ib));let r=await s.ld.data(),a=[],o=5;for(let p=0;p<r.length/o;p++){let h=(100-Math.trunc(100/(1+Math.exp(r[o*p+3]))))/100,f=[r[o*p+0]/jo[1][0],r[o*p+1]/jo[1][1],r[o*p+2]+0],m=[Math.trunc(n[0]*f[0]),Math.trunc(n[1]*f[1]),f[2]];a.push({part:bb[p],positionRaw:f,position:m,score:h})}let i=Math.round(100*a.reduce((p,h)=>p+=h.score,0)/a.length)/100;if(i<(t.body.minConfidence||0))return null;let l=X0e(a,n),c=j0e(l,[n[0],n[1]]);Object.keys(s).forEach(p=>te(s[p]));let u={};for(let[p,h]of Object.entries(vb)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(A=>A.part===h[m]),y=l.find(A=>A.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}u[p]=f}return{id:0,score:i,box:c.keypointsBox,boxRaw:c.keypointsBoxRaw,keypoints:l,annotations:u}}async function Cb(e,t){let n=[e.shape[2]||0,e.shape[1]||0];return kb<(t.body.skipFrames||0)&&t.skipFrame?kb++:(Sb=await K0e(e,t,n),kb=0),Sb?[Sb]:[]}var Eb={};Mc(Eb,{connected:()=>Nb,kpt:()=>Tb});var Tb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Nb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var dn,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Rb=Number.MAX_SAFE_INTEGER;async function $b(e){return ie.initial&&(dn=null),dn?e.debug&&ae("cached model:",dn.modelUrl):(dn=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!dn||!dn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",dn.modelUrl)),dn}function Z0e(e,t){let[n,s]=e.shape;return j(()=>{let r=(i,l)=>xe(i,L(fe(i,Ee(l,"int32")),Ee(l,"int32"))),a=G(e,[s*n]),o=Bn(a,0).dataSync()[0];if(o>t){let i=Fs(a,0),l=r(i,n).dataSync()[0],c=fe(i,Ee(n,"int32")).dataSync()[0];return[l,c,o]}return[0,0,o]})}async function Db(e,t){var n;return Rb<(((n=t.body)==null?void 0:n.skipFrames)||0)&&t.skipFrame&&Object.keys(Zn.keypoints).length>0?(Rb++,[Zn]):(Rb=0,new Promise(async s=>{var u;let r=j(()=>{if(!(dn==null?void 0:dn.inputs[0].shape))return null;let d=$e.resizeBilinear(e,[dn.inputs[0].shape[2],dn.inputs[0].shape[1]],!1);return L(d,2).sub(1)}),a;if(t.body.enabled&&(a=await(dn==null?void 0:dn.predict(r))),te(r),a){Zn.keypoints.length=0;let d=a.squeeze();te(a);let p=d.unstack(2);te(d);for(let h=0;h<p.length;h++){let[f,m,g]=Z0e(p[h],t.body.minConfidence);g>(((u=t.body)==null?void 0:u.minConfidence)||0)&&Zn.keypoints.push({score:Math.round(100*g)/100,part:Tb[h],positionRaw:[f/dn.inputs[0].shape[2],m/dn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/dn.inputs[0].shape[2]),Math.round(e.shape[1]*m/dn.inputs[0].shape[1])]})}p.forEach(h=>te(h))}Zn.score=Zn.keypoints.reduce((d,p)=>p.score>d?p.score:d,0);let o=Zn.keypoints.map(d=>d.position[0]),i=Zn.keypoints.map(d=>d.position[1]);Zn.box=[Math.min(...o),Math.min(...i),Math.max(...o)-Math.min(...o),Math.max(...i)-Math.min(...i)];let l=Zn.keypoints.map(d=>d.positionRaw[0]),c=Zn.keypoints.map(d=>d.positionRaw[1]);Zn.boxRaw=[Math.min(...l),Math.min(...c),Math.max(...l)-Math.min(...l),Math.max(...c)-Math.min(...c)];for(let[d,p]of Object.entries(Nb)){let h=[];for(let f=0;f<p.length-1;f++){let m=Zn.keypoints.find(y=>y.part===p[f]),g=Zn.keypoints.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}Zn.annotations[d]=h}s([Zn])}))}var Pb={};Mc(Pb,{connected:()=>b0,kpt:()=>Op,pairs:()=>_b});var Op=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_b=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],b0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var pn,El=0,Y0e=1.5,Dn={boxes:[],bodies:[]},Fb=Number.MAX_SAFE_INTEGER,ps=[];async function w8(e){return ie.initial&&(pn=null),pn?e.debug&&ae("cached model:",pn.modelUrl):(wc(["size"],e),pn=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!pn||!pn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",pn.modelUrl)),El=pn.inputs[0].shape?pn.inputs[0].shape[2]:0,El===-1&&(El=256),pn}function k8(){for(let e of _b){let t=ps.find(s=>s.part===e[0]),n=ps.find(s=>s.part===e[1]);if(t&&n&&t.position[0]>n.position[0]){let s=t;t=n,n=s}}}async function I8(e,t,n,s){let r=e[0][0];ps.length=0;let a=0;for(let c=0;c<r.length;c++)if(a=r[c][2],a>t.body.minConfidence){let u=[(s[3]-s[1])*r[c][1]+s[1],(s[2]-s[0])*r[c][0]+s[0]];ps.push({score:Math.round(100*a)/100,part:Op[c],positionRaw:u,position:[Math.round((n.shape[2]||0)*u[0]),Math.round((n.shape[1]||0)*u[1])]})}k8(),a=ps.reduce((c,u)=>u.score>c?u.score:c,0);let o=[],i=Ab(ps.map(c=>c.position),[n.shape[2],n.shape[1]]),l={};for(let[c,u]of Object.entries(b0)){let d=[];for(let p=0;p<u.length-1;p++){let h=ps.find(m=>m.part===u[p]),f=ps.find(m=>m.part===u[p+1]);h&&f&&h.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&d.push([h.position,f.position])}l[c]=d}return o.push({id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:ps,annotations:l}),o}async function S8(e,t,n,s){let r=[];for(let a=0;a<e[0].length;a++){let o=e[0][a],i=Math.round(100*o[51+4])/100;if(i>t.body.minConfidence){ps.length=0;for(let u=0;u<17;u++){let d=o[3*u+2];if(d>t.body.minConfidence){let p=[(s[3]-s[1])*o[3*u+1]+s[1],(s[2]-s[0])*o[3*u+0]+s[0]];ps.push({part:Op[u],score:Math.round(100*d)/100,positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}}k8();let l=Ab(ps.map(u=>u.position),[n.shape[2],n.shape[1]]),c={};for(let[u,d]of Object.entries(b0)){let p=[];for(let h=0;h<d.length-1;h++){let f=ps.find(g=>g.part===d[h]),m=ps.find(g=>g.part===d[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}c[u]=p}r.push({id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...ps],annotations:c})}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function Ob(e,t){return!pn||!(pn==null?void 0:pn.inputs[0].shape)?[]:(t.skipFrame||(Dn.boxes.length=0),Fb++,t.skipFrame&&Fb<=(t.body.skipFrames||0)?Dn.bodies:new Promise(async n=>{let s={};if(Fb=0,Dn.bodies=[],Dn.boxes.length>=(t.body.maxDetected||0))for(let r=0;r<Dn.boxes.length;r++){s.crop=$e.cropAndResize(e,[Dn.boxes[r]],[0],[El,El],"bilinear"),s.cast=pe(s.crop,"int32"),s.res=await(pn==null?void 0:pn.predict(s.cast));let a=await s.res.array(),o=s.res.shape[2]===17?await I8(a,t,e,Dn.boxes[r]):await S8(a,t,e,Dn.boxes[r]);Dn.bodies=Dn.bodies.concat(o),Object.keys(s).forEach(i=>te(s[i]))}if(Dn.bodies.length!==t.body.maxDetected){s.resized=$e.resizeBilinear(e,[El,El],!1),s.cast=pe(s.resized,"int32"),s.res=await(pn==null?void 0:pn.predict(s.cast));let r=await s.res.array();Dn.bodies=s.res.shape[2]===17?await I8(r,t,e,[0,0,1,1]):await S8(r,t,e,[0,0,1,1]),Object.keys(s).forEach(a=>te(s[a]))}Dn.boxes.length=0;for(let r=0;r<Dn.bodies.length;r++)if(Dn.bodies[r].keypoints.length>Op.length/2){let a=vc(Dn.bodies[r].boxRaw,Y0e),o=Fp(a);Dn.boxes.push(o)}n(Dn.bodies)}))}var kc=[{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 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p=u.box[0]+u.box[2]/2-u.box[3]*Ic(u.rotation.angle.yaw)/90,h=u.box[1]+u.box[3]/2+u.box[2]*Ic(u.rotation.angle.pitch)/90,f=new Path2D(`
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
C
${p} ${u.box[1]},
${p} ${u.box[1]+u.box[3]},
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`),m=new Path2D(`
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
C
${u.box[0]} ${h},
${u.box[0]+u.box[2]} ${h},
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r=$l(e);r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(zp(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Gb(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)tge(r,l,s)}}async function Xb(e,t,n){let s=fn(pa,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,zp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Gb(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+2*i[l][2]}, ${127.5-2*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function Kb(e,t,n){let s=fn(pa,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,zp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function F8(e,t,n){let s=fn(pa,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,zp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}async function O8(e,t){if(!e||!t)return;$l(t).drawImage(e,0,0)}async function M8(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ot(),r=fn(pa,n),a=Promise.all([jb(e,t.face,r),qb(e,t.body,r),Xb(e,t.hand,r),Kb(e,t.object,r),Hb(e,t.gesture,r)]);return t.performance.draw=Math.trunc(ot()-s),a}var nge=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},z8=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},s=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],b=g[2]-y[2];return[A,x,b]},r=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],b=g[0]*y[1]-g[1]*y[0];return[A,x,b]},a=g=>{let[y,A,x,b,w,k,S,N,R]=g,P,$,D;return b<1?b>-1?(D=Math.asin(b),$=Math.atan2(-S,y),P=Math.atan2(-k,w)):(D=-Math.PI/2,$=-Math.atan2(N,R),P=0):(D=Math.PI/2,$=Math.atan2(N,R),P=0),isNaN(P)&&(P=0),isNaN($)&&($=0),isNaN(D)&&(D=0),{pitch:2*-P,yaw:2*-$,roll:2*-D}},o=g=>{let y=(x,b,w,k)=>Math.atan2(k-b,w-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.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 l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?nge(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var Zb=async(e,t)=>{var d,p,h,f;let n,s,r,a,o,i,l,c=[];e.state="run:face",n=ot();let u=await D6(t,e.config);if(e.performance.face=Math.trunc(ot()-n),!t.shape||t.shape.length!==4)return[];if(!u)return[];for(let m=0;m<u.length;m++){if(e.analyze("Get Face"),!u[m].tensor||u[m].tensor.isDisposedInternal){ae("Face object is disposed:",u[m].tensor);continue}let g=z8(u[m],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?ab(u[m].tensor||nn([]),e.config,m,u.length):{}:(e.state="run:emotion",n=ot(),o=e.config.face.emotion.enabled?await ab(u[m].tensor||nn([]),e.config,m,u.length):{},e.performance.emotion=Math.trunc(ot()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?l=e.config.face.description.enabled?nb(u[m].tensor||nn([]),e.config,m,u.length):[]:(e.state="run:description",n=ot(),l=e.config.face.description.enabled?await nb(u[m].tensor||nn([]),e.config,m,u.length):[],e.performance.embedding=Math.trunc(ot()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,r]=await Promise.all([s,a,o,i,l,r])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=u[m])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((f=(h=u[m])==null?void 0:h.annotations)==null?void 0:f.rightEyeIris)&&(delete u[m].annotations.leftEyeIris,delete u[m].annotations.rightEyeIris);let y=u[m].annotations&&u[m].annotations.leftEyeIris&&u[m].annotations.leftEyeIris[0]&&u[m].annotations.rightEyeIris&&u[m].annotations.rightEyeIris[0]&&u[m].annotations.leftEyeIris.length>0&&u[m].annotations.rightEyeIris.length>0&&u[m].annotations.leftEyeIris[0]!==null&&u[m].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(u[m].annotations.leftEyeIris[3][0]-u[m].annotations.leftEyeIris[1][0]),Math.abs(u[m].annotations.rightEyeIris[4][1]-u[m].annotations.rightEyeIris[2][1]))/t.shape[2]:0,A=e.config.face.detector.return?dt(u[m].tensor):null;te(u[m].tensor),u[m].tensor&&delete u[m].tensor,c.push({...u[m],id:m,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:y!==0?Math.trunc(500/y/11.7)/100:0,rotation:g,tensor:A}),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),c};var L8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},B8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},W8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(c=!1),p>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},V8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=u8(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Pe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function U8(e,t){var o,i,l,c,u,d,p,h,f,m,g,y,A,x,b,w,k,S,N,R,P,$,D,T,O,B,H;let n=performance.now();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(Pe.canvas=e.canvas,!Pe.body||e.body.length!==Pe.body.length)Pe.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let X=e.body[z].box.map((K,oe)=>((r-1)*Pe.body[z].box[oe]+K)/r),ee=e.body[z].boxRaw.map((K,oe)=>((r-1)*Pe.body[z].boxRaw[oe]+K)/r),J=e.body[z].keypoints.map((K,oe)=>({score:K.score,part:K.part,position:[Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].position[0]+K.position[0])/r:K.position[0],Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].position[1]+K.position[1])/r:K.position[1]],positionRaw:[Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].positionRaw[0]+K.positionRaw[0])/r:K.position[0],Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].positionRaw[1]+K.positionRaw[1])/r:K.position[1]]})),Q={},ne={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?ne=Eb:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?ne=wb:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(ne=Pb);for(let[K,oe]of Object.entries(ne.connected)){let ce=[];for(let he=0;he<oe.length-1;he++){let Ae=J.find(Ce=>Ce.part===oe[he]),Se=J.find(Ce=>Ce.part===oe[he+1]);Ae&&Se&&Ae.score>(t.body.minConfidence||0)&&Se.score>(t.body.minConfidence||0)&&ce.push([Ae.position,Se.position])}Q[K]=ce}Pe.body[z]={...e.body[z],box:X,boxRaw:ee,keypoints:J,annotations:Q}}if(!Pe.hand||e.hand.length!==Pe.hand.length)Pe.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let X=e.hand[z].box.map((ne,K)=>((r-1)*Pe.hand[z].box[K]+ne)/r),ee=e.hand[z].boxRaw.map((ne,K)=>((r-1)*Pe.hand[z].boxRaw[K]+ne)/r);Pe.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Pe.hand[z].keypoints=e.hand[z].keypoints);let J=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((ne,K)=>ne.map((oe,ce)=>((r-1)*(Pe.hand[z].keypoints[K][ce]||1)+(oe||0))/r)):[],Q={};if(Object.keys(Pe.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Pe.hand[z].annotations=e.hand[z].annotations,Q=Pe.hand[z].annotations;else if(e.hand[z].annotations)for(let ne of Object.keys(e.hand[z].annotations))Q[ne]=e.hand[z].annotations[ne]&&e.hand[z].annotations[ne][0]?e.hand[z].annotations[ne].map((K,oe)=>K.map((ce,he)=>((r-1)*Pe.hand[z].annotations[ne][oe][he]+ce)/r)):null;Pe.hand[z]={...e.hand[z],box:X,boxRaw:ee,keypoints:J,annotations:Q}}if(!Pe.face||e.face.length!==Pe.face.length)Pe.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let X=e.face[z].box.map((Q,ne)=>((r-1)*Pe.face[z].box[ne]+Q)/r),ee=e.face[z].boxRaw.map((Q,ne)=>((r-1)*Pe.face[z].boxRaw[ne]+Q)/r),J={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};J.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,J.angle={roll:((r-1)*(((f=(h=Pe.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((A=(y=Pe.face[z].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[z].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(w=Pe.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((N=(S=e.face[z].rotation)==null?void 0:S.angle)==null?void 0:N.pitch)||0))/r},J.gaze={bearing:((r-1)*(((P=(R=Pe.face[z].rotation)==null?void 0:R.gaze)==null?void 0:P.bearing)||0)+(((D=($=e.face[z].rotation)==null?void 0:$.gaze)==null?void 0:D.bearing)||0))/r,strength:((r-1)*(((O=(T=Pe.face[z].rotation)==null?void 0:T.gaze)==null?void 0:O.strength)||0)+(((H=(B=e.face[z].rotation)==null?void 0:B.gaze)==null?void 0:H.strength)||0))/r},Pe.face[z]={...e.face[z],rotation:J,box:X,boxRaw:ee}}if(!Pe.object||e.object.length!==Pe.object.length)Pe.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let X=e.object[z].box.map((J,Q)=>((r-1)*Pe.object[z].box[Q]+J)/r),ee=e.object[z].boxRaw.map((J,Q)=>((r-1)*Pe.object[z].boxRaw[Q]+J)/r);Pe.object[z]={...e.object[z],box:X,boxRaw:ee}}if(e.persons){let z=e.persons;if(!Pe.persons||z.length!==Pe.persons.length)Pe.persons=JSON.parse(JSON.stringify(z));else for(let X=0;X<z.length;X++)Pe.persons[X].box=z[X].box.map((ee,J)=>((r-1)*Pe.persons[X].box[J]+ee)/r)}e.gesture&&(Pe.gesture=e.gesture);let a=performance.now();return e.performance&&(Pe.performance={...e.performance,interpolate:Math.round(a-n)}),Pe}function S0(e,t,n={order:2}){let s=0;for(let r=0;r<e.length;r++){let a=n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);s+=n.order===2?a*a:a**n.order}return s}function G8(e,t,n={order:2}){let s=S0(e,t,n),r=n.order===2?Math.sqrt(s):s**(1/n.order);return Math.max(0,100-r)/100}function H8(e,t,n={order:2,threshold:0}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let s=Number.MAX_SAFE_INTEGER,r=-1;for(let a=0;a<t.length;a++){let o=S0(e,t[a],{order:n.order});if(o<s&&(s=o,r=a),s<n.threshold)break}return s=n.order===2?Math.sqrt(s):s**(1/n.order),{index:r,distance:s,similarity:Math.max(0,100-s)/100}}function j8(e,t,n,s,r){var i,l,c,u,d,p,h,f,m,g,y,A,x,b,w,k;let a=0,o=[];for(let S of e){let N={id:a++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of t)S.box[0]>O.box[0]&&S.box[0]<O.box[0]+O.box[2]&&S.box[1]+S.box[3]>O.box[1]&&S.box[1]+S.box[3]<O.box[1]+O.box[3]&&(N.body=O);if(N.body)for(let O of n)O.box[0]+O.box[2]>N.body.box[0]&&O.box[0]+O.box[2]<N.body.box[0]+N.body.box[2]&&O.box[1]+O.box[3]>N.body.box[1]&&O.box[1]+O.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.left=O),O.box[0]<N.body.box[0]+N.body.box[2]&&O.box[0]>N.body.box[0]&&O.box[1]+O.box[3]>N.body.box[1]&&O.box[1]+O.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.right=O);for(let O of s)O.face!==void 0&&O.face===S.id?(i=N.gestures)==null||i.push(O):O.iris!==void 0&&O.iris===S.id?(l=N.gestures)==null||l.push(O):O.body!==void 0&&O.body===((c=N.body)==null?void 0:c.id)?(u=N.gestures)==null||u.push(O):O.hand!==void 0&&O.hand===((p=(d=N.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=N.gestures)==null||h.push(O):O.hand!==void 0&&O.hand===((m=(f=N.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=N.gestures)==null||g.push(O));let R=[],P=[],$=O=>{O&&O.length===4&&(R.push(O[0],O[0]+O[2]),P.push(O[1],O[1]+O[3]))};$((y=N.face)==null?void 0:y.box),$((A=N.body)==null?void 0:A.box),$((b=(x=N.hands)==null?void 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2Q==`;async function sge(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(C0);break;case"body":case"full":n=await t(T0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function rge(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+C0;break;case"full":case"body":n="data:image/jpeg;base64,"+T0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:ie.Image&&(s=new ie.Image),s.onload=async()=>{let r=Ts(s.naturalWidth,s.naturalHeight);if(!r)ae("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function age(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(C0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(T0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ae("Warmup tfjs-node not loaded");return s}async function q8(e,t){let n=ot();if(e.state="warmup",t&&(e.config=fn(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await sge(e):typeof Image!="undefined"||ie.Canvas!==void 0?s=await rge(e):s=await age(e);let a=ot();e.config.debug&&ae("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Sc,Lp,Bp,N0,K8=class{constructor(t){ve(this,"version");ve(this,"config");ve(this,"result");ve(this,"state");ve(this,"process");ve(this,"tf");ve(this,"env");ve(this,"draw");ve(this,"models");ve(this,"events");ve(this,"faceTriangulation");ve(this,"faceUVMap");ve(this,"performance");Lc(this,Sc,void 0);Lc(this,Lp,void 0);Lc(this,Bp,void 0);ve(this,"gl");ve(this,"analyze",(...t)=>{if(!zc(this,Lp))return;let n=this.tf.engine().state.numTensors,s=zc(this,Sc);Bc(this,Sc,n);let r=n-s;r!==0&&ae(...t,r)});Lc(this,N0,t=>{if(!zc(this,Bp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Ke))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});ve(this,"similarity",G8);ve(this,"distance",S0);ve(this,"match",H8);ve(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});a0(),this.env=ie,xa.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Jh}/dist/`,xa.modelBasePath=this.env.browser?"../models/":"file://models/",xa.backend=this.env.browser?"humangl":"tensorflow",this.version=Lx,Object.defineProperty(this,"version",{value:Lx}),this.config=JSON.parse(JSON.stringify(xa)),Object.seal(this.config),t&&(this.config=fn(this.config,t)),this.tf=Sl,this.state="idle",Bc(this,Sc,0),Bc(this,Lp,!1),Bc(this,Bp,!1),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Mp,this.draw={options:pa,canvas:(n,s)=>O8(n,s),face:(n,s,r)=>jb(n,s,r),body:(n,s,r)=>qb(n,s,r),hand:(n,s,r)=>Xb(n,s,r),gesture:(n,s,r)=>Hb(n,s,r),object:(n,s,r)=>Kb(n,s,r),person:(n,s,r)=>F8(n,s,r),all:(n,s,r)=>M8(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=P6,this.faceUVMap=F6,this.gl=Bt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(xa)),this.config.backend=t}validate(t){return n2(xa,t||this.config)}image(t,n=!0){return yc(t,this.config,n)}async segmentation(t,n){return N8(t,n,this.config)}enhance(t){return tb(t)}async init(){await I0(this,!0),await this.tf.ready(),h6(this.env)}async load(t){this.state="load";let n=ot(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=fn(this.config,t)),ie.initial&&(this.config.debug&&ae(`version: ${this.version}`),this.config.debug&&ae(`tfjs version: ${this.tf.version_core}`),await I0(this)||ae("error: backend check failed"),await ef(),this.env.browser&&(this.config.debug&&ae("configuration:",this.config),this.config.debug&&ae("tf flags:",this.tf.ENV.flags))),await R8(this),ie.initial&&this.config.debug&&ae("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ie.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await $8(this),this.emit("load"));let a=Math.trunc(ot()-n);a>(this.performance.load||0)&&(this.performance.load=a)}next(t=this.result){return U8(t,this.config)}async warmup(t){return q8(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var y,A,x,b,w,k,S,N,R,P,$,D,T,O,B,H,z,X,ee,J,Q,ne;this.state="config";let r,a;this.config=fn(this.config,n),this.state="check";let o=zc(this,N0).call(this,t);o&&(ae(o,t),s({error:o}));let i=ot();await I0(this),await this.load(),r=ot(),this.state="image";let l=yc(t,this.config);if(this.process=l,this.performance.image=Math.trunc(ot()-r),this.analyze("Get Image:"),!l.tensor){this.config.debug&&ae("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=ot(),this.config.skipFrame=await f6(this.config,l.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(ot()-r),this.analyze("Check Changed:");let c=[],u=[],d=[],p=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?Zb(this,l.tensor):[],this.performance.face&&delete this.performance.face):(r=ot(),c=this.config.face.enabled?await Zb(this,l.tensor):[],a=Math.trunc(ot()-r),a>0&&(this.performance.face=a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let h=this.config.body.maxDetected===-1?fn(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?(((y=this.config.body.modelPath)==null?void 0:y.includes("posenet"))?u=this.config.body.enabled?db(l.tensor,h):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?u=this.config.body.enabled?Cb(l.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?u=this.config.body.enabled?Db(l.tensor,h):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("movenet"))&&(u=this.config.body.enabled?Ob(l.tensor,h):[]),this.performance.body&&delete this.performance.body):(r=ot(),((w=this.config.body.modelPath)==null?void 0:w.includes("posenet"))?u=this.config.body.enabled?await db(l.tensor,h):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?u=this.config.body.enabled?await Cb(l.tensor,h):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("efficientpose"))?u=this.config.body.enabled?await Db(l.tensor,h):[]:((N=this.config.body.modelPath)==null?void 0:N.includes("movenet"))&&(u=this.config.body.enabled?await Ob(l.tensor,h):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?fn(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?(((P=(R=this.config.hand.detector)==null?void 0:R.modelPath)==null?void 0:P.includes("handdetect"))?d=this.config.hand.enabled?gb(l.tensor,f):[]:((D=($=this.config.hand.detector)==null?void 0:$.modelPath)==null?void 0:D.includes("handtrack"))&&(d=this.config.hand.enabled?xb(l.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=ot(),((O=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?await gb(l.tensor,f):[]:((H=(B=this.config.hand.detector)==null?void 0:B.modelPath)==null?void 0:H.includes("handtrack"))&&(d=this.config.hand.enabled?await xb(l.tensor,f):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((z=this.config.object.modelPath)==null?void 0:z.includes("nanodet"))?p=this.config.object.enabled?zb(l.tensor,this.config):[]:((X=this.config.object.modelPath)==null?void 0:X.includes("centernet"))&&(p=this.config.object.enabled?Bb(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ot(),((ee=this.config.object.modelPath)==null?void 0:ee.includes("nanodet"))?p=this.config.object.enabled?await zb(l.tensor,this.config):[]:((J=this.config.object.modelPath)==null?void 0:J.includes("centernet"))&&(p=this.config.object.enabled?await Bb(l.tensor,this.config):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,u,d,p]=await Promise.all([c,u,d,p])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ot(),m=[...B8(c),...L8(u),...V8(d),...W8(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(ot()-r)),this.performance.total=Math.trunc(ot()-i);let g=((ne=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:ne.shape)||[];this.result={face:c,body:u,hand:d,gesture:m,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return j8(c,u,d,m,g)}},te(l.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Sc=new WeakMap,Lp=new WeakMap,Bp=new WeakMap,N0=new WeakMap;return oge;})();
/**
* @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 backend 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 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
* 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 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 See the LICENSE file. */
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