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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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Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}let n;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,r){e();try{let n=r();return t(),n}catch(n){throw t(),n}}nextTensorId(){return cg.nextTensorId++}nextVariableId(){return cg.nextVariableId++}clone(e){let t=B.runKernel(gi,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(ti,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[t],n,a,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,A0(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){let n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=J2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(J2(e)){let{kernelName:c,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=A0(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:S,dtype:T}=b;return this.makeTensorFromDataId(v,S,T)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=J2(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=pg(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&Es(e[0])&&(a=e.map(o=>fh(o)));let s=n.write(a,t,r),i=new nt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=Nv(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new nt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Lp(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*dg(e.dtype)),this.state.numBytes+=r,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:r})),e instanceof Lp||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 r=e.size*dg(e.dtype);this.state.numBytes-=r}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,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,n,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:a},o=pg(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=r[d],p=K0(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),n(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=yy(e),r=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof nt,()=>"The result y returned by f() must be a tensor.");let s=JR(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[a.id]=r==null?dM(a.shape):r,QR(i,s,l=>this.tidy(l),pM);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return P(Ps(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{P(t.every(i=>i instanceof nt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let r,n={};t.forEach((i,o)=>{n[o]=i});let a=(i,o)=>(r=e(...t,o),P(r.value instanceof nt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),P(Ps(r.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),r.value),s=(i,o)=>{let l=r.gradFunc(i,o),u=Array.isArray(l)?l:[l];P(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),P(u.every(h=>h instanceof nt),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let d={};return u.forEach((h,p)=>{d[p]=()=>h}),d};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Dp(),r=await this.backend.time(e);return r.wallMs=Dp()-t,r}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new Ob;for(let e in 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The output of every math call will be downloaded to CPU and checked for NaNs. 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t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}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)}};jf.className="Adam";Hi(jf);var Hf=class extends as{constructor(e,t,r,n=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=ce(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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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)}};Hf.className="Adamax";Hi(Hf);var Sh=class extends as{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=mr(Se(-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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n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(xt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Er(ce(l,le(xt(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=ce(n,p);n.assign(c)}else{let 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=St(e),n=!0;for(let s of r)if(!(s instanceof pa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof pa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Eo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of St(e))s.push(i.shape);this.build(tn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=St(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=tn(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=eB(e),i=this.computeOutputShape(s),o,l=tB(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new pa(l,u,this,St(e),t,this.name,d)):o=new pa(l,i,this,St(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ja(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ja(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new da(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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(r=>r.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 r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}CA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${LA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=Mg(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=St(e);let r=new To;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return kp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=St(e);let r;return t==null?r=Oo(null,e.length):r=St(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=T0(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Hc);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(tn(d)),p=T0(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ca(o in r),a.push(r[o])}return tn(a)}runInternalGraph(e,t){t==null&&(t=Oo(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Hc);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=St(d.call(x,f)),A=St(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=St(d.call(m,f)),A=St(d.computeMask(m,g));if(d.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],v=y[x],S=A[x];r[b.id]=[v,S]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ca(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof Na?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=Na.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=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 r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=Na.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=Na.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=Na.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Na.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Na.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],S=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let T=a[b];if(T.inboundNodes.length<=v){i(m,g);return}let E=T.inboundNodes[v];y.push(E.outputTensors[S])}y.length>0&&m.apply(tn(y),A)}function l(m){let g=m.name,y=fa(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!CL(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ca(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ca(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}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(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function aU(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===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!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function A6(e,t){return aU(e,t,"classWeight")}async function x6(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Wr(e);if(e.shape.length===2){if(e.shape[1]>1)return Rn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),Tt(i,"float32")}else return null}function sU(e,t){return L(e,t)}var iU=32;function b6(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=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 s=p4("input",e.inputNames,r),i=p4("output",e.outputNames,n),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function p4(e,t,r){if(r instanceof nt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function oU(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 lU(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.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 a=r.validationData!=null,s,i;if(a)if(h4(r.validationData))w.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=oU(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=d6(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=p6(d,h,r.epochs,null,null,uU(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let f=r.initialEpoch==null?0:r.initialEpoch,m=await t.iterator();for(;f<r.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(n||(m=await t.iterator());!n||y<r.batchesPerEpoch;){let x=await m.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.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, ${r.batchesPerEpoch*r.epochs} batches). 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof _g))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=fa(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}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 r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},lm=_g;lm.className="Sequential";ue.registerClass(lm);function kU(e){return new Za(e)}function IU(e){return new lm(e)}function SU(e,t){return t==null&&(t={}),bU(e,t)}function k6(e){return e6(e)}function TU(e,t){PA.registerCallbackConstructor(e,t)}var ln=class extends ue.Serializable{getConfig(){return{}}},I6=class extends ln{apply(e,t=1){return UL(e,t)}};I6.className="elu";ue.registerClass(I6);var S6=class extends ln{apply(e){return aA(e)}};S6.className="selu";ue.registerClass(S6);var T6=class extends ln{apply(e){return Oa(e)}};T6.className="relu";ue.registerClass(T6);var N6=class extends ln{apply(e){return K(()=>kh(6,Oa(e)))}};N6.className="relu6";ue.registerClass(N6);var C6=class extends ln{apply(e){return e}};C6.className="linear";ue.registerClass(C6);var E6=class extends ln{apply(e){return 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t={};return t.className="linear",t.config={},sg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},sg(t)}else return e instanceof ln?e:sg(e)}function VA(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 O6=class extends ue.Serializable{},Mh=class extends O6{constructor(e){super(),VA(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 K(()=>{let t=_t([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,nr(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Ch(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Mh.className="L1L2";ue.registerClass(Mh);function NU(e){return VA(e),new Mh({l1:e!=null?e.l1:null,l2:0})}function CU(e){return VA(e),new Mh({l2:e!=null?e.l2:null,l1:0})}var g4={l1l2:"L1L2"};function bt(e){return AA(e)}function 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st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Rt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Mt(e.alphaRegularizer),this.alphaConstraint=lr(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 ${e.sharedAxes}`)}build(e){e=mt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new Xt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=je(e),Pf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:zt(this.alphaInitializer),alphaRegularizer:bt(this.alphaRegularizer),alphaConstraint:or(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};jA.className="PReLU";ue.registerClass(jA);var HA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return vh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};HA.className="ELU";ue.registerClass(HA);var qA=class extends st{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 r=je(e);return L(r,me(mn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qA.className="ThresholdedReLU";ue.registerClass(qA);var KA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new WA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=je(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Softmax";ue.registerClass(KA);function xu(e,t,r){if(typeof e=="number")return Oo(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!LL(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ma(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Ea(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Gs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function XA(e,t){return K(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function D6(e,t){return K(()=>(jt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function EU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=Aa()),jt(s),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(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Ly(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=va(o,r)),o})}function A4(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=Aa()),jt(s),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=XA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Us.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function RU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=Aa()),jt(s),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 ${e.rank}.`);let o=D6(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Vy(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=va(o,r)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var ZA=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ZA.verifyArgs(t),this.rank=e,gr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${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,Ln(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,jt(this.dataFormat),this.activation=Hs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Rt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=lr(t.biasConstraint),this.biasRegularizer=Mt(t.biasRegularizer),this.activityRegularizer=Mt(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(Ca("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!xA(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:js(this.activation),useBias:this.useBias,biasInitializer:zt(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Fh=class extends ZA{constructor(e,t){super(e,t),this.kernel=null,Fh.verifyArgs(t),this.filters=t.filters,gr(this.filters,"filters"),this.kernelInitializer=Rt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=lr(t.kernelConstraint),this.kernelRegularizer=Mt(t.kernelRegularizer)}build(e){e=mt(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 r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=je(e);let r,n=this.bias==null?null:this.bias.read(),a=Gw(this.activation.getClassName());if(a!=null&&this.rank===2)r=A4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=EU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=A4(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=RU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=mt(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ma(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:zt(this.kernelInitializer),kernelRegularizer:bt(this.kernelRegularizer),kernelConstraint:or(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)}`)}},L6=class extends Fh{constructor(e){super(2,e),L6.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!xA(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)}.`)}},um=L6;um.className="Conv2D";ue.registerClass(um);var B6=class extends Fh{constructor(e){super(3,e),B6.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)}.`)}},dm=B6;dm.className="Conv3D";ue.registerClass(dm);var YA=class extends um{constructor(e){if(super(e),this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Ea(o,h,u,this.padding),f=Ea(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,1]));let g=Wy(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=va(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Ea(t[n],o,s,this.padding),t[a]=Ea(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};YA.className="Conv2DTranspose";ue.registerClass(YA);var JA=class extends dm{constructor(e){if(super(e),this.inputSpec=[new Xt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=mt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Ea(l,f,h,this.padding),A=Ea(u,m,p,this.padding),x=Ea(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,4,1]));let v=K7(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=tt(v,[0,4,1,2,3])),this.bias!==null&&(v=va(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=mt(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Ea(t[n],u,i,this.padding),t[a]=Ea(t[a],d,o,this.padding),t[s]=Ea(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};JA.className="Conv3DTranspose";ue.registerClass(JA);var W6=class extends Fh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new 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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Rt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Mt(t.depthwiseRegularizer),this.depthwiseConstraint=lr(t.depthwiseConstraint),this.pointwiseInitializer=Rt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Mt(t.pointwiseRegularizer),this.pointwiseConstraint=lr(t.pointwiseConstraint)}build(e){if(e=mt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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 r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Xt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=je(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),r=mw(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=tt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.pointwiseInitializer=zt(this.pointwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.pointwiseRegularizer=bt(this.pointwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseConstraint),e.pointwiseConstraint=or(this.pointwiseConstraint),e}};W6.className="SeparableConv";var QA=class extends W6{constructor(e){super(2,e)}};QA.className="SeparableConv2D";ue.registerClass(QA);var V6=class extends Fh{constructor(e){super(1,e),V6.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"&&!xA(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)}.`)}},ex=V6;ex.className="Conv1D";ue.registerClass(ex);var tx=class extends st{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 K(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=Kc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Kc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Kc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Kc(r,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}};tx.className="Cropping2D";ue.registerClass(tx);var rx=class extends st{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,jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,zL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=je(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=tt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};rx.className="UpSampling2D";ue.registerClass(rx);function MU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=Aa()),jt(a);let i=XA(e,a);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 i=bh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var nx=class extends ZA{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Rt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=lr(e.depthwiseConstraint),this.depthwiseRegularizer=Mt(e.depthwiseRegularizer)}build(e){if(e=mt(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 r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=je(e);let r=MU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=va(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ma(t,this.kernelSize[0],this.padding,this.strides[0]),s=ma(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=zt(this.depthwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.depthwiseConstraint=or(this.depthwiseRegularizer),e}};nx.className="DepthwiseConv2D";ue.registerClass(nx);function U6(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function G6(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ya(2,l));if(t=tt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Kt(a,-1)),a=tt(a,u)),n&&(t=_n(t,0),a!=null&&(a=_n(a,0)));let d=[],h,p=r,c=t.shape[0],f=nn(t),m;a!=null&&(m=nn(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=m[y],S=ce(Pn(v),v),T=le(L(x[0],v),L(p[0],S)),E=p.map((R,_)=>le(L(x[1][_],v),L(R,S)));return{output:T,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=ur(d,1)),[h,g,p]})}var j6=class extends st{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 cm({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 Xt({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 ya(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Cg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Cg(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Xt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.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(s=>new Xt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new ja("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==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(n=>_t([r,n])):this.states_=[_t([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>_t([r,n])):this.states_[0]=_t([r,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()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>mr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=U6(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new Xt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof pa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=je(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=G6((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=_t(e.shape);return t=ke(t,[1,2]),t=Nh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Tg(t,[1,r]):t):this.cell.stateSize>1?[Tg(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 r=this.cell.getConfig();return this.getClassName()===j6.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=fa(n,r);return new e(Object.assign(t,{cell:a}))}},ss=j6;ss.className="RNN";ue.registerClass(ss);var $h=class extends st{},pm=class extends $h{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,gr(this.units,"units"),this.activation=Hs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Rt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Cu([1,Gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Cu([1,Gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ma(L(e,s),this.kernel.read()):a=Ma(e,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ma(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};pm.className="SimpleRNNCell";ue.registerClass(pm);var ax=class extends ss{constructor(e){e.cell=new pm(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};ax.className="SimpleRNN";ue.registerClass(ax);var hm=class extends $h{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,gr(this.units,"units"),this.activation=Hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Hs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Rt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Cu([1,Gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Cu([1,Gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=mt(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 K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ma(e,this.kernel.read());this.useBias&&(u=va(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Zt(d,[2*this.units,this.units],d.rank-1),c=Ma(n,h),[f,m,g]=Zt(u,3,u.rank-1),[y,A]=Zt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Ma(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,Dt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:js(this.activation),recurrentActivation:js(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};hm.className="GRUCell";ue.registerClass(hm);var sx=class extends ss{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 K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};sx.className="GRU";ue.registerClass(sx);var Ph=class extends $h{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,gr(this.units,"units"),this.activation=Hs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Hs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Rt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=lr(e.kernelConstraint),this.recurrentConstraint=lr(e.recurrentConstraint),this.biasConstraint=lr(e.biasConstraint),this.dropout=Cu([1,Gs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Cu([1,Gs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=mt(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Zn{apply(i,o){let l=a.apply([s]),u=new Jf().apply([s]),d=a.apply([s*2]);return Zb(Zb(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=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 n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ma(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ma(n,this.recurrentKernel.read())),this.useBias&&(h=va(h,this.bias.read()));let[p,c,f,m]=Zt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:js(this.activation),recurrentActivation:js(this.recurrentActivation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),recurrentInitializer:zt(this.recurrentInitializer),biasInitializer:zt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),recurrentConstraint:or(this.recurrentConstraint),biasConstraint:or(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Ph.className="LSTMCell";ue.registerClass(Ph);var ix=class extends ss{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 Ph(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ix.className="LSTM";ue.registerClass(ix);var cm=class extends $h{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){Cg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{Eo(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(fa(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Eg(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}CA(t)}};cm.className="StackedRNNCells";ue.registerClass(cm);function qs(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):Yw(t(),r),o=()=>Eh(i,t,n);return!a||a<=1?mr(o().clone()):Array(a).fill(void 0).map(o).map(l=>mr(l.clone()))}var H6=class extends ss{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 Xt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}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 K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=_t(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new ja("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[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(()=>_t(a)):this.states_=[_t(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>_t(a)):this.states_[0]=_t(a);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()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>mr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ma(l,n[0],a,s[0],i[0]),h=ma(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};H6.className="ConvRNN2D";var fm=class extends Ph{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,gr(this.filters,"filters"),this.kernelSize=xu(r,2,"kernelSize"),this.kernelSize.forEach(o=>gr(o,"kernelSize")),this.strides=xu(n||1,2,"strides"),this.strides.forEach(o=>gr(o,"strides")),this.padding=a||"valid",Ln(this.padding),this.dataFormat=s||"channelsLast",jt(this.dataFormat),this.dilationRate=xu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>gr(o,"dilationRate"))}build(e){var t;e=mt(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Zn{apply(d,h){let p=l.apply([u]),c=cn([u]),f=l.apply([u*2]);return bA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=qs({ones:()=>Pn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,J)=>!ee||!ee[J]?V:L(ee[J],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=qs({ones:()=>Pn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,S]=Zt(this.kernel.read(),i,A),[T,E,R,_]=this.useBias?Zt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,T,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,S,_,this.padding);let[M,I,z,O]=Zt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Bs(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?va(a,r,this.dataFormat):a}recurrentConv(e,t){return Bs(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};fm.className="ConvLSTM2DCell";ue.registerClass(fm);var ox=class extends H6{constructor(e){let t=new fm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ox.className="ConvLSTM2D";ue.registerClass(ox);var mm=class extends st{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,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Eh(()=>Yw(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};mm.className="Dropout";ue.registerClass(mm);var lx=class extends mm{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};lx.className="SpatialDropout1D";ue.registerClass(lx);var ux=class extends st{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,gr(this.units,"units"),this.activation=Hs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Rt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Rt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=lr(e.kernelConstraint),this.biasConstraint=lr(e.biasConstraint),this.kernelRegularizer=Mt(e.kernelRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.activityRegularizer=Mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=mt(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=mt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=Gw(this.activation.getClassName()),a;return n!=null?a=Ma(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ma(r,this.kernel.read()),this.bias!=null&&(a=va(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:js(this.activation),useBias:this.useBias,kernelInitializer:zt(this.kernelInitializer),biasInitializer:zt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:or(this.kernelConstraint),biasConstraint:or(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ux.className="Dense";ue.registerClass(ux);var dx=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=mt(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 ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],$s(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=tt(r,n)}return VL(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};dx.className="Flatten";ue.registerClass(dx);var px=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.activation=Hs(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.activation.apply(r)})}getConfig(){let e={activation:js(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};px.className="Activation";ue.registerClass(px);var hx=class extends st{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 K(()=>(e=je(e),BL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};hx.className="RepeatVector";ue.registerClass(hx);var cx=class extends st{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 r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=$s(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){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 K(()=>{this.invokeCallHook(e,t);let r=je(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};cx.className="Reshape";ue.registerClass(cx);var fx=class extends st{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ya(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=mt(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return tt(je(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Permute";ue.registerClass(fx);var mx=class extends st{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 r=je(e),n=-1;return k0(Tu(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=-1,a=!0,s=k0(Tu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};mx.className="Masking";ue.registerClass(mx);var gx=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(St(e.inputLength))}this.inputDim=e.inputDim,gr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,gr(this.outputDim,"outputDim"),this.embeddingsInitializer=Rt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Mt(e.embeddingsRegularizer),this.activityRegularizer=Mt(e.activityRegularizer),this.embeddingsConstraint=lr(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 K(()=>this.maskZero?(e=je(e),Tu(e,at(e))):null)}computeOutputShape(e){if(e=mt(e),this.inputLength==null)return[...e,this.outputDim];let t=St(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);r.dtype!=="int32"&&(r=Zf(r,"int32"));let n=Zw(this.embeddings.read(),G(r,[r.size]));return G(n,mt(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:zt(this.embeddingsInitializer),embeddingsRegularizer:bt(this.embeddingsRegularizer),activityRegularizer:bt(this.activityRegularizer),embeddingsConstraint:or(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};gx.className="Embedding";ue.registerClass(gx);var Ol=class extends st{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 r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[mt(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 a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Fs(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Fs(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=Gs(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Nh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat($s(u.slice(1))));p=tt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ya(1,l).concat([0]);r.push(tt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(tt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ya(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Fs(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{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(n=>n==null))return null;t=t.map(n=>n==null?n:Kt(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=ga(r,t[n]);return r})}},yx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};yx.className="Add";ue.registerClass(yx);var Ax=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};Ax.className="Multiply";ue.registerClass(Ax);var xx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};xx.className="Average";ue.registerClass(xx);var bx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=rs(t,e[r]);return t})}};bx.className="Maximum";ue.registerClass(bx);var vx=class extends Ol{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=kh(t,e[r]);return t})}};vx.className="Minimum";ue.registerClass(vx);var wx=class extends Ol{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 n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.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 K(()=>bA(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,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}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 K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Pn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Kt(t[s],-1)):n.push(t[s]);let a=It(n,this.axis);return _y(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};wx.className="Concatenate";ue.registerClass(wx);function yp(e,t){for(;e<0;)e+=t;return e}function FU(e,t,r){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(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(tt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=rt(o,u)}return o.shape.length===1&&(o=Kt(o,1)),o})}var kx=class extends Ol{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[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],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>yp(a,e[s].shape.length)):n=[yp(this.axes,t.shape.length),yp(this.axes,r.shape.length)],this.normalize&&(t=R0(t,n[0]),r=R0(r,n[1])),FU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[yp(this.axes,e.length),yp(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};kx.className="Dot";ue.registerClass(kx);var Ix=class extends st{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 K(()=>{this.invokeCallHook(e,t);let r=je(e);return Eh(()=>le(Yf(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};Ix.className="GaussianNoise";ue.registerClass(Ix);var Sx=class extends st{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 K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?Eh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,Yf(r.shape,1,n))},()=>r,t.training||!1):r})}};Sx.className="GaussianDropout";ue.registerClass(Sx);var Tx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(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 K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Eh(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Fl(md(r),this.rate);o=Zf(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>je(e),t.training||!1)}return e})}};Tx.className="AlphaDropout";ue.registerClass(Tx);function Hp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=L7(e,t,r,n,a,s);else if(e.rank===3)i=B7(e,t,r,n,a,s);else if(e.rank===4)i=W7(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function $U(e,t,r,n,a=.001){return K(()=>{let s=Ff(e,n),i=s.mean,o=s.variance;return[Hp(e,i,o,r,t,a),i,o]})}function PU(e,t,r,n,a=.001){return K(()=>{let s=Ff(e,n),i=s.mean,o=s.variance,l=[];for(let c of ya(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[Hp(e,u,d,p,h,a),i,o]})}function _U(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ya(0,e.rank-1))?$U(e,t,r,n,a):PU(e,t,r,n,a)}var Nx=class extends st{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=Rt(e.betaInitializer||"zeros"),this.gammaInitializer=Rt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Rt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Rt(e.movingVarianceInitializer||"ones"),this.betaConstraint=lr(e.betaConstraint),this.gammaConstraint=lr(e.gammaConstraint),this.betaRegularizer=Mt(e.betaRegularizer),this.gammaRegularizer=Mt(e.gammaRegularizer)}build(e){e=mt(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==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 Xt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=je(e),a=n.shape,s=a.length,i=ya(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Oo(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ya(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return Hp(n,g,y,A,x,this.epsilon)}else return Hp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=_U(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(ce(b,y),x);g.write(ce(b,v))})};return m(this.movingMean,c,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:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),movingMeanInitializer:zt(this.movingMeanInitializer),movingVarianceInitializer:zt(this.movingVarianceInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer),betaConstraint:or(this.betaConstraint),gammaConstraint:or(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="BatchNormalization";ue.registerClass(Nx);var Cx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Rt(e.betaInitializer||"zeros"),this.gammaInitializer=Rt(e.gammaInitializer||"ones"),this.betaRegularizer=Mt(e.betaRegularizer),this.gammaRegularizer=Mt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=mt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Fs(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=je(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=Ff(r,this.axis,!0),o=Oo(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Gn(s,h),i=Gn(i,h),u=Gn(u,p),d=Gn(d,p),Hp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:zt(this.betaInitializer),gammaInitializer:zt(this.gammaInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Cx.className="LayerNormalization";ue.registerClass(Cx);function zU(e,t,r){return K(()=>{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(r==null&&(r=Aa()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Kn(e,n)})}var Ex=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Aa():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[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.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=mt(e);let t,r;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?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(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?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>zU(je(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ex.className="ZeroPadding2D";ue.registerClass(Ex);function gm(e,t,r,n,a,s){return K(()=>{jt(a),Hw(s),Ln(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=XA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Mf(e,t,r,o):i=kf(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function q6(e,t,r,n,a,s){return K(()=>{jt(a),Hw(s),Ln(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=Aa()),s==null&&(s="max"),e=D6(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Yy(e,t,r,o):i=Oy(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var K6=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(gr(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)}`);gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Ln(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=mt(e);let t=ma(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=Nh(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return rt(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends K6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"max")}};Rx.className="MaxPooling1D";ue.registerClass(Rx);var Mx=class extends K6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"avg")}};Mx.className="AveragePooling1D";ue.registerClass(Mx);var X6=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new 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];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Ln(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(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}},Fx=class extends X6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"max")}};Fx.className="MaxPooling2D";ue.registerClass(Fx);var $x=class extends X6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),gm(e,t,r,n,a,"avg")}};$x.className="AveragePooling2D";ue.registerClass($x);var Z6=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new 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];gr(this.poolSize,"poolSize"),gr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),Ln(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=mt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ma(t,this.poolSize[0],this.padding,this.strides[0]),r=ma(r,this.poolSize[1],this.padding,this.strides[1]),n=ma(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(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}},Px=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),q6(e,t,r,n,a,"max")}};Px.className="MaxPooling3D";ue.registerClass(Px);var _x=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return jt(a),Ln(n),q6(e,t,r,n,a,"avg")}};_x.className="AveragePooling3D";ue.registerClass(_x);var Y6=class extends st{constructor(e){super(e),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},zx=class extends Y6{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return Vt(r,1)})}};zx.className="GlobalAveragePooling1D";ue.registerClass(zx);var Ox=class extends Y6{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return yr(r,1)})}};Ox.className="GlobalMaxPooling1D";ue.registerClass(Ox);var J6=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,jt(this.dataFormat),this.inputSpec=[new Xt({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}},Dx=class extends J6{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Vt(r,[1,2]):Vt(r,[2,3])})}};Dx.className="GlobalAveragePooling2D";ue.registerClass(Dx);var Lx=class extends J6{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?yr(r,[1,2]):yr(r,[2,3])})}};Lx.className="GlobalMaxPooling2D";ue.registerClass(Lx);var Q6=class extends st{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,r={}){let n=t.layer,a=fa(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Bx=class extends Q6{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=mt(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=mt(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=je(e),G6((r,n)=>[je(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Bx.className="TimeDistributed";ue.registerClass(Bx);function OU(e){_l(_L,"BidirectionalMergeMode",e)}var DU="concat",Wx=class extends Q6{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=fa(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=fa(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?DU:e.mergeMode,OU(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,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):tn(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=U6(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.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=r,s.push(...r);let u=r.map(d=>new Xt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof pa;for(let l of s)if(l instanceof pa!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=_n(a,1));let i;return this.mergeMode==="concat"?i=bA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Eo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Eo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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implemented`)}},rH=(e,t,r)=>{switch(e.op){case"Equal":return[Mn(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[Tu(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[mn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[Fl(k("a",e,t,r),k("b",e,t,r))];case"Less":return[Hy(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[$l(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[ga(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[Rf(k("a",e,t,r))];case"LogicalOr":return[Zy(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Vr(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},aH=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Iu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[Iu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[aw(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[gd(k("x",e,t,r))];case"LogSoftmax":return[qy(k("x",e,t,r))];case"SparseToDense":return[hA(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},sH=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[yr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Vt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Ws(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[_y(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[k0(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Rn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[R7(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Jy(k("x",e,t,r),i,o)]}case"Cumprod":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[S0(k("x",e,t,r),i,o,l)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[Gy(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[Dy(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[X7(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},iH=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[It(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Su(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[Su(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[_n(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[_n(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[Aw(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=rt(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(rt(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[ur(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return nn(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Gn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Zt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[Sw(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Tw(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[hA(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},oH=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=wp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=wp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[wp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[wp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lH=(e,t,r)=>{switch(e.op){case"FFT":return[zf(k("x",e,t,r))];case"IFFT":return[Up(k("x",e,t,r))];case"RFFT":return[Of(k("x",e,t,r))];case"IRFFT":return[lA(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},uH=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=a0.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=a0.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[a0.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dH=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[Kt(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[rt(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[pw(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Kn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[$f(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[If(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[Z7(k("x",e,t,r),n,a)]}case"BroadcastTo":return[Mp(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[V7(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function S4(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>Bj(s,i,o));case"basic_math":return K(()=>Wj(s,i,o));case"control":return qj(s,i,o);case"convolution":return K(()=>Kj(s,i,o));case"creation":return K(()=>Xj(s,i,o));case"dynamic":return Zj(s,i,o);case"evaluation":return K(()=>Yj(s,i,o));case"image":return K(()=>tH(s,i,o));case"graph":return K(()=>Jj(s,i,o));case"logical":return K(()=>rH(s,i,o));case"matrices":return K(()=>nH(s,i,o));case"normalization":return K(()=>aH(s,i,o));case"reduction":return K(()=>sH(s,i,o));case"slice_join":return K(()=>iH(s,i,o));case"sparse":return K(()=>oH(s,i,o));case"spectral":return K(()=>lH(s,i,o));case"string":return K(()=>uH(s,i,o));case"transformation":return K(()=>dH(s,i,o));case"hash_table":return eH(s,i,o,n);case"custom":let l=dk(s.op);if(l&&l.customExecutor)return l.customExecutor(new Lj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,r);return w.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var T4=class{constructor(e={},t={},r={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=r,this.functionMap=n,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let r=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(r))}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 N4(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>pn(p)[0]),d=[];n!=null&&(d=n.map(p=>pn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((Fk(p)||mH(p)||gH(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function pH(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>pn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var hH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],cH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],fH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Fk(e){return hH.indexOf(e.op)>=0}function mH(e){return cH.indexOf(e.op)>=0}function gH(e){return fH.indexOf(e.op)>=0}var qg=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new qg(e.functions[r],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(r=>e[r].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=N4(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return pH(this.graph,this.weightMap,r)}execute(e,t){e=this.mapInputs(e);let r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=r.map(d=>this.graph.nodes[pn(d)[0]]),a=t.map(d=>pn(d)[0]),s=a.map(d=>this.graph.nodes[d]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return K(()=>{let d=new T4(this.weightMap,l,u,this.functionExecutorMap),h={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=pn(f),y=[];y[g]=e[f],h[m]=y});let p=this.getFrozenTensorIds(h),c={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let g=S4(m,h,d,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=g,this.checkTensorForDisposal(m.name,m,h,d,p,a,c)}}return this.parent==null&&d.dispose(p),t.map(f=>Lr(f,h,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(r=>e[r]).map(r=>r.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,r,n,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(r[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=Aj(o.name,r,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!a.has(u.id)){let d=i[u.id];if(d===1){if(!this.keepTensorForDebug)u.dispose();else{let[h,p]=Ra(t.name,n);this.intermediateTensors[h]?this.intermediateTensors[h][p]=u:(this.intermediateTensors[h]=[],this.intermediateTensors[h][p]=u)}delete i[u.id]}else d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,r=!1,n={},a={}){r||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Y().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new T4(this.weightMap,n,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,r);let i=t.map(u=>Lr(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,r){let n=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(n,this.outputNodes,!0,t,r)}async executeWithControlFlow(e,t,r,n){let a=Object.keys(e),s=a.map(A=>this.graph.nodes[pn(A)[0]]),i=r.map(A=>pn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:h}=N4(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),c={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=pn(A),v=[];v[b]=e[A],c[x]=v});let f={},m=this.getFrozenTensorIds(c),g={};for(;p.length>0;){let A=this.processStack(s,p,t,c,g,m,i,f,l);await Promise.all(A)}d==null&&!n&&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=o.filter(A=>!Fk(A)&&!Lr(A.name,c,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${A}`)}return c}processStack(e,t,r,n,a,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();r.currentContext=d.contexts;let h="";if(d.node.op==="Enter"&&k("isConstant",d.node,n,r)&&([h]=Ra(d.node.name,r)),n[d.node.name]==null){let p=S4(d.node,n,r,this._resourceManager);h||([h]=Ra(d.node.name,r));let c=r.currentContext;w.isPromise(p)?u.push(p.then(f=>(n[h]=f,r.currentContext=c,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l),f))):(n[h]=p,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l))}else this.processChildNodes(d.node,t,r,n,a,l)}return 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t=Tr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Tr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Tr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new qg(v4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=v4.Instance.transformGraph(e.modelInitializer);this.initializer=new qg(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let r=Tr.getSaveHandlers(e);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${e}'`);e=r[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 nt)&&!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,r,n)=>(t[r]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=this.executor.execute(e,t);return r.length>1?r:r[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=await this.executor.executeAsync(e,t);return r.length>1?r:r[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,r)=>(t[r]=[e[r]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function bH(e,t={}){if(e==null)throw 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TextDecoder;else{let{StringDecoder:r}=bv();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof nt)&&!(e instanceof Promise)&&!t)}function TH(e){return e==null||NH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof nt||w.isTypedArray(e)}function NH(e){return e===null||typeof e!="object"&&typeof e!="function"}function CH(e){return IH(e,EH)}function EH(e){return e instanceof nt?{value:e.clone(),recurse:!1}:Mu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Ok=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},Dk=class extends Ok{constructor(){super(Dk.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},Lk=Dk;Lk.INITIAL_CAPACITY=32;function Bk(e){return new FH(e)}function qx(e){return new $H(e)}function RH(e,t){return new Wk(e,t)}function MH(e,t=Vk.FAIL){return new VH(e,t)}var xr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new BH(this,e)}filter(e){return new DH(this,e)}map(e){return new LH(this,e)}mapAsync(e){return new C4(this,e)}serialMapAsync(e){return new C4(this,e).serial()}flatmap(e){return new WH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new OH(this,e,t)}columnMajorBatch(e,t=!0,r=_k){return this.rowMajorBatch(e,t).map(n=>SH(n,r))}concatenate(e,t){return new Wk(Bk([this,e]),t)}take(e){return e<0||e==null?this:new zH(this,e)}skip(e){return e<0||e==null?this:new _H(this,e)}prefetch(e){return new Uk(this,e)}shuffle(e,t){return new UH(this,e,t)}serial(){return new PH(this)}},FH=class extends xr{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let 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this.upstream.next()}},zH=class extends xr{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},OH=class extends xr{constructor(e,t,r=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},DH=class extends xr{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},LH=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},BH=class extends xr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},C4=class extends xr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ha.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ha.getTensorsInContainer(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Kx=class extends xr{constructor(){super(),this.outputQueue=new Lk,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},WH=class extends Kx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ha.getTensorsInContainer(e.value),r=this.transform(e.value),n=ha.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)ha.isTensorInList(a,n)||a.dispose();return!0}},Wk=class extends xr{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Vk=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(Vk||{}),VH=class extends xr{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof xr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await zk(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Uk=class extends xr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new Ok(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},UH=class extends Uk{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=kH.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},bd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),dn(async()=>(await r.iterator()).columnMajorBatch(e,t,HH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,dn(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,dn(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return dn(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return dn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return dn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,dn(async()=>{let n=qx(async()=>({value:await t.iterator(),done:!1}));return RH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,dn(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=wH.alea(t||w.now().toString());return dn(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,dn(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};bd.MAX_BUFFER_SIZE=1e4;function dn(e,t=null){return new class extends bd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function GH(e){return dn(async()=>Bk(e),e.length)}function jH(e){if(!Mu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return dn(async()=>{let r=await zk(e,n=>{if(n instanceof bd)return{value:n.iterator(),recurse:!1};if(Mu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return MH(r,1)},t)}function HH(e){if(e===null)return null;let t=e[0];return TH(t)?{value:qH(e),recurse:!1}:{value:null,recurse:!0}}function qH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof nt?ur(e):ft(e)}var Gk=class extends bd{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Yc='"',xp=Symbol("out"),E4=Symbol("field"),Jc=Symbol("quote"),og=Symbol("quoteafterquote"),R4=Symbol("quoteinquote"),jk=class extends bd{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new Gk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=xp;for(let i=0;i<a;i++)switch(s){case xp:switch(e.charAt(i)){case Yc:n=i+1,s=Jc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=xp;break;default:s=E4,n=i;break}break;case E4:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=xp,n=i+1;break;default:}break;case Jc:switch(e.charAt(i)){case Yc:s=og;break;default:}break;case og:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=xp,n=i+1;break;case Yc:s=Jc;break;default:s=R4;break}break;case R4:switch(e.charAt(i)){case Yc:s=Jc;break;default:}break;default:}if(s===og?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},Hk=class extends xr{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(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new Hk(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(r){throw new Error(`Error thrown while initializing video stream: ${r.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,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ft(r,t)}},qk=class extends xr{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Tt([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=ca([s,a,o,i],[1,4])}else this.cropBox=ca([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new qk(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=On.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Kt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Kk=class{},Xk=class extends xr{split(e){return new KH(this,e)}},KH=class extends Xk{constructor(e,t){super(),this.upstream=e,this.impl=new XH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},XH=class extends Kx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},ZH=class extends xr{decodeUTF8(){return new YH(this)}},YH=class extends Xk{constructor(e){super(),this.upstream=e,this.impl=new JH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},JH=class extends Kx{constructor(e){if(super(),this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=bv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Y().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},Zk=class extends ZH{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},n.onabort=s=>t(new Error("Aborted")),n.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,r);n.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function QH(e,t={},r){let n,a;typeof e=="string"?n=e:(n=e.url,a=eq(e));let s=await(r||w.fetch)(n,a);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new Zk(i,t)}else throw new Error(s.statusText)}var eq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Yk(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var Jk=class extends Kk{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(Yk(this.input)&&Y().get("IS_NODE")){let e=py();this.input=e.readFileSync(this.input.slice(7))}return new Zk(this.input,this.options)}},Qk=class extends Kk{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return Yk(this.url)?new Jk(this.url,this.fileOptions).iterator():QH(this.url,this.fileOptions)}};function tq(e,t={}){return new jk(new Qk(e),t)}function rq(e){let t=qx(e);return dn(async()=>t)}function nq(e){return dn(async()=>{let t=await e();return qx(()=>t.next())})}async function aq(e,t){return qk.create(e,t)}async function sq(e){return Hk.create(e)}var iq="0.0.0";function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var oq=Xn.whereImpl,e8=class extends _u{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Xp(this,ar())}nextDataId(){return e8.nextDataId++}write(e,t,r){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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d=N.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,f=d.filterDepth,m=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,v=d.effectiveFilterHeight,S=d.effectiveFilterWidth,T=b-1-d.padInfo.front,E=S-1-d.padInfo.left,R=v-1-d.padInfo.top,_=We(s.shape,"float32"),M=1/(f*m*g),I=r.bufferSync(a);for(let z=0;z<d.batchSize;++z)for(let O=0;O<d.inChannels;++O)for(let j=0;j<d.inDepth;++j)for(let X=0;X<d.inHeight;++X)for(let D=0;D<d.inWidth;++D){let Q=j-T,V=X-R,ee=D-E,J=0;for(let ie=0;ie<b;ie+=y){let Z=(Q+ie)/h;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let ae=0;ae<v;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let Ae=0;Ae<S;Ae+=x){let be=(ee+Ae)/c;be<0||be>=d.outWidth||Math.floor(be)!==be||(J+=I.get(z,Z,de,be,O))}}}_.set(J*M,z,j,X,D,O)}return r.makeTensorInfo(_.shape,_.dtype,_.values)}var XK={kernelName:Z0,backendName:"cpu",kernelFunc:KK};function 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r.makeTensorInfo(a.shape,a.dtype,m)}var QK={kernelName:fi,backendName:"cpu",kernelFunc:JK};function eX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Ft({inputs:{x:a},backend:r,attrs:{shape:l}}),f=sn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Ft({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Lo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var tX={kernelName:Ho,backendName:"cpu",kernelFunc:eX};function rX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Yx(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var 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h=Cr(u.dtype,"int32"),p=w.makeOnesTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?1:c[x];else{let b=m(y,A-1);p[x]=i?c[b]*p[b]:c[x]*p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=sn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var RX={kernelName:Ko,backendName:"cpu",kernelFunc:EX};function MX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Te(a,"cumsum");let l=N.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=sn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Cr(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=sn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var FX={kernelName:oi,backendName:"cpu",kernelFunc:MX};function $X(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Yx(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=n8(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
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="",u=`
#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",r="varying",n="varying",a="texture2D",s="gl_FragColor",i="",o=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,u=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
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int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}function p5(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var NI=`
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:CI}=N;function EQ(e,t,r){let n=[];if(e.forEach(p=>{let c=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=h5(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
`),s=e.map(p=>RQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
`),i=t.texShape,o=Hr(),l=$Q(o),u,d,h=zQ(o);return t.isPacked?(u=MQ(t.logicalShape,i,r.enableShapeUniforms),d=_Q(o)):(u=FQ(t.logicalShape,i,r.enableShapeUniforms),d=PQ(o)),r.packedInputs&&(h+=BQ),[h,l,d,a,u,s,r.userCode].join(`
`)}function Id(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return JQ(e,t);case 1:return eee(e,t);case 2:return ree(e,t);case 3:return aee(e,t);case 4:return iee(e,t);case 5:return oee(e);case 6:return lee(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function EI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return YQ(e);case 1:return QQ(e,t);case 2:return tee(e,t);case 3:return nee(e,t);default:return see(e,t)}}function RQ(e,t,r=!1,n){let a="";r?a+=EI(e,n):a+=Id(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=uee(e,t):a+=dee(e,t)),a}function MQ(e,t,r){switch(e.length){case 0:return RI();case 1:return WQ(e,t,r);case 2:return XQ(e,t,r);case 3:return UQ(e,t,r);default:return jQ(e,t,r)}}function FQ(e,t,r){switch(e.length){case 0:return RI();case 1:return VQ(e,t,r);case 2:return ZQ(e,t,r);case 3:return GQ(e,t,r);case 4:return HQ(e,t,r);case 5:return qQ(e,t);case 6:return KQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function $Q(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function PQ(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function _Q(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function zQ(e){return`${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);
}
${OQ}
${DQ}
${LQ}
`}var OQ=`
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);
}
`,DQ=`
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);
}
`,LQ=`
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);
}
`,BQ=`
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 RI(){return`
int getOutputCoords() {
return 0;
}
`}function WQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?r?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:r?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function VQ(e,t,r){return t[0]===1?r?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?r?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:r?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function UQ(e,t,r){if(r)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),s=a*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec3(b, r, c);
}
`}function GQ(e,t,r){if(r)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${vm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let n=Dl(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function jQ(e,t,r){if(r)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),s=a*Math.ceil(e[e.length-2]/2),i=s,o="",l="b, r, c";for(let u=2;u<e.length-1;u++)i*=e[e.length-u-1],o=`
int b${u} = index / ${i};
index -= b${u} * ${i};
`+o,l=`b${u}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${o}
int b = index / ${s};
index -= b * ${s};
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec${e.length}(${l});
}
`}function HQ(e,t,r){if(r)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${vm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let n=Dl(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function qQ(e,t){let r=Dl(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function KQ(e,t){let r=Dl(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${r}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function XQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return r?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let a=Math.ceil(e[1]/2);return r?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${a});
int c = imod(index, ${a}) * 2;
return ivec2(r, c);
}
`}function ZQ(e,t,r){return w.arraysEqual(e,t)?r?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?r?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?r?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:r?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function Ll(e){return`offset${e}`}function YQ(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Hr();return`
vec4 ${r}() {
return ${n.texture2D}(${t}, halfCR);
}
`}function JQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
float ${n}() {
return sampleTexture(${r}, halfCR);
}
`;let i=Ll(r);if(t)return`
float ${n}() {
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], ${i});
return sampleTexture(${r}, uv);
}
`;let[o,l]=e.shapeInfo.texShape;return`
float ${n}() {
vec2 uv = uvFromFlat(${o}, ${l}, ${i});
return sampleTexture(${r}, uv);
}
`}function QQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=Hr();if(t)return`
vec4 ${n}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${s.texture2D}(${r}, uv);
}
`;let i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${s.texture2D}(${r}, uv);
}
`}function eee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Sd(e)}
}
`;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(i===1&&s===1)return`
float ${n}(int index) {
return sampleTexture(${r}, halfCR);
}
`;let o=Ll(r);return i===1?t?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${r}TexShape[0]));
return sampleTexture(${r}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${s}.0);
return sampleTexture(${r}, uv);
}
`:s===1?t?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${r}TexShape[1]), 0.5);
return sampleTexture(${r}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
return sampleTexture(${r}, uv);
}
`:t?`
float ${n}(int index) {
vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${o});
return sampleTexture(${r}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
return sampleTexture(${r}, uv);
}
`}function tee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Hr();if(s!=null&&w.arraysEqual(r,s))return t?`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return ${l.texture2D}(${n}, uv);
}
`:`
vec4 ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);
return ${l.texture2D}(${n}, uv);
}
`;if(t)return`
vec4 ${a}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${n}, uv);
}
`;let u=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],d=Math.ceil(r[1]/2);return`
vec4 ${a}(int row, int col) {
vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);
return ${l.texture2D}(${n}, uv);
}
`}function ree(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(r,s)){if(t)return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`;let p=s[0],c=s[1];return`
float ${a}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:i,keptDims:o}=w.squeezeShape(r),l=i;if(l.length<r.length){let p=Td(e,l),c=["row","col"];return`
${Id(p,t)}
float ${a}(int row, int col) {
return ${a}(${Nd(c,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
${Sd(e)}
}
`;let u=s[0],d=s[1],h=Ll(n);return d===1?t?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${r[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
return sampleTexture(${n}, uv);
}
`:u===1?t?`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
float index = dot(vec3(row, col, ${h}), vec3(${r[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n}Shape[1] + col + ${h};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${r[1]} + col + ${h};
vec2 uv = uvFromFlat(${u}, ${d}, index);
return sampleTexture(${n}, uv);
}
`}function nee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=Td(e,p),m=["b","row","col"];return`
${EI(f,t)}
vec4 ${a}(int b, int row, int col) {
return ${a}(${Nd(m,c)});
}
`}let o=Hr();if(t)return`
vec4 ${a}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${o.texture2D}(${n}, uv);
}
`;let l=i[0],u=i[1],d=Math.ceil(r[2]/2),h=d*Math.ceil(r[1]/2);return`
vec4 ${a}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${u}, ${h}, ${d}, b, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function aee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let m=Td(e,u),g=["row","col","depth"];return`
${Id(m,t)}
float ${a}(int row, int col, int depth) {
return ${a}(${Nd(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${s}, ${i}, 1)));
${Sd(e)}
}
`;let d=e.shapeInfo.texShape,h=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===s&&c==null)return t?`
float ${a}(int row, int col, int depth) {
int stride1 = ${n}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${i}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(p===i&&c==null)return t?`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${r[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let f=Ll(n);return t?`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${n}Shape[1] * ${n}Shape[2];
int stride1 = ${n}Shape[2];
int index = row * ${s} + col * ${i} + depth + ${f};
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s} + col * ${i} + depth + ${f};
vec2 uv = uvFromFlat(${h}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function see(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=Hr();if(t)return`
vec4 ${n}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${r}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${r}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}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 ${a.texture2D}(${r}, uv);
}
`;let s=e.shapeInfo.logicalShape,i=s.length,o=e.shapeInfo.texShape,l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=l[0],d=l[1],h=Math.ceil(s[i-1]/2),p=h*Math.ceil(s[i-2]/2),c="int b, int row, int col",f=`b * ${p} + (row / 2) * ${h} + (col / 2)`;for(let m=2;m<i-1;m++)c=`int b${m}, `+c,p*=s[i-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${n}(${c}) {
int index = ${f};
int texR = index / ${d};
int texC = index - texR * ${d};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
return ${a.texture2D}(${r}, uv);
}
`}function iee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=Td(e,l),x=["row","col","depth","depth2"];return`
${Id(A,t)}
float ${a}(int row, int col, int depth, int depth2) {
return ${a}(${Nd(x,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${a}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, 1)));
${Sd(e)}
}
`;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],f=`int stride2 = ${n}Shape[3];`,m=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(c===o&&d==null)return t?`
float ${a}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${i}, ${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(c===s&&d==null)return t?`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}TexShape[1], ${n}TexShape[0]);
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${r[1]*r[2]}, ${r[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let y=Ll(n);return t?`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${y});
return sampleTexture(${n}, uv);
}
`:`
float ${a}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} +
depth * ${s} + depth2;
vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
return sampleTexture(${n}, uv);
}
`}function oee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=Td(e,l),g=["row","col","depth","depth2","depth3"];return`
${Id(m)}
float ${n}(int row, int col, int depth, int depth2, int depth3) {
return ${n}(${Nd(g,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${a})) +
depth3;
${Sd(e)}
}
`;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1];if(c===o&&d==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;if(c===a&&d==null)return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${c}.0, ${p}.0);
return sampleTexture(${r}, uv);
}
`;let f=Ll(r);return`
float ${n}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${o} + col * ${i} + depth * ${s} +
depth2 * ${a} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${c}, index);
return sampleTexture(${r}, uv);
}
`}function lee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=Td(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Id(g)}
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${n}(${Nd(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${d}, ${u}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${Sd(e)}
}
`;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],f=p[1];if(f===d&&h==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${c}.0);
return sampleTexture(${r}, uv);
}
`;if(f===i&&h==null)return`
float ${n}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${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, ${c}.0);
return sampleTexture(${r}, uv);
}
`;let m=Ll(r);return`
float ${n}(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 * ${d} + col * ${u} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
vec2 uv = uvFromFlat(${c}, ${f}, index);
return sampleTexture(${r}, uv);
}
`}function Sd(e){let t=e.name,r=w.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
for (int i = 0; i < ${r}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function uee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=CI(e.shapeInfo.logicalShape,t.logicalShape),l=yt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
`);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((g,y)=>`coords.${h[y+u]}`).join(", ");let c="return outputValue;",f=w.sizeFromShape(e.shapeInfo.logicalShape)===1,m=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)c=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(f&&!m)i===1?c=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:c=`
return vec4(outputValue.x);
`;else if(o.length){let g=s-2,y=s-1;o.indexOf(g)>-1&&o.indexOf(y)>-1?c="return vec4(outputValue.x);":o.indexOf(g)>-1?c="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(c="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${a}() {
${l} coords = getOutputCoords();
${d}
vec4 outputValue = get${n}(${p});
${c}
}
`}function dee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${r}, resultUV);
}
`;let u=yt(l),d=CI(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(m=>`coords.${c[m+h]} = 0;`).join(`
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${c[g+h]}`).join(", "),`
float ${a}() {
${u} coords = getOutputCoords();
${p}
return get${n}(${f});
}
`}function yt(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 ${e} is not yet supported`)}function h5(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function Td(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Nd(e,t){return t.map(r=>e[r]).join(", ")}function pee(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=EQ(a,i,t),l=iI(e.gl,o),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...MI(e,t,u)}}function MI(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let f=t.variableNames[c];n[f]=e.getUniformLocation(r,f,p),n[`offset${f}`]=e.getUniformLocation(r,`offset${f}`,p),t.enableShapeUniforms&&(a[`${f}Shape`]=e.getUniformLocation(r,`${f}Shape`,p),s[`${f}TexShape`]=e.getUniformLocation(r,`${f}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,f)=>{i[f]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function $4(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((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function hee(e,t,r,n,a){t.program.enableShapeUniforms||($4(t.inShapeInfos,r),$4([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=h5(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.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,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function cee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=h5(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Y().getNumber("WEBGL_VERSION")}`,s}function un(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var fee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Hr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?vm(["r","c","d"],e):Dl(["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;
}
`}},mee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Hr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?vm(["r","c","d"],e):Dl(["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;
}
`}},gee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Hr();this.outputShape=e,this.userCode=`
${NI}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},yee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Hr();this.outputShape=e,this.userCode=`
${NI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},Aee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Hr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?p5():d5(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 = ${r.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];
}
${r.output} = vec4(${n}, 0., 0., 0.);
}
`}},xee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Hr();this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
localCoords = coords;
if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${i};
if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${s};
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 = ${r.texture2D}(A, uv);
if (offset == 0) {
result[${o}] = values[0];
} else if (offset == 1) {
result[${o}] = values[1];
} else if (offset == 2) {
result[${o}] = values[2];
} else {
result[${o}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?p5():d5(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${n}
${r.output} = ${a};
}
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${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return sI(e,r)}function PI(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return uI(e,t)}function _I(e){let t=new Uint16Array([0,1,2,2,1,3]);return dI(e,t)}function Dh(e,t,r,n,a,s){hI(t,r);let i=pI(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Y().getNumber("WEBGL_VERSION")===1?we(e,()=>e.texImage2D(o,0,n,t,r,0,a,s,null)):we(e,()=>e.texStorage2D(o,1,n,t,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[r,t]}}function c5(e){return e.internalFormatFloat}function zI(e,t,r,n){let[a,s]=Oh(t,r);return Dh(e,a,s,c5(n),n.textureFormatFloat,e.FLOAT)}function f5(e){return e.internalFormatHalfFloat}function OI(e,t,r,n){let[a,s]=Oh(t,r);return 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t-1}var{addImpl:vee,bincountImpl:XI,bincountReduceImpl:wee,ceilImpl:kee,concatImpl:Iee,equalImpl:See,expImpl:Tee,expm1Impl:Nee,floorImpl:Cee,gatherNdImpl:Eee,gatherV2Impl:Ree,greaterImpl:Mee,greaterEqualImpl:Fee,lessImpl:$ee,lessEqualImpl:Pee,linSpaceImpl:_ee,logImpl:zee,maxImpl:Oee,maximumImpl:Dee,minimumImpl:Lee,multiplyImpl:Bee,negImpl:Wee,notEqualImpl:Vee,prodImpl:Uee,rangeImpl:Gee,rsqrtImpl:jee,sigmoidImpl:Hee,simpleAbsImpl:ZI,sliceImpl:qee,sparseFillEmptyRowsImpl:Kee,sparseReshapeImpl:Xee,sparseSegmentReductionImpl:YI,sqrtImpl:Zee,stridedSliceImpl:Yee,stringNGramsImpl:Jee,stringSplitImpl:Qee,stringToHashBucketFastImpl:ete,subImpl:tte,tileImpl:rte,topKImpl:nte,transposeImpl:A5,uniqueImpl:ate}=Am;function JI(e,t){return["x","y","z","w","u","v"].slice(0,t).map(r=>`${e}.${r}`)}function Br(e,t){return t===1?[e]:JI(e,t)}function ste(e,t){if(e===1)return"rc";let r="";for(let n=0;n<e;n++)r+=t[n],n<e-1&&(r+=",");return r}var ite=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=un(this.outputShape.length),this.rank===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let t=Br("rc",this.rank),r=yt(this.rank),n=this.getOutOfBoundsCondition(t),a=this.getSetup(t),s=this.getOutput(t);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${n}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${s}));
}
}
`}}getSourceCoordsArr(e){let t=[];for(let r=0;r<=1;r++)for(let n=0;n<=1;n++){let a=`${r===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let s=2;s<this.rank;s++)a=`${e[e.length-1-s]},`+a;t.push(a)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let r=this.rank-2;r<this.rank;r++)t+=`${e[r]} >= ${this.enableShapeUniforms?`outShape[${r}]`:this.outputShape[r]}`,r<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),r=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],n=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
int r = ${t[0]};
int c = ${t[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${r};
bool rEdge = rp1 >= ${n};
`}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
cEdge ? 0. : getA(${t[1]}),
rEdge ? 0. : getA(${t[2]}),
rEdge || cEdge ? 0. : getA(${t[3]})`}},QI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
${a}
${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${n}] =
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${n>0?"}":""}
`}this.userCode=`
${ote(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?p5():d5(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]};
${r}
setOutput(result);
}
`}};function ote(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?CQ(["r","c","d"],"inputShape"):Dl(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var lte=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=_4(t,r),a=z4(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=P4(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=_4(r,n),s=z4(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=P4(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function ute(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function P4(e,t,r,n,a){let s=dte(t,n),i;if(a){let[l,u]=wd(e[0],e[1]);i=l*u}else{let[l,u]=Oh(e[0],e[1]);i=l*u}let o=ute(r,s);return i*o}function dte(e,t){switch(e){case 3:return g5(t);case 4:return y5(t);case 1:return c5(t);case 0:return f5(t);case 2:return m5(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function pte(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function _4(e,t){if(e===1)return 3;if(e===0||e==null)return pte(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function z4(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Xa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},Yn="if (isnan(x)) return x;",hte="return x;",O4="return abs(x);",cte="return (x >= 0.0) ? x : (exp(x) - 1.0);",fte=Yn+`
return (x < 0.0) ? 0.0 : x;
`,mte=Yn+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,uu="return x;",gte="return 1.0 / (1.0 + exp(-1.0 * x));",yte="return x;",Ate=`
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;
`,xte=`
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;
`,bte=`
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;
`,vte="return 1.0 / (1.0 + exp(-1.0 * x));",No=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},wte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=un(this.outputShape.length);let t=e.length,r=Br("rc",t),n=yt(t),a=ste(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 packedInput = getA(${a});
setOutput(getChannel(packedInput, ${i}));
}
`}},kte=Xn.whereImpl,Ite=1e-7,Ste=1e-4,ug={};function Tte(e){return e in ug||(ug[e]={}),ug[e]}var Nte=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Cte=600;function Ete(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*Cte/1024/1024}var e9=class extends _u{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof bu)t=e;else{let r=xa(Y().getNumber("WEBGL_VERSION"),e);t=new bu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=xa(Y().getNumber("WEBGL_VERSION"));t=new bu(r),this.binaryCache=Tte(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new lte(this.gpgpu),this.numMBBeforeWarning=Ete(),this.texData=new Xp(this,ar())}nextDataId(){return e9.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new No(i,uu):h=new Xa(i,uu);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new No(n,uu):c=new Xa(n,uu);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Qc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ar().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new No(a,uu):p=new Xa(a,uu);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=ar().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!nI(r))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Qc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Y().getBool("WEBGL_PACK")&&n===!0,i=s?l0(t):t,o=s?new yee(i):new gee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().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:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Nte){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return kte(e.shape,t)}packedUnaryOp(e,t,r){let n=new No(e.shape,t),a=this.compileAndRun(n,[e],r);return ar().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=ZI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,O4,e.dtype);let t=new Xa(e.shape,O4),r=this.compileAndRun(t,[e]);return ar().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return ar().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new wte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ite(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Bo(e.shape),...Wo(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Bo(t),...Wo(t)],s=new QI(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=l0(a),o;n?o=new mee(i):o=new fee(i);let l=!0,u=[t!=null?t:Qc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Qc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!qp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=cee(e,u,d),p=this.getAndSaveBinary(h,()=>pee(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||hee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Ite:Ste}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=bI(r,o),t.texShape=d),a!=null){let h=l0(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=wd(d[0],d[1])),o?p=new xee(h,m):p=new Aee(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),S=this.texData.get(v.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,Y().get("ENGINE_COMPILE_ONLY")?this.disposeData(v.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(v.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=Rte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await gA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(u5(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=MI(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Lh=e9;Lh.nextDataId=0;function Rte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var Mte="0.0.0";function t9(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}gh.isBrowser()&&Ml("webgl",()=>new Lh,2);var Fte={forceHalfFloat:t9},r9=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Pu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=un(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;
`,Bh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=un(a);let s="";if(n)if(a===0||w.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${yt(a)} coords = getOutputCoords();
`,a===1)this.enableShapeUniforms?s+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Br("coords",a);this.enableShapeUniforms?s+=`
bool nextRowOutOfBounds =
(${i[a-2]} + 1) >= outShape[${a} - 2];
bool nextColOutOfBounds =
(${i[a-1]} + 1) >= outShape[${a} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:s+=`
bool nextRowOutOfBounds =
(${i[a-2]} + 1) >= ${this.outputShape[a-2]};
bool nextColOutOfBounds =
(${i[a-1]} + 1) >= ${this.outputShape[a-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${s}
setOutput(result);
}
`}};function on(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $te={kernelName:gi,backendName:"webgl",kernelFunc:on};function Ki(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=on({inputs:{x:n},backend:r}),l=on({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Pte={kernelName:Yp,backendName:"webgl",kernelFunc:Ki},n9="return (a < 0.) ? b * a : a;",a9=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function _te(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bh(a9,a.shape,i.shape):new Pu(n9,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var zte={kernelName:yi,backendName:"webgl",kernelFunc:_te},s9="return (a < 0.) ? b * a : a;",i9=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Ote(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bh(i9,n.shape,a.shape):new Pu(s9,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var Dte={kernelName:Ei,backendName:"webgl",kernelFunc:Ote},Cd="if (isnan(x)) return x;",Lte=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Bte=`
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:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new No(i.shape,t):d=new Xa(i.shape,e),o.runWebGLProgram(d,[i],l)}}function wr({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new Pu(e,l.shape,u.shape);return d.runWebGLProgram(E,[S,T],Cr(b.dtype,v.dtype))}),A=Ki({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Cr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Bh(t,l.shape,u.shape,r):c=new Pu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function km(e,t=!1){if(e==="linear")return t?yte:hte;if(e==="relu")return t?xte:fte;if(e==="elu")return t?Ate:cte;if(e==="relu6")return t?bte:mte;if(e==="prelu")return t?i9:s9;if(e==="leakyrelu")return t?a9:n9;if(e==="sigmoid")return t?vte:gte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var o9=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=un(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${i}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${i}
}`:m=`vec4 activation(vec4 x) {
${i}
}`,g="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&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 = ${d}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${d}; i++) {
int batchA = ${A};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${h});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${c[0]} * ${f[0]});
result += (${c[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;"},L4=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},B4="return a * b;";function x5(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new L4(D4.REAL,n.shape,a.shape),d=new L4(D4.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Ki({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=Bee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Bh(B4,n.shape,a.shape):i=new Pu(B4,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Wte={kernelName:Ti,backendName:"webgl",kernelFunc:x5};function Vte(e,t,r){let n=[Bo(e.shape),...Wo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Bo(t),...Wo(t)],i=new QI(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!qp(a.shape,l)&&!(d.texture!==null&&qp(d.shape,l))?Vte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var Ute={kernelName:fl,backendName:"webgl",kernelFunc:ve},W4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
if (inIdx < 0 || inIdx >= ${a}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${u}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},Gte=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 u=Math.floor(r/4)*4,d=r%4,h=`
if (${t==="sum"}) {
sumValue += dot(values, ones);
} else if (${t==="prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${o}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${o}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(i="1.0",h=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(i="0.0",h=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let c="";a%r>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${i};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${h}
} else if (${d===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${h}
} else if (${d===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${h}
}
setOutput(${l});
}
`}};function jte(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Bl(e,t,r,n){let a=jte(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new W4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new W4({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Gte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Hte=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=yt(this.rank),a=qte(t);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function qte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var Kte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=yt(this.rank),a=JI("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${n} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${a[this.rank-1]};
if(++${a[this.rank-2]} < ${r[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Im(e,t,r){let n=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kte(e.shape,t):new Hte(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function Xte(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=Im(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=w.sizeFromShape(p),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=mh(e.dtype),A=Bl(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function Sm(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Xte(a,s,i,r)}var Zte={kernelName:Di,backendName:"webgl",kernelFunc:Sm};function vr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=A5(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=Im(a,s,i);return u}var Yte={kernelName:Ui,backendName:"webgl",kernelFunc:vr},l9=1e3;function W0({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],S=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,T],R=Math.max(y,A),_=r?S.shape[1]:S.shape[2],M=s!=null,I=i!=null,z=l==="leakyrelu",O=l!=null?km(l,!0):null,j=M||I||z||O!=null,X;if((c===1||f===1)&&_>l9&&j===!1){let Q=S,V=T;r&&(Q=vr({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=vr({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,J=f===1,ie=Q;ee&&(ie=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(ie));let Z=f===1?2:1,ae=V;J&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=x5({inputs:{a:ie,b:ae},backend:a});X=Sm({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Cr(e.dtype,t.dtype),V=new o9(b,v,[R,c,f],r,n,M,O,I,z),ee=[S,T];if(s!=null&&ee.push(s),I&&ee.push(i),z){let J=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(J),E.push(J)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function Jte(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return W0({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Qte={kernelName:_s,backendName:"webgl",kernelFunc:Jte},V4="return abs(x);";function ere(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=ZI(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new No(n.shape,V4):a=new Xa(n.shape,V4),r.runWebGLProgram(a,[n],n.dtype)}var tre={kernelName:jo,backendName:"webgl",kernelFunc:ere},rre=Yn+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,nre=it({opSnippet:rre}),are={kernelName:Ou,backendName:"webgl",kernelFunc:nre},sre=Yn+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,ire=it({opSnippet:sre}),ore={kernelName:Du,backendName:"webgl",kernelFunc:ire},U4="return a + b;",lre=wr({opSnippet:U4,packedOpSnippet:U4,supportsComplex:!0,cpuKernelImpl:vee}),ure={kernelName:Qa,backendName:"webgl",kernelFunc:lre},dre=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
float result = ${n};
setOutput(result);
}
`}},pre=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${r.join(`
`)}
vec4 result = ${n};
setOutput(result);
}
`}};function p0(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return on({inputs:{x:n[0]},backend:r});if(n.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=p0({inputs:n.slice(0,o),backend:r}),u=p0({inputs:n.slice(o),backend:r});return p0({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Cr(o,l)),s=n.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new pre(n[0].shape,s):new dre(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var hre={kernelName:Ys,backendName:"webgl",kernelFunc:p0};function cre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Bl(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var fre={kernelName:Lu,backendName:"webgl",kernelFunc:cre};function mre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Bl(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var gre={kernelName:Bu,backendName:"webgl",kernelFunc:mre},yre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${n}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},Are=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=yt(o),u=Br("coords",o),d,h;if(s===1){h=o+1;let T=yt(h);d=`
${T} sourceLocR = ${T}(${u.join()}, 0);
++${u[o-1]};
${T} sourceLocG = ${T}(${u.join()}, 0);
++${u[o-2]};
${T} sourceLocA = ${T}(${u.join()}, 0);
--${u[o-1]};
${T} sourceLocB = ${T}(${u.join()}, 0);
--${u[o-2]};`}else h=o,d=`
${l} sourceLocR = coords;
++${u[o-1]};
${l} sourceLocG = coords;
++${u[o-2]};
${l} sourceLocA = coords;
--${u[o-1]};
${l} sourceLocB = coords;
--${u[o-2]};`;let p=["x","y","z","w","u","v"].slice(0,h),c="."+p[h-1],f=p.map(T=>"int "+T),m=Br("sourceLocR",h-1).concat("inIdx.r"),g=Br("sourceLocG",h-1).concat("inIdx.g"),y=Br("sourceLocB",h-1).concat("inIdx.b"),A=Br("sourceLocA",h-1).concat("inIdx.a"),x=r==="max"?"greaterThan":"lessThan",b=n?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${A.join()})));`,v=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,S=n?"":`
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()}));
}
${S}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
${d}
ivec4 srcIdx = ivec4(sourceLocR${c}, sourceLocG${c},
sourceLocB${c}, sourceLocA${c}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${v};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${v};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${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 u9(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new yre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=u9(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function d9(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new Are(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=d9(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function p9(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=u9(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return d9(e,t,n)}function xre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=p9(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var bre={kernelName:Js,backendName:"webgl",kernelFunc:xre};function vre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=p9(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var wre={kernelName:Wu,backendName:"webgl",kernelFunc:vre},kre=Yn+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,Ire=it({opSnippet:kre}),Sre={kernelName:Vu,backendName:"webgl",kernelFunc:Ire},Tre=Yn+"return log(x + sqrt(x * x + 1.0));",Nre=it({opSnippet:Tre}),Cre={kernelName:Uu,backendName:"webgl",kernelFunc:Nre},Ere=Yn+`
return atan(x);
`,Rre=it({opSnippet:Ere}),Mre={kernelName:Gu,backendName:"webgl",kernelFunc:Rre},Fre=Lte+`
return atan(a, b);
`,$re=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Bte+`
return result;
`,Pre=wr({opSnippet:Fre,packedOpSnippet:$re}),_re={kernelName:Hu,backendName:"webgl",kernelFunc:Pre},zre=Yn+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Ore=it({opSnippet:zre}),Dre={kernelName:ju,backendName:"webgl",kernelFunc:Ore},Kp=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let T=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${c});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${d};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${h};
wC += ${u}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${T} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${n?a?m:g:`wR * ${h} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(s/4)*4,v=s%4,S=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${A}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${p}, ${c});
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 < ${d};
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 * ${u};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
getValue(batch, xR, xC + 3 * ${u}, d)
);
${S}
}
int xC = xCCorner + ${b};
if (${v===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${S}
} else if (${v===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${S}
} else if (${v===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${S}
}
}
setOutput(${x});
}
`}},b5=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=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"),r){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${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 += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${c};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${h}) {
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 = ${n?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${c} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let S=Math.floor(s/4)*4,T=s%4,E=`
if (${A}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${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 += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${c};
wR += ${d}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${S}; wC += 4) {
int xC = xCCorner + wC * ${h};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
);
${E}
}
int xC = xCCorner + ${S};
if (${T===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${E}
} else if (${T===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${E}
} else if (${T===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
initializationValue
);
${E}
}
}
setOutput(${v});
}
}
`}};function Lre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;kd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return on({inputs:{x:a},backend:r});let h=new Kp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var Bre={kernelName:Qs,backendName:"webgl",kernelFunc:Lre};function Wre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new b5(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var Vre={kernelName:Zp,backendName:"webgl",kernelFunc:Wre},Ure=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
const ivec2 pads = ivec2(${u}, ${d});
const float avgMultiplier = float(${h});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${o};
wR += ${s}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${l};
wC+= ${i}) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},Gre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
const ivec3 pads = ivec3(${c}, ${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 < ${d};
wD += ${o}) {
float dyD = float(dyDCorner + wD) / ${a}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${h};
wR += ${l}) {
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 < ${p};
wC += ${u}) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`}};function jre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Gre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Hre={kernelName:Z0,backendName:"webgl",kernelFunc:jre};function qre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;kd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new Ure(d);return r.runWebGLProgram(h,[a],i.dtype)}var Kre={kernelName:X0,backendName:"webgl",kernelFunc:qre};function Xre(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return W0({a,b:s,transposeA:i,transposeB:o,backend:r})}var Zre={kernelName:ei,backendName:"webgl",kernelFunc:Xre},Yre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${i};
float scale = ${o};
float inv = scale * inversesqrt(variance + float(${s}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},Jre=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${i};
vec4 scale = ${o};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
setOutput((x - mean) * inv + offset);
}
`}},Qre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Jre(n.shape,a.shape,s.shape,d,h,l):new Yre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},ene={kernelName:fi,backendName:"webgl",kernelFunc:Qre},tne=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=rne(this.rank),n,a=e.map((s,i)=>`sourceLoc.${ey[i]} = start[${i}] + coords.${ey[i]};`);n=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${a.join(`
`)}
`,this.userCode=`
void main() {
${n}
setOutput(getSource(${r}));
}
`}},ey=["x","y","z","w","u","v"];function rne(e){if(e===1)return"sourceLoc";if(e<=6)return ey.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var nne=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),r=Br("coords",this.rank),n=Br("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
result.x = ${s};
if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.y = ${s};
--${n[this.rank-1]};
}
`,o=this.rank===1?"":`
--${r[this.rank-1]};
if (++${r[this.rank-2]} < ${e[this.rank-2]}) {
++${n[this.rank-2]};
result.z = ${s};
if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
++${n[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,d)=>`start[${d}]`).join()});`:e.map((u,d)=>`${n[d]} = ${r[d]} + start[${d}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}};function ane(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=Ot.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Ed(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Ot.parseSliceParams(a,s,i);if(Ot.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=qee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=Ot.isSliceContinous(a.shape,o,l);if(u||!d){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nne(l):new tne(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),ane(a,o,l,r)}var sne={kernelName:xl,backendName:"webgl",kernelFunc:Ed},ine=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=vr({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Ed({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},one={kernelName:Ho,backendName:"webgl",kernelFunc:ine};function lne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=XI(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var une={kernelName:Y0,backendName:"webgl",kernelFunc:lne};function dne(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var pne={kernelName:J0,backendName:"webgl",kernelFunc:dne},hne="return float(a != b);",h9=wr({opSnippet:hne,cpuKernelImpl:Vee,dtype:"bool"}),cne={kernelName:ll,backendName:"webgl",kernelFunc:h9};function Wh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return on({inputs:{x:a.complexTensorInfos.real},backend:r})}var fne={kernelName:ih,backendName:"webgl",kernelFunc:Wh},mne="return float(int(x));";function gne(e,t){let r=new Xa(e.shape,mne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function ty(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return on({inputs:{x:a},backend:r});let i=_t(a.shape),o=ty({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Ki({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Wh({inputs:{input:a},backend:r}),o=ty({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=on({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return gne(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=h9({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var yne={kernelName:ti,backendName:"webgl",kernelFunc:ty},G4="return ceil(x);",Ane=it({opSnippet:G4,packedOpSnippet:G4,cpuKernelImpl:kee}),xne={kernelName:ri,backendName:"webgl",kernelFunc:Ane},bne=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));
}
`}},vne=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 wne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Y().getBool("WEBGL_PACK_CLIP")?o=new vne(a.shape):o=new bne(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var kne={kernelName:es,backendName:"webgl",kernelFunc:wne},Ine=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 j4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Sne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new Ine(n.shape),i=[j4(n,a.complexTensorInfos.real),j4(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Tne={kernelName:Jp,backendName:"webgl",kernelFunc:Sne},Nne=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${r.join(`
`)}
}
`}},Cne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=yt(n),s=Br("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${d}), vec2(${u.join()}));
}`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
return getChannel(
getT${f}(${t0(i,l,m)}),
vec2(${t0(u,l,m)}));
}`}let p=o.length,c=o[o.length-1];h+=`
return getChannel(
getT${p}(${t0(i,l,c)}),
vec2(${t0(u,l,c)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${h}
}
void main() {
${a} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[n-1]} = ${s[n-1]} + 1;
if (${s[n-1]} < ${r[n-1]}) {
result.g = getValue(${s});
}
${s[n-2]} = ${s[n-2]} + 1;
if (${s[n-2]} < ${r[n-2]}) {
result.a = getValue(${s});
}
${s[n-1]} = ${s[n-1]} - 1;
if (${s[n-2]} < ${r[n-2]} &&
${s[n-1]} < ${r[n-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function t0(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function Tm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return on({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Ene={kernelName:rh,backendName:"webgl",kernelFunc:Tm};function mu(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Wh({inputs:{input:m},backend:r})),h=e.map(m=>Tm({inputs:{input:m},backend:r})),p=mu(d,t,r),c=mu(h,t,r),f=Ki({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=Iee(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=mu(e.slice(0,d),t,r),p=mu(e.slice(d),t,r),c=mu([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new Cne(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=Rne(e,t,r),o=new Nne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function Rne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function c9(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return on({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),mu(o,s,r)}var Mne={kernelName:qo,backendName:"webgl",kernelFunc:c9},f9=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=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="";r&&(n?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:a?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${r}
}`:x=`
float activation(float x) {
${r}
}
`,b="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${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 < ${h}; wR++) {
int xR = xRCorner + wR * ${u};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${c}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${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, ${c}) *
getW(wR, wC, ${c}, d2);
} else {
dotProd +=
getX(batch, ${c}, xR, xC) *
getW(wR, wC, ${c}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${c}, d2),
getW(wR, wC, ${c} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${c}),
getX(batch, xR, xC, ${c} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${c}, xR, xC),
getX(batch, ${c} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${c}, d2),
getW(wR, wC, ${c} + 1, d2),
getW(wR, wC, ${c} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${c}),
getX(batch, xR, xC, ${c} + 1),
getX(batch, xR, xC, ${c} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${c}, xR, xC),
getX(batch, ${c} + 1, xR, xC),
getX(batch, ${c} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${v}
${b}
setOutput(result);
}
`}},Fne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${a}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${r}, ${n});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${d}; wF++) {
int xF = xFCorner + wF * ${o};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h}; wR++) {
int xR = xRCorner + wR * ${l};
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 < ${c}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${c}) *
getW(wF, wR, wC, ${c}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${c}),
getX(batch, xF, xR, xC, ${c} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${c}, d2),
getW(wF, wR, wC, ${c} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${c}),
getX(batch, xF, xR, xC, ${c} + 1),
getX(batch, xF, xR, xC, ${c} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${c}, d2),
getW(wF, wR, wC, ${c} + 1, d2),
getW(wF, wR, wC, ${c} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},$ne=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=un(this.outputShape.length);let{dataFormat:r}=t,n=Hr(),a=r==="channelsLast",s=a?0:1,i=a?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
blockIndex = rc.y + ${d};
pos = rc.x + ${u};
${o}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${s}] && 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[${i}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${a}) {
innerDims = vec2(d1, ch);
result[${u*2+d}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${u*2+d}] = 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}
${n.output} = result;
}
`}};function m9({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>l9)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(qp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let S=W0({a:x,b:v,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(S.dataId);w.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=r.outShape,g=on({inputs:{x:S},backend:n}),g.shape=r.outShape,y.push(S)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=W0({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function g9({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),S=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(S);let T=new $ne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(T,[v],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,z=o==="leakyrelu",O=o?km(o,!0):null,j=new o9(_.shape,S.shape,[1,g,r.outChannels],A,x,M,O,I,z),X=[_,S];if(a&&X.push(a),I&&X.push(s),z){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function Pne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;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"))c=m9({x:a,filter:s,convInfo:p,backend:r});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=g9({x:a,filter:s,convInfo:p,backend:r});else{let m=new f9(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var _ne={kernelName:ni,backendName:"webgl",kernelFunc:Pne},zne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${s}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},One=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${d}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},Dne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${a};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${r} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},Lne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${o}, ${l}, ${u});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${a}.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 < ${r}; 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 = ${r} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${i}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function Bne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new zne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Wne={kernelName:Q0,backendName:"webgl",kernelFunc:Bne};function Vne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new One(p);return r.runWebGLProgram(c,[a,s],"float32")}var Une={kernelName:ai,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new Fne(u);return r.runWebGLProgram(d,[a,s],"float32")}var jne={kernelName:Qp,backendName:"webgl",kernelFunc:Gne};function Hne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new Dne(u);return r.runWebGLProgram(d,[a,s],"float32")}var qne={kernelName:ef,backendName:"webgl",kernelFunc:Hne};function Kne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new Lne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Xne={kernelName:tf,backendName:"webgl",kernelFunc:Kne},Zne=Cd+`
return cos(x);
`,Yne=it({opSnippet:Zne}),Jne={kernelName:si,backendName:"webgl",kernelFunc:Yne},Qne=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,eae=it({opSnippet:Qne}),tae={kernelName:ii,backendName:"webgl",kernelFunc:eae},rae=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-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 >= ${s}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${c} ) {
setOutput(float(${a}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${a}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},nae=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new rae(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},aae={kernelName:Xo,backendName:"webgl",kernelFunc:nae},H4=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"1.0":`getX(${q4(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${yt(n)} coords = getOutputCoords();
int end = ${K4(n,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${K4(n,"coords")} = idx;
val *= getX(${q4(n,"coords")});
}
setOutput(val);
}
`}};function q4(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 product for rank ${e} is not yet supported`)}function K4(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function sae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=vr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=on({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new H4(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new H4(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=vr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var iae={kernelName:Ko,backendName:"webgl",kernelFunc:sae},X4=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${Z4(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${yt(n)} coords = getOutputCoords();
int end = ${Y4(n,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${Y4(n,"coords")} = idx;
val += getX(${Z4(n,"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 ${e} is not yet supported`)}function oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=vr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=on({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new X4(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new X4(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=vr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var lae={kernelName:oi,backendName:"webgl",kernelFunc:oae};function uae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=XI(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=wee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var dae={kernelName:rf,backendName:"webgl",kernelFunc:uae},pae=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,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 hae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new pae(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var cae={kernelName:Zo,backendName:"webgl",kernelFunc:hae},y9=class{constructor(e,t=!1,r=null,n=!1,a=!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=un(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${r}
}`:a?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${r}
}`:l=`
float activation(float x) {
${r}
}
`,u="result = activation(result);");let d=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&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 / ${o};
int q = d2 - d1 * ${o};
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 < ${s}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${i}; 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;
${d}
${u}
setOutput(result);
}
`}},A9=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=un(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;p+=`
for (int r = 0; r < ${u}; r++) {
`;for(let g=0;g<d;g++)p+=`
xTexelC${g*2} = vec4(0.0);
xTexelC${g*2}Ready = 0;
xTexelC${g*2+1} = vec4(0.0);
xTexelC${g*2+1}Ready = 0;
xC${g} = vec4(0.0);`;p+=`
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let g=0;g<(h+1)/2;g++){let y=g*2;if(p+=`
xC = xCCorner + ${y*l};
`,o===1){if(y<d&&(i%2===1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
`,l===1&&y>0?p+=`
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.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${y} = vec4(previous.zw, xTexelC${y}.xy);
} else {
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xC${y} = xTexelC${y};
`,y+1<d)){let A=i%2===0?w.nearestLargerEven(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${A};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
`,l>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
xTexelC${y}Ready = 1;
}
`),p+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
`):A===1?p+=`
xC${y+1} = xTexelC${y};
`:p+=`
xCOffset = xC + ${A};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y+1} = xTexelC${y+1};
`}}else y<d&&(i%2===1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.0);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`,y+1<d&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
xTexelC${y} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${y}.zw = vec2(0.0);
}
xTexelC${y}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${y+1}.zw = vec2(0.);
}
xTexelC${y+1}Ready = 1;
}
xC${y} = vec4(
xTexelC${y}.xy, xTexelC${y+1}.xy);
`,y+1<d&&(p+=`
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
`)));y<d&&(p+=`
wTexel = getW(r, ${y}, d1, q);
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
`,y+1<d&&(p+=`
wTexel = getW(r, ${y+1}, d1, q);
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`,p+=`
}
`;let c="",f="";r&&(n?c=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${r}
}`:a?c=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${r}
}`:c=`vec4 activation(vec4 x) {
${r}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${c}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${s};
int q = d2 - d1 * ${s};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function fae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new A9(h):p=new y9(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var mae={kernelName:li,backendName:"webgl",kernelFunc:fae},gae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${s} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${t} - ${n};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},yae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${r}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${r} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Aae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new gae(h);return r.runWebGLProgram(p,[a,s],"float32")}var xae={kernelName:nf,backendName:"webgl",kernelFunc:Aae};function bae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new yae(h);return r.runWebGLProgram(p,[a,s],"float32")}var vae={kernelName:af,backendName:"webgl",kernelFunc:bae},wae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`}};function kae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new wae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Iae={kernelName:sf,backendName:"webgl",kernelFunc:kae},Sae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
const ivec2 strides = ivec2(${a}, ${s});
const ivec2 pads = ivec2(${d}, ${h});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${i}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${o}; w++) {
int wIn = wBeg + w * ${u};
if (wIn >= 0 && wIn < ${r}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function Tae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Sae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Nae={kernelName:eh,backendName:"webgl",kernelFunc:Tae};function Cae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=vr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=x5({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=Sm({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var Eae={kernelName:th,backendName:"webgl",kernelFunc:Cae},Rae="return (x >= 0.0) ? x : (exp(x) - 1.0);",Mae=`
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`,Fae=it({opSnippet:Rae,packedOpSnippet:Mae}),$ae={kernelName:di,backendName:"webgl",kernelFunc:Fae},Pae="return (b >= 1.0) ? a : a * (b + 1.0);",_ae=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,zae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Bh(_ae,n.shape,a.shape):new Pu(Pae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},Oae={kernelName:of,backendName:"webgl",kernelFunc:zae},Dae=`
return vec4(equal(a, b));
`,Lae="return float(a == b);",Bae=wr({opSnippet:Lae,packedOpSnippet:Dae,dtype:"bool",cpuKernelImpl:See}),Wae={kernelName:Yo,backendName:"webgl",kernelFunc:Bae},Vae=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${N.ERF_P};
float a1 = ${N.ERF_A1};
float a2 = ${N.ERF_A2};
float a3 = ${N.ERF_A3};
float a4 = ${N.ERF_A4};
float a5 = ${N.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Uae=it({opSnippet:Vae}),Gae={kernelName:qu,backendName:"webgl",kernelFunc:Uae},jae=Cd+`
return exp(x);
`,Hae=`
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,x9=it({opSnippet:jae,packedOpSnippet:Hae,cpuKernelImpl:Tee,dtype:"float32"}),qae={kernelName:pi,backendName:"webgl",kernelFunc:x9};function ry(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Kae={kernelName:Jo,backendName:"webgl",kernelFunc:ry},J4="return exp(x) - 1.0;",Xae=it({opSnippet:J4,packedOpSnippet:J4,cpuKernelImpl:Nee}),Zae={kernelName:Qo,backendName:"webgl",kernelFunc:Xae},Q4=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${a};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${n});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${n}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${s};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function b9(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Q4("real",l,t),d=new Q4("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Ki({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function Yae(e){let{inputs:t,backend:r}=e,{input:n}=t;return b9(n,!1,r)}var Jae={kernelName:lf,backendName:"webgl",kernelFunc:Yae},Qae=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 Vh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Qae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var ese={kernelName:Ku,backendName:"webgl",kernelFunc:Vh},tse=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 < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},rse={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new tse(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},ev="return floor(x);",nse=it({opSnippet:ev,packedOpSnippet:ev,cpuKernelImpl:Cee}),ase={kernelName:hi,backendName:"webgl",kernelFunc:nse},sse=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,ise=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,ose=wr({opSnippet:sse,packedOpSnippet:ise,dtype:"int32"}),lse={kernelName:ci,backendName:"webgl",kernelFunc:ose},use=class{constructor(e){this.variableNames=["A"];let t=Hr(),[r,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${r}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},dse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Hr(),[r,n]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${n}.0, ${r}.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;
}
`}},pse={kernelName:zp,backendName:"webgl",kernelFunc:hse},du;function hse(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(du==null&&(du=document.createElement("canvas").getContext("2d")),du.canvas.width=l,du.canvas.height=u,du.drawImage(a,0,0,l,u),a=du.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Y().getBool("WEBGL_PACK")?new dse(h):new use(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function cse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,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=m9({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=g9({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,S=c==="leakyrelu",T=c?km(c,!1):null,E=new f9(g,b,T,v,S),R=[a,s];if(i&&R.push(i),o&&R.push(o),S){let _=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var fse={kernelName:zs,backendName:"webgl",kernelFunc:cse};function mse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?km(p,y):null,x=[a,s],b=i!=null,v=o!=null,S=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),S){let _=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(_),f.push(_)}let T;y?T=new A9(g,b,A,v,S):T=new y9(g,b,A,v,S);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(T,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var gse={kernelName:Os,backendName:"webgl",kernelFunc:mse},yse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=yt(t.length),a=yt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${n} strides = ${n}(${this.strides});
void main() {
${a} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${s};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function Ase(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=Eee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let f=new yse(i,h,[u,d]),m=r.runWebGLProgram(f,[c,p],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var xse={kernelName:rl,backendName:"webgl",kernelFunc:Ase},bse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=yt(this.rank),n=vse(e,2);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${n}));
}
`}};function vse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function v9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(Y().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=Ree(x,A,f);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new bse(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var wse={kernelName:tl,backendName:"webgl",kernelFunc:v9},kse="return float(a > b);",Ise=`
return vec4(greaterThan(a, b));
`,Sse=wr({opSnippet:kse,packedOpSnippet:Ise,cpuKernelImpl:Mee,dtype:"bool"}),Tse={kernelName:nl,backendName:"webgl",kernelFunc:Sse},Nse="return float(a >= b);",Cse=`
return vec4(greaterThanEqual(a, b));
`,Ese=wr({opSnippet:Nse,packedOpSnippet:Cse,dtype:"bool",cpuKernelImpl:Fee}),Rse={kernelName:mi,backendName:"webgl",kernelFunc:Ese};function Mse(e){let{inputs:t,backend:r}=e,{input:n}=t;return b9(n,!0,r)}var Fse={kernelName:uf,backendName:"webgl",kernelFunc:Mse},$se="return float(!isnan(x) && !isinf(x));",Pse=it({opSnippet:$se,dtype:"bool"}),_se={kernelName:Xu,backendName:"webgl",kernelFunc:Pse},zse="return float(isinf(x));",Ose=it({opSnippet:zse,dtype:"bool"}),Dse={kernelName:Zu,backendName:"webgl",kernelFunc:Ose},Lse="return float(isnan(x));",Bse=it({opSnippet:Lse,dtype:"bool"}),Wse={kernelName:Yu,backendName:"webgl",kernelFunc:Bse},Vse="return float(a < b);",Use=`
return vec4(lessThan(a, b));
`,Gse=wr({opSnippet:Vse,packedOpSnippet:Use,cpuKernelImpl:$ee,dtype:"bool"}),jse={kernelName:al,backendName:"webgl",kernelFunc:Gse},Hse="return float(a <= b);",qse=`
return vec4(lessThanEqual(a, b));
`,Kse=wr({opSnippet:Hse,packedOpSnippet:qse,cpuKernelImpl:Pee,dtype:"bool"}),Xse={kernelName:sl,backendName:"webgl",kernelFunc:Kse};function Zse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=_ee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var Yse={kernelName:df,backendName:"webgl",kernelFunc:Zse},Jse=Cd+`
return x < 0.0 ? 0./0. : log(x);
`,Qse=`
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`,eie=it({opSnippet:Jse,packedOpSnippet:Qse,cpuKernelImpl:zee}),tie={kernelName:Ai,backendName:"webgl",kernelFunc:eie},rie=Cd+`
return log(1.0 + x);
`,nie=it({opSnippet:rie}),aie={kernelName:Ju,backendName:"webgl",kernelFunc:nie},sie="return float(a >= 1.0 && b >= 1.0);",iie=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,oie=wr({opSnippet:sie,packedOpSnippet:iie,dtype:"bool"}),lie={kernelName:il,backendName:"webgl",kernelFunc:oie},uie="return float(!(x >= 1.0));",die=it({opSnippet:uie}),pie={kernelName:Qu,backendName:"webgl",kernelFunc:die},hie="return float(a >= 1.0 || b >= 1.0);",cie=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,fie=wr({opSnippet:hie,packedOpSnippet:cie,dtype:"bool"}),mie={kernelName:nh,backendName:"webgl",kernelFunc:fie},gie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${s}; j <= ${s}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${i}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${o};
setOutput(val);
}
`}},yie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${s};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${s}; j <= ${s}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${o};
setOutput(result);
}
`}},Aie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new yie(a.shape,s,i,o,l):new gie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},xie={kernelName:ah,backendName:"webgl",kernelFunc:Aie},bie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${n}) * norm + float(${r});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${n})
* float(${a})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${a});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},vie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new bie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},wie={kernelName:pf,backendName:"webgl",kernelFunc:vie};function kie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Bl(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function w9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let S=0;S<x.length;S++)x[S]=a.shape[d[S]];let b=A5(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=Im(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=Oee(A,w.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=kie(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var Iie={kernelName:xi,backendName:"webgl",kernelFunc:w9},Sie=r9+`
return max(a, b);
`,Tie=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wm+`
return result;
`,Nie=wr({opSnippet:Sie,packedOpSnippet:Tie,cpuKernelImpl:Dee}),Cie={kernelName:bi,backendName:"webgl",kernelFunc:Nie};function Eie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;kd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return on({inputs:{x:a},backend:r});let h=new Kp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var Rie={kernelName:vi,backendName:"webgl",kernelFunc:Eie};function Mie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new b5(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var Fie={kernelName:sh,backendName:"webgl",kernelFunc:Mie},$ie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${a};
wR += ${n}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; 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);
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 * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Pie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${d}, ${h}, ${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 < ${o};
wD += ${a}) {
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 += ${s}) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${c} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${u} +
wR * ${u} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function _ie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new b5(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new Pie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var zie={kernelName:cf,backendName:"webgl",kernelFunc:_ie};function Oie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;kd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new Kp(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new $ie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var Die={kernelName:hf,backendName:"webgl",kernelFunc:Oie};function Lie(e,t,r,n){let a=new Kp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new Kp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var Bie={kernelName:ff,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=Lie(n,o,d,l);return[h,p]}};function Wie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Bl(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var Vie={kernelName:wi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let T=0;T<b.length;T++)b[T]=n.shape[d[T]];let v=A5(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(f.dataId);S.values=v}else f=Im(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=Wie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function Uie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Bl(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Gie={kernelName:ki,backendName:"webgl",kernelFunc:Uie},jie=r9+`
return min(a, b);
`,Hie=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wm+`
return result;
`,qie=wr({opSnippet:jie,packedOpSnippet:Hie,cpuKernelImpl:Lee}),Kie={kernelName:Ii,backendName:"webgl",kernelFunc:qie},Xie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=yt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
int start = ${s};
int end = ${i};
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=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
for (int i = 0; i < ${n}; 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};
}
}
${a} coords = outC - start;
setOutput(getX(${o}));
}
`}},Zie=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,f)=>c[0]+e[f]+c[1]);let n=e.length,a=yt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,f)=>c[0]+e[f]).join(","),o=Br("rc",n),l=Br("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
${a} source = rc;
if (source < start) {
source = start * 2 - source - ${h};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${h};
}
source -= start;
`;p=`
${a} rc = outputLoc;
${c}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[n-1]} += 1;
if(${u}) {
${c}
result[1] = getChannel(getX(${l.join()}), ${d});
}
`}else{let c=`
${a} source = rc;
${a} lt = ${a}(lessThan(source, start));
${a} gte = ${a}(greaterThanEqual(source, end));
${a} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${h}) +
gte * ((end - 1) * 2 - source + ${h});
source -= start;
`;p=`
${a} rc = outputLoc;
${c}
result[0] = getChannel(getX(${l.join()}), ${d});
${o[n-1]} += 1;
if(${u}) {
${c}
result[1] = getChannel(getX(${l.join()}), ${d});
}
rc = outputLoc;
${o[n-2]} += 1;
if(${o[n-2]} < ${this.outputShape[n-2]}) {
${c}
result[2] = getChannel(getX(${l.join()}), ${d});
${o[n-1]} += 1;
if(${u}) {
${c}
result[3] = getChannel(getX(${l.join()}), ${d});
}
}
`}this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},Yie=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zie(n.shape,a,s):new Xie(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},Jie={kernelName:Si,backendName:"webgl",kernelFunc:Yie},Qie=`if (b == 0.0) return NAN;
return mod(a, b);`,eoe=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+wm+`
return result;
`,toe=wr({opSnippet:Qie,packedOpSnippet:eoe}),roe={kernelName:ed,backendName:"webgl",kernelFunc:toe},noe=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],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}));
}
`}},aoe=`
if (a == b) {
return 1.0;
};
return a / b;`,soe=`
// 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;
`,k9=wr({opSnippet:aoe,packedOpSnippet:soe,checkOutOfBounds:!0}),ioe={kernelName:ui,backendName:"webgl",kernelFunc:k9},tv="return a - b;",I9=wr({opSnippet:tv,packedOpSnippet:tv,supportsComplex:!0,cpuKernelImpl:tte}),ooe={kernelName:Wi,backendName:"webgl",kernelFunc:I9};function S9(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=w9({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=I9({inputs:{a,b:u},backend:r}),h=x9({inputs:{x:d},backend:r}),p=Sm({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=k9({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var loe={kernelName:Li,backendName:"webgl",kernelFunc:S9};function uoe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:S9({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new noe(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var doe={kernelName:mf,backendName:"webgl",kernelFunc:uoe},poe=Yn+`
return -x;
`,hoe=`
vec4 result = -x;
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;function coe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=Wee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new No(n.shape,hoe):a=new Xa(n.shape,poe),r.runWebGLProgram(a,[n],n.dtype)}var foe={kernelName:ol,backendName:"webgl",kernelFunc:coe},moe=Xn.nonMaxSuppressionV3Impl;function goe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=moe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var yoe={kernelName:ul,backendName:"webgl",kernelFunc:goe},Aoe=Xn.nonMaxSuppressionV4Impl;function xoe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=Aoe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var boe={kernelName:td,backendName:"webgl",kernelFunc:xoe},voe=Xn.nonMaxSuppressionV5Impl;function woe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=voe(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var koe={kernelName:dl,backendName:"webgl",kernelFunc:woe},Ioe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${n}), float(${r}),
float(index == coords.y)));
}
`}},Soe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new Ioe(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Toe={kernelName:hl,backendName:"webgl",kernelFunc:Soe};function V0(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Wh({inputs:{input:n},backend:r}),s=V0({inputs:{x:a},backend:r}),i=Tm({inputs:{input:n},backend:r}),o=V0({inputs:{x:i},backend:r}),l=Ki({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Vh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Noe={kernelName:Cl,backendName:"webgl",kernelFunc:V0};function T9(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Wh({inputs:{input:n},backend:r}),s=T9({inputs:{x:a},backend:r}),i=Tm({inputs:{input:n},backend:r}),o=V0({inputs:{x:i},backend:r}),l=Ki({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Vh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Coe={kernelName:pl,backendName:"webgl",kernelFunc:T9};function Eoe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return ry({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=ry({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=c9({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var Roe={kernelName:cl,backendName:"webgl",kernelFunc:Eoe},Moe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=yt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
int start = ${s};
int end = ${i};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
void main() {
${a} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${a} coords = outC - start;
setOutput(getX(${o}));
}
}
`}},Foe=class{constructor(e,t,r){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 n=e.length,a=yt(n),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=Br("rc",n),l=Br("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
if(${u}) {
`,n===1?"":`}
rc = outputLoc;
${o[n-2]} += 1;
if(${o[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${o[n-1]} += 1;
if(${u}) {`],p=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",c="";for(let f=0,m=n===1?2:4;f<m;f++)c+=`
${h[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${a} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${d});
}
`;c+=n===1?"} ":"}}",this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${c}
setOutput(result);
}
`}},N9=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Vh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Foe(a.shape,s,i):new Moe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},$oe={kernelName:Ni,backendName:"webgl",kernelFunc:N9},Poe=`
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);
`,_oe=`
// 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;
`,zoe=wr({opSnippet:Poe,packedOpSnippet:_oe}),Ooe={kernelName:Ci,backendName:"webgl",kernelFunc:zoe};function Doe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=vr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=Uee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=mh(a.dtype),x=Bl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var Loe={kernelName:Ri,backendName:"webgl",kernelFunc:Doe},C9=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Gee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},Boe={kernelName:rd,backendName:"webgl",kernelFunc:C9},Woe="return 1.0 / x;",Voe=it({opSnippet:Woe}),Uoe={kernelName:nd,backendName:"webgl",kernelFunc:Voe},Goe=Yn+`
return (x < 0.0) ? 0.0 : x;
`,joe=`
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;
`,Hoe=it({opSnippet:Goe,packedOpSnippet:joe}),qoe={kernelName:Mi,backendName:"webgl",kernelFunc:Hoe},Koe=Yn+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Xoe=`
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;
`,Zoe=it({opSnippet:Koe,packedOpSnippet:Xoe}),Yoe={kernelName:$i,backendName:"webgl",kernelFunc:Zoe},Joe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/d[0]},
${u[1]/d[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${h};
// 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);
}
`}},Qoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/d[0]},
${u[1]/d[1]},
${u[1]/d[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${h};
// 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 < ${r-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 ele(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Qoe(a.shape,l,u,s,i):new Joe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var tle={kernelName:Fi,backendName:"webgl",kernelFunc:ele},rle=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*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(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${h});
const float invWidthScale = float(${p});
const int winHeight = int(${c});
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 >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${a-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 nle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new rle(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var ale={kernelName:yf,backendName:"webgl",kernelFunc:nle},sle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/d[0]},
${u[1]/d[1]});
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},ile=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/d[0]},
${u[1]/d[1]},
${u[1]/d[1]});
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
${o}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${r-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function ole(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ile(a.shape,l,u,s,i):new sle(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var lle={kernelName:ad,backendName:"webgl",kernelFunc:ole},ule=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${d});
const float invHeightScale = float(${h});
const float invWidthScale = float(${p});
const int winHeight = int(${c});
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 >= ${s}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${i}) {
continue;
}
float sourceFracRow =
float(${o[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${o[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${n}) - 1),
${r} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${a}) - 1),
${r} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function dle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new ule(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var ple={kernelName:gf,backendName:"webgl",kernelFunc:dle},hle=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>n(o)).join(","),s=yt(r);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},cle=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=Br("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=yt(r);r===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${a}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${i} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${o(n.slice())};
if(${a}){
result.g = ${l(n.slice())};
}
if(${s}) {
result.b = ${u(n.slice())};
if(${a}) {
result.a = ${d(n.slice())};
}
}
setOutput(result);
}
`;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let f=e.map((y,A)=>p(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function fle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=w.parseAxisParam(s,a.shape);if(i===0)return on({inputs:{x:a},backend:r});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cle(a.shape,o):new hle(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var mle={kernelName:ml,backendName:"webgl",kernelFunc:fle},gle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
vec3 fill = vec3(${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]));
${a}
if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${r}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},yle={kernelName:El,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new gle(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},Ale=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,xle=it({opSnippet:Ale}),ble={kernelName:gl,backendName:"webgl",kernelFunc:xle},vle="return inversesqrt(x);",wle=it({opSnippet:vle,cpuKernelImpl:jee}),kle={kernelName:Pi,backendName:"webgl",kernelFunc:wle},E9=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=yt(a.length),l=yt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
${o} strides = ${o}(${a});
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 < ${t}; j++) {
int index = round(${d});
flattenedIndex += index * ${c};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Ile(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new E9(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var Sle={kernelName:yl,backendName:"webgl",kernelFunc:Ile},Tle=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=yt(r);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${n});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function Nle(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Tle(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Cr(a.dtype,s.dtype))}var Cle={kernelName:Al,backendName:"webgl",kernelFunc:Nle},Ele=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${N.SELU_SCALEALPHA};
float scale = ${N.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Rle=it({opSnippet:Ele}),Mle={kernelName:sd,backendName:"webgl",kernelFunc:Rle},Fle=Cd+`
return 1.0 / (1.0 + exp(-1.0 * x));
`,$le=`
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`,Ple=it({opSnippet:Fle,packedOpSnippet:$le,cpuKernelImpl:Hee}),_le={kernelName:zi,backendName:"webgl",kernelFunc:Ple},zle=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Ole=it({opSnippet:zle}),Dle={kernelName:id,backendName:"webgl",kernelFunc:Ole},Lle=Cd+`
return sin(x);
`,Ble=it({opSnippet:Lle}),Wle={kernelName:_i,backendName:"webgl",kernelFunc:Ble},Vle=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,Ule=it({opSnippet:Vle}),Gle={kernelName:bl,backendName:"webgl",kernelFunc:Ule},jle=`
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;
`,Hle=it({opSnippet:jle}),qle={kernelName:od,backendName:"webgl",kernelFunc:Hle},Kle=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=N9({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=vr({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},Xle={kernelName:vl,backendName:"webgl",kernelFunc:Kle};function Zle(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=Kee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var Yle={kernelName:oh,backendName:"webgl",kernelFunc:Zle};function Jle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=Xee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Qle={kernelName:ld,backendName:"webgl",kernelFunc:Jle};function eue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=YI(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var tue={kernelName:lh,backendName:"webgl",kernelFunc:eue};function rue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=YI(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var nue={kernelName:uh,backendName:"webgl",kernelFunc:rue};function aue(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new E9(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var sue={kernelName:dh,backendName:"webgl",kernelFunc:aue};function iue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Ed({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var oue={kernelName:wl,backendName:"webgl",kernelFunc:iue},rv="return sqrt(x);",lue=it({opSnippet:rv,packedOpSnippet:rv,cpuKernelImpl:Zee}),uue={kernelName:Oi,backendName:"webgl",kernelFunc:lue},due="return x * x;",pue=it({opSnippet:due}),hue={kernelName:ud,backendName:"webgl",kernelFunc:pue},nv="return (a - b) * (a - b);",cue=wr({opSnippet:nv,packedOpSnippet:nv}),fue={kernelName:Bi,backendName:"webgl",kernelFunc:cue};function mue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Yn+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Xa(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var gue={kernelName:Gi,backendName:"webgl",kernelFunc:mue},yue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=yt(r.length),s=yt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${a} begin = ${a}(${e});
${a} strides = ${a}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function Aue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=Ot.computeOutShape(A,x,b),E=Ed({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),E=We(a.shape,a.dtype,T),R=Yee(c,E,b,A);v=r.makeTensorInfo(f,a.dtype,R.values)}else{let T=new yue(A,b,c);v=r.runWebGLProgram(T,[a],a.dtype)}let S=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),S}var xue={kernelName:kl,backendName:"webgl",kernelFunc:Aue};function bue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Jee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var vue={kernelName:ph,backendName:"webgl",kernelFunc:bue};function wue(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Qee(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var kue={kernelName:Af,backendName:"webgl",kernelFunc:wue};function Iue(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=ete(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var Sue={kernelName:xf,backendName:"webgl",kernelFunc:Iue},Tue="return tan(x);",Nue=it({opSnippet:Tue}),Cue={kernelName:Il,backendName:"webgl",kernelFunc:Nue},Eue=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,Rue=it({opSnippet:Eue}),Mue={kernelName:Vi,backendName:"webgl",kernelFunc:Rue},Fue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=yt(this.rank),a=$ue(e);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function $ue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function R9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=rte(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Fue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var Pue={kernelName:ts,backendName:"webgl",kernelFunc:R9},_ue=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));
}
}
`}},zue=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 yo(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function av(e){let t=1;for(;t<e;)t*=2;return t}function Oue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=nte(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Vh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=w.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&yo(r,c);let g=av(s),y=av(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),z=new _ue(M),O=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(z,I,"int32",O),yo(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new zue([f,R/2]),I=[[d],[A===null?1:0],[g]],z=A;A=r.runWebGLProgram(M,_,"int32",I),yo(r,z);let O=g/2,j=O*2;for(let X=O;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=Ed({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),yo(r,v);let S=v9({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});yo(r,m);let T=u.slice(0,-1);T.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:T},backend:r}),yo(r,v);let E=S;return S=ve({inputs:{x:S},attrs:{shape:T},backend:r}),yo(r,E),[S,A]}var Due={kernelName:Sl,backendName:"webgl",kernelFunc:Oue},Lue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${o} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${o} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${a});
}
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(${a});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${i} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`}};function Bue(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Lue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var Wue={kernelName:Tl,backendName:"webgl",kernelFunc:Bue};function Vue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;kd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=ate(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Uue={kernelName:bf,backendName:"webgl",kernelFunc:Vue};function Gue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Ed({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var jue={kernelName:Nl,backendName:"webgl",kernelFunc:Gue},Hue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
sumValue += dot(values, segFilter);
`,p="";a%r>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`);let c="";a%r>0&&(c=`
if (inIdx < 0 || inIdx >= ${a}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${c}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${r}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${h}
}
int inIdx = inOffset + ${u};
if (${d===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${h}
} else if (${d===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${h}
} else if (${d===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${h}
}
setOutput(${l});
}
`}};function que(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=mh(a.dtype),g=(b,v,S,T,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},z=new Hue(I,v),O=r.compileAndRun(z,[b,S],T);if(l.push(O),O.shape[1]===E)return O;let j=C9({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=R9({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(O,v,X,T,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=vr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Kue={kernelName:hh,backendName:"webgl",kernelFunc:que},Xue=[Qte,tre,are,ore,ure,hre,fre,gre,bre,wre,Sre,Cre,Mre,_re,Dre,Bre,Vre,Hre,Kre,Zre,ene,one,une,pne,yne,xne,kne,Pte,Tne,Mne,_ne,Wne,Une,jne,qne,Xne,Jne,tae,aae,iae,lae,dae,cae,mae,xae,vae,Iae,Nae,Eae,$ae,Oae,Wae,Gae,qae,Kae,Zae,Jae,ese,rse,ase,lse,pse,fse,gse,xse,wse,Tse,Rse,$te,Fse,Ene,_se,Dse,Wse,zte,jse,Xse,Yse,tie,aie,lie,pie,mie,xie,wie,Iie,Cie,Rie,Fie,zie,Die,Bie,Vie,Gie,Kie,Jie,roe,doe,Wte,foe,yoe,boe,koe,cne,Toe,Coe,Roe,$oe,Ooe,Dte,Loe,Boe,fne,ioe,Uoe,qoe,Yoe,Ute,tle,ale,lle,ple,mle,yle,ble,kle,Sle,Cle,Mle,_le,Dle,Wle,Gle,sne,loe,qle,Xle,Yle,Qle,tue,nue,sue,oue,uue,hue,fue,gue,xue,vue,kue,Sue,ooe,Zte,Cue,Mue,Pue,Due,Wue,Yte,Uue,jue,Kue,Noe];for(let e of Xue)qn(e);var Da=Y();Da.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Da.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Da.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Da.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Da.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Da.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Da.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Da.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Da.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Da.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Zue="return a + b;",Yue="return areal * breal - aimag * bimag;",Jue="return areal * bimag + aimag * breal;",Que="return a / b;",ede="return a * b;",tde="return (a - b) * (a - b);",rde="return a - b;",nde="return f32(a == b);",ade="return vec4<f32>(a == b);",sde="return f32(a > b);",ide="return vec4<f32>(a > b);",ode="return f32(a >= b);",lde="return vec4<f32>(a >= b);",ude="return f32(a < b);",dde="return vec4<f32>(a < b);",pde="return f32(a <= b);",hde="return vec4<f32>(a <= b);",cde="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",fde=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,mde=`
if (isnan(a)) { return a; }
if (isnan(b)) { return b; }
`,M9=`
if (isNaN.r) {
resultTemp.r = uniforms.NAN;
}
if (isNaN.g) {
resultTemp.g = uniforms.NAN;
}
if (isNaN.b) {
resultTemp.b = uniforms.NAN;
}
if (isNaN.a) {
resultTemp.a = uniforms.NAN;
}
`,gde=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,yde=`
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);
`,Ade="return f32(a != b);",xde="return vec4<f32>(a != b);",bde=`
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);
`,vde=`
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 = a < vec4<f32>(0.0) & floor(b) < b;
${M9}
return resultTemp;
`,wde="if (a < 0.0) { return b * a; } return a;",kde=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function sv(e,t){let r=t?M9:mde;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = isnanVec4(a) | isnanVec4(b);
`+r+`
return resultTemp;
`:r+`
return ${e}(a, b);
`}function Uh(e,t){switch(e){case 0:return ede;case 1:return Zue;case 2:return rde;case 3:return Que;case 4:return t?ade:nde;case 5:return t?ide:sde;case 6:return t?lde:ode;case 7:return t?dde:ude;case 8:return t?hde:pde;case 9:return t?fde:cde;case 10:return t?xde:Ade;case 11:return tde;case 12:return t?yde:gde;case 14:return t?kde:wde;case 15:return sv("max",t);case 16:return sv("min",t);case 13:return t?vde:bde;case 17:return Yue;case 18:return Jue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Ide="return abs(a);",Sde="return ceil(a);",Tde="return cos(a);",Nde=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Cde="return exp(a) - 1.0;",Ede="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Rde=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,Mde="return exp(a);",Fde="return floor(a);",$de="return a;",Pde=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,_de="return f32(!(a >= 1.0));",zde="return -a;",Ode="if (a < 0.0) { return uniforms.alpha * a; } return a;",Dde=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`,Lde="if(a < 0.0) { return 0.0; } return a;",Bde="return clamp(a, 0.0, 6.0);",Wde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Vde=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isnanVec4(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;
`,Ude="return 1.0/sqrt(a);",Gde="return 1.0 / (1.0 + exp(-1.0 * a));",jde="return sin(a);",Hde=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,qde="return sqrt(a);",Kde="return a * a;",Xde=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,Zde="return f32(i32((a)));";function bo(e,t){switch(e){case 0:return Ide;case 2:return Tde;case 3:return Nde;case 1:return Sde;case 4:return t?Rde:Ede;case 5:return Mde;case 6:return Cde;case 7:return Fde;case 8:return $de;case 9:return Pde;case 10:return _de;case 11:return zde;case 14:return t?Dde:Ode;case 12:return t?Vde:Lde;case 13:return t?Wde:Bde;case 15:return Ude;case 18:return Gde;case 16:return jde;case 17:return Hde;case 19:return qde;case 20:return Kde;case 21:return Xde;case 22:return Zde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function os(e,t=!1){if(e===null)return null;if(e==="linear")return bo(8);if(e==="relu")return bo(12,t);if(e==="elu")return bo(4,t);if(e==="relu6")return bo(13,t);if(e==="prelu")return Uh(14,t);if(e==="sigmoid")return bo(18,t);if(e==="leakyrelu")return bo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Yde(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function Ar(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function h0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function v5(){return`
@stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`}function Xi(){return`
${v5()}
fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
@builtin(global_invocation_id) GlobalId : vec3<u32>,
@builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
localId = LocalId;
globalId = GlobalId;
numWorkgroups = NumWorkgroups;
`}function et(){return`
${Xi()}
let index = getGlobalIndex();
`}function Jde(e,t,r,n=!1){let a=[];if(a.push(`
let workGroupSizeX = ${r.workGroupSize[0]}u;
let workGroupSizeY = ${r.workGroupSize[1]}u;
let workGroupSizeZ = ${r.workGroupSize[2]}u;
var<private> localId: vec3<u32>;
var<private> globalId: vec3<u32>;
var<private> numWorkgroups: vec3<u32>;
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex() -> i32 {
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
workGroupID.y * numWorkgroups.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`),n===!0)return a.push(`
struct Uniform {
size : i32,
numChannels : i32,
outShapeStrides : vec2<i32>,
dispatchSize : vec3<u32>,
};
@group(0) @binding(0) var<storage, write> result: array<${h0(t.dtype,r.isVec4)}>;
@group(0) @binding(2) var<uniform> uniforms: Uniform;
`),[iv,a.join(`
`),ov(t.shape),r.getUserCode()].join(`
`);let s="struct Uniforms { NAN : f32, ";r.variableNames.forEach((d,h)=>{s+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${Ar(e[h].shape.length)}, `}),s+=`outShape : ${Ar(t.shape.length)}, `;let i=t.shape.length-1;s+=`
outShapeStrides: ${Ar(i)}, `,r.size&&(s+="size : i32, "),r.uniforms&&(s+=r.uniforms),s+="};",a.push(s),r.atomic?a.push(`
@group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
`):a.push(`
@group(0) @binding(0) var<storage, write> result: array<${h0(t.dtype,r.isVec4)}>;
`),r.variableNames.forEach((d,h)=>{a.push(`
@group(0) @binding(${1+h}) var<storage, read> ${d}: array<${h0(e[h].dtype,r.isVec4)}>;
`)}),s!==""&&a.push(`
@group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
`);let[o,l]=ape(t.shape,r.dispatchLayout),u=[iv,a.join(`
`),ov(t.shape),o,Qde(t.shape.length)];if(r.atomic||u.push(epe(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>tpe(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
`);u.push(d)}return u.push(r.getUserCode()),u.join(`
`)}var iv=`
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) && all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) && all(coord < shape);
}
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) && all(coord < shape);
}
fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(shape.y, 1));
}
fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
}
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
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;
}
// NaN defination in IEEE 754-1985 is :
// - sign = either 0 or 1.
// - biased exponent = all 1 bits.
// - fraction = anything except all 0 bits (since all 0 bits represents infinity).
// https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
fn isnan(val: f32) -> bool {
let floatToUint: u32 = bitcast<u32>(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
}
`;function Qde(e){let t="";switch(e){case 0:case 1:t+=`
fn getOutputIndexFromCoords(coords : i32) -> i32 {
return coords;
}
`;break;case 2:t+=`
fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
}
`;break;case 3:t+=`
fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
}
`;break;case 4:t+=`
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
return dot(coords, vec4<i32>(
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
}
`;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function epe(e,t,r){let n=e.length,a=h0(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
result[flatIndex] = ${a}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
result[flatIndex] = ${a}(value);
}`:s=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
result[flatIndex] = ${a}(value);
}
fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
result[flatIndex] = ${a}(value);
}`,n>=2){let i=["d0","d1","d2","d3"].slice(0,n),o=Ar(n);r?s+=`
fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex / 4, value);
}
fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex / 4, value);
}
`:s+=`
fn setOutputAtCoords(${i.map(l=>`${l} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndex(flatIndex, value);
}
fn setOutputAtCoordsI32(${i.map(l=>`${l} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
setOutputAtIndexI32(flatIndex, value);
}
`}return s}function tpe(e,t,r,n){let a=rpe(e,r);return e.shape.length<=t.length&&(a+=npe(e,t,r,n)),a}function rpe(e,t){let r=e.name,n=e.shape.length,a=Ar(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
fn ${s}() -> vec4<f32> {
return vec4<f32>(${r}[0]);
}
`:`
fn ${s}() ->f32 {
return f32(${r}[0]);
}
`;let l=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,u=`${n}D`;return n===0&&(u="1D"),t?`
fn ${s}(${o}) -> vec4<f32> {
return vec4<f32>(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
${l}) / 4]);
}
`:`
fn ${s}(${o}) -> f32 {
return f32(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
${l})]);
}
`}function npe(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=Ar(l);if(w.arraysEqual(e.shape,t)&&n)return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${a}[globalIndex]);
}
fn ${i}Coords(coords : ${u}) -> vec4<f32> {
return vec4<f32>(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
return f32(${a}[globalIndex]);
}
fn ${i}Coords(coords : ${u}) -> f32 {
return f32(${a}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
}
`;let d=N.getBroadcastDims(e.shape,t),h=l-o,p="";if(o===0)return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
return get${s}();
}
fn ${i}Coords(coords : ${u}) -> vec4<f32> {
return get${s}();
}
`:`
fn ${i}Index(globalIndex : i32) -> f32{
return get${s}();
}
fn ${i}Coords(coords : ${u}) -> f32{
return get${s}();
}
`;l<2&&d.length>=1?p="coords = 0;":p=d.map(g=>`coords[${g+h}] = 0;`).join(`
`);let c="";if(l<2&&o>0)c="coords";else if(l>1){let g=Ar(o),y=e.shape.map((A,x)=>`coords[${x+h}]`).join(", ");c=`${g}(${y})`}else c="coords";let f=`uniforms.${a.charAt(0).toLowerCase()+a.slice(1)}Shape`,m=`${o}D`;return r?`
fn ${i}Index(globalIndex : i32) -> vec4<f32> {
var coords = getCoordsFromIndex(globalIndex);
${p}
return ${a}[getIndexFromCoords${m}(${c}, ${f}) / 4];
}
fn ${i}Coords(coordsIn : ${u}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${a}[getIndexFromCoords${m}(${c}, ${f}) / 4];
}
`:`
fn ${i}Index(globalIndex : i32) -> f32 {
var coords = getCoordsFromIndex(globalIndex);
${p}
return f32(${a}[getIndexFromCoords${m}(${c}, ${f})]);
}
fn ${i}Coords(coordsIn : ${u}) -> f32 {
var coords = coordsIn;
${p}
return f32(${a}[getIndexFromCoords${m}(${c}, ${f})]);
}
`}function ape(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${Ar(s)}{
let globalIndex = getGlobalIndex();
return getCoordsFromIndex(globalIndex);
}
`,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=Yde(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=Ar(l),h=`fn getOutputCoords() -> ${d} {
${i}
`;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function ov(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=Ar(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return` fn getCoordsFromIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides[${o}]`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${u};`}).join("");return`
fn getCoordsFromIndex(index : i32) -> ${n} {
${s}
return ${n}(${a.join(",")});
}
`}var F9={};Le(F9,{ArrayBufferToTypedArray:()=>P9,GPUBytesPerElement:()=>ny,computeDispatch:()=>ze,computeWorkGroupSizeForConv2d:()=>w5,computeWorkGroupSizeForMatMul:()=>$9,computeWorkPerThreadForConv2d:()=>k5,flatDispatchLayout:()=>Ke,isWebGPUSupported:()=>I5,tilesFitEvenlyIntoShape:()=>Ya});var Mo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Ya(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((r,n)=>r%e[n]===0)}function ze(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(Mo(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(Mo(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(Mo(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function w5(e,t){let r=Mo(e.x.map(a=>t[a])),n=Mo(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function $9(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function k5(e,t){let r=Mo(e.x.map(a=>t[a])),n=Mo(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Ke(e){return{x:e.map((t,r)=>r)}}function ny(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function P9(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function I5(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function _9(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n/e[0]}>, ${t}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
let RowPerThread = ${e[1]};
let ColPerThread = ${e[0]};
let TileInner = ${n};
${Xi()}
let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
let tileCol = i32(localId.x);
let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
let globalCol = i32(globalId.x);
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, RowPerThread>;
var ACached : vec4<f32>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
var globalColA = tileCol;
let RowPerThreadB = TileInner / i32(workGroupSizeY);
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);
}
}`}var spe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[Ya(n,this.aShape.slice(1)),Ya(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=os(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${s}
}`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
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] / 4;
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);
${a}
${n}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${_9(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
`}};function S5(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
var<workgroup> mm_Asub : array<array<f32, ${a}>, ${r}>;
var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${a}>;
${Xi()}
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) / ${a} + 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 = ${a} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${a} / ${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 * ${a} + 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 * ${a} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${a}; 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 ipe(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Xi()}
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 z9=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=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=n?e[1]:e[2];this.workGroupSize=$9(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[Ya(a,this.aShape.slice(1)),Ya(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let r="",n="";if(this.activation){let s=os(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${s}
}
`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
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);
${a}
${n}
setOutputAtCoords(batch, row, col, value);
}
${this.outputShape[1]>1?S5([this.workPerThread,this.workPerThread,1],this.workGroupSize):ipe(this.workGroupSize)}
`}};function ope(){return`
var<workgroup> sumValues : array<f32, workGroupSizeX>;
${Xi()}
let coords = getOutputCoords();
let batch = coords[0];
let row = coords[1];
let col = coords[2];
var sum = 0.0;
let Length = uniforms.dimInner;
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
let dataA = mm_readA(batch, row, k);
let dataB = mm_readB(batch, k, col);
sum = sum + dataA * dataB;
}
sumValues[localId.x] = sum;
workgroupBarrier();
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
currentSize = currentSize / 2u) {
if (localId.x < currentSize)
{
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
}
workgroupBarrier();
}
if (localId.x == 0u) {
sum = sumValues[0] + sumValues[1];
mm_write(batch, row, col, sum);
}
}
`}var lpe=class{constructor(e,t=!1,r=!1,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=os(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${s}
}
`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
${e}
}
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
var value = valueIn;
let outCoord = vec3<i32>(batch, row, col);
${a}
${n}
setOutputAtCoords(batch, row, col, value);
}
${ope()}
`}};function upe(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
var<workgroup> mm_Asub1 : array<array<f32, ${n}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Asub2 : array<array<f32, ${n}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${r}>, ${n}>;
// 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.
${Xi()}
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) / ${n} + 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 + ${n};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${n};
}
} 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 + ${n};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${n};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${n}; 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 + ${n};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${n};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${n}; 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 >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var dpe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,r="",n="";if(this.activation){let s=os(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${s}
}`:r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${s}
}`,n="value = activation(value, outCoord);"}let a=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${r}
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;
${a}
${n}
setOutputAtCoords(batch, row, col, value);
}
}
${upe(this.workGroupSize)}
`}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var ppe={kernelName:fl,backendName:"webgpu",kernelFunc:qe};function T5({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=Rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],S=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),T=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,T],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new lpe([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new dpe(b,v,[R,c,f],s,l,i):_?M=new spe(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new z9(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[S,T];s&&I.push(s),i&&I.push(i);let z=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}];l==="leakyrelu"&&(z.push({type:"float32",data:[o]}),M.uniforms+=" alpha : f32,");let O=a.runWebGPUProgram(M,I,e.dtype,z),j=qe({inputs:{x:O},backend:a,attrs:{shape:x}});E.push(O);for(let X of E)a.disposeData(X.dataId);return j}function hpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return T5({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var cpe={kernelName:_s,backendName:"webgpu",kernelFunc:hpe},lv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${Uh(this.op,!1)}
}
${et()}
if(index < uniforms.size) {
let areal = getARealByOutputIndex(index);
let aimag = getAImagByOutputIndex(index);
let breal = getBRealByOutputIndex(index);
let bimag = getBImagByOutputIndex(index);
setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},fpe=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
let b = getBByOutputCoords(coords);`;return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${Uh(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${et()}
// 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"}[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${t}
setOutputAtIndex(flatIndex, binaryOperation(a, b));
}
}
}
`}},mpe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${Uh(this.op,this.isVec4)}
}
${et()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}},O9=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${Uh(this.op,!1)}
}
${et()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
let b = getBByOutputIndex(index);
setOutputAtIndex(index, binaryOperation(a, b));
}
}
`}};function uv(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new mpe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new fpe(e,t,r,a):new O9(e,t,r)}function zn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var gpe={kernelName:gi,backendName:"webgpu",kernelFunc:zn};function Rd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=zn({inputs:{x:n},backend:r}),l=zn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var ype={kernelName:Yp,backendName:"webgpu",kernelFunc:Rd},Gh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${bo(this.op,!1)}
}
${et()}
if (index < uniforms.size) {
let a = getAByOutputIndex(index);
setOutputAtIndex(index, unaryOperation(a));
}
}
`}};function kr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Gh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function qr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=uv(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Cr(y.dtype,A.dtype))});else{let g=new lv(17,i.shape,o.shape),y=new lv(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=Rd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Cr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=uv(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:Ape,ceilImpl:xpe,concatImpl:bpe,equalImpl:vpe,expImpl:wpe,expm1Impl:kpe,floorImpl:Ipe,gatherNdImpl:Spe,gatherV2Impl:Tpe,greaterEqualImpl:Npe,greaterImpl:Cpe,lessEqualImpl:Epe,lessImpl:Rpe,logImpl:Mpe,maxImpl:Fpe,maximumImpl:$pe,minimumImpl:Ppe,multiplyImpl:_pe,negImpl:zpe,notEqualImpl:Ope,prodImpl:Dpe,rangeImpl:Lpe,rsqrtImpl:Bpe,simpleAbsImpl:Wpe,sliceImpl:Vpe,stridedSliceImpl:Upe,stringNGramsImpl:Gpe,subImpl:jpe,tileImpl:Hpe,topKImpl:qpe,transposeImpl:Kpe,uniqueImpl:Axe}=Am,Xpe=kr({opType:0,cpuKernelImpl:Wpe}),Zpe={kernelName:jo,backendName:"webgpu",kernelFunc:Xpe},Ype=qr({opSnippet:1,cpuKernelImpl:Ape,supportsComplex:!0}),Jpe={kernelName:Qa,backendName:"webgpu",kernelFunc:Ype},Qpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
${et()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
${e.join(`
`)}
setOutputAtIndex(flatIndex, ${t});
}
}
}
`}};function ehe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return zn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Cr(o,l)),s=n.map(o=>o.shape),i=new Qpe(s);return r.runWebGPUProgram(i,n,a)}var the={kernelName:Ys,backendName:"webgpu",kernelFunc:ehe},D9=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,t=(n,a)=>this.outputShape.length===1?n:`${n}[${a}]`,r=n=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${n}]`;return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${e}
// 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(outputIndex : i32) -> vec2<i32>{
let outputCoords = getCoordsFromIndex(outputIndex);
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 = ${r(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${t("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;
}
${et()}
let outputIndex = index / i32(workGroupSizeX);
let coordInfo = getInputCoordInfo(outputIndex);
let Length = ${r("uniforms.axis")};
var bestIndex = i32(localId.x);
var bestValue = uniforms.infinityValue;
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x[getInputIndex(coordInfo, k)]);
if (!isnan(candidate) && candidate ${this.op} bestValue) {
bestValue = candidate;
bestIndex = k;
}
}
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = bestIndex;
workgroupBarrier();
var reduceSize = min(u32(Length), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
if (candidate ${this.op} bestValue) {
bestValue = candidate;
xBestValues[localId.x] = bestValue;
xBestIndices[localId.x] = xBestIndices[localId.x + interval];
}
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
}
}
`}},rhe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=ze(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]}>;
${v5()}
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
@builtin(workgroup_id) workgroupId : vec3<u32>) {
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] = A[y * width + x];
}
workgroupBarrier();
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputAtIndex((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},nhe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Ar(this.outputShape.length),t=ahe(this.newDim);return`
${et()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromIndex(flatIndex);
setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function ahe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Ja(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Kpe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new rhe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new nhe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var she={kernelName:Ui,backendName:"webgpu",kernelFunc:Ja};function ihe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ja({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new D9(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var ohe={kernelName:Js,backendName:"webgpu",kernelFunc:ihe};function lhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ja({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new D9(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var uhe={kernelName:Wu,backendName:"webgpu",kernelFunc:lhe},L9=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.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(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"),`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
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}
}
}
setOutputAtIndex(index, ${t});
}
}
`}},B9=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d = coords[3];
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutputAtIndex(index, value);
}
}
`}};function dhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new B9(d):(h=new L9(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var phe={kernelName:Qs,backendName:"webgpu",kernelFunc:dhe};function hhe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return T5({a,b:s,transposeA:i,transposeB:o,backend:r})}var che={kernelName:ei,backendName:"webgpu",kernelFunc:hhe},fhe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Ar(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Ar(this.rank),t=mhe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${ay[a]} = uniforms.start[${a}] + coords.${ay[a]};`),`
${et()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getCoordsFromIndex(index);
${r.join(`
`)}
setOutputAtIndex(index, getSource(${t}));
}
}
`}},ay=["x","y","z","w","u","v"];function mhe(e){if(e===1)return"sourceLoc";if(e<=6)return ay.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Md(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=Ot.parseSliceParams(a,s,i);if(Ot.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Vpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new fhe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var ghe={kernelName:xl,backendName:"webgpu",kernelFunc:Md},yhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Ja({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Md({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},Ahe={kernelName:Ho,backendName:"webgpu",kernelFunc:yhe},W9=qr({opSnippet:10,dtype:"bool",cpuKernelImpl:Ope}),xhe={kernelName:ll,backendName:"webgpu",kernelFunc:W9};function jh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return zn({inputs:{x:a.complexTensorInfos.real},backend:r})}var bhe={kernelName:ih,backendName:"webgpu",kernelFunc:jh};function vhe(e,t){let r=new Gh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function sy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return zn({inputs:{x:a},backend:r});let i=_t(a.shape),o=sy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Rd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=jh({inputs:{input:a},backend:r}),o=sy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=zn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return vhe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=W9({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var whe={kernelName:ti,backendName:"webgpu",kernelFunc:sy},khe=kr({opType:1,cpuKernelImpl:xpe}),Ihe={kernelName:ri,backendName:"webgpu",kernelFunc:khe},She=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
${et()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isnan(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputAtIndex(index, clampedValue);
}
}
`}},The=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
${et()}
if(index < uniforms.size) {
let value = getAByOutputIndex(index);
if (isnan(value)) {
setOutputAtIndex(index, value);
return;
}
setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Nhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return w.sizeFromShape(a.shape)%4===0?o=new She(a.shape):o=new The(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var Che={kernelName:es,backendName:"webgpu",kernelFunc:Nhe},Ehe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
${et()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${e.join(`
`)}
}
}
}
`}};function Nm(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return zn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Rhe={kernelName:rh,backendName:"webgpu",kernelFunc:Nm};function iy(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>jh({inputs:{input:A},backend:r})),f=e.map(A=>Nm({inputs:{input:A},backend:r})),m=iy(c,t,r),g=iy(f,t,r),y=Rd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=bpe(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=Mhe(e,t,r),o=s.map(c=>c.shape),l=new Ehe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function Mhe(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function V9(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),iy(o,s,r)}var Fhe={kernelName:qo,backendName:"webgpu",kernelFunc:V9},$he=class{constructor(e,t=!1,r=null,n=!1,a=!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.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.hasLeakyreluAlpha=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ya(e,[r,a]),Ya(t,[a,n])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
let divBy4Remainder${e} = flatIndex${e} % 4;
let divBy4Index${e} = flatIndex${e} / 4;
let curData${e} = x[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[divBy4Index${e} + 1];
if (divBy4Remainder${e} == 1) {
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
} else if (divBy4Remainder${e} == 2) {
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
} else if (divBy4Remainder${e} == 3) {
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
}
}
`}getUserCode(){let e=_9(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`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[getIndexFromCoords4D(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);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} else if (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,r=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${t}
}
return vec4<f32>(0.0);
`,n=this.fitB?"return W[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,a="",s="";if(this.activation){let o=os(this.activation,this.isVec4);if(this.hasPreluActivationWeights)a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`;else{if(this.hasLeakyreluAlpha)throw a=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaByOutputCoords(outCoord);
${o}
}`,new Error("Leakyrelu is not supported.");a=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${o}
}`}s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${a}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${r}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${n}
}
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);
${i}
${s}
setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${e}
`}},Phe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=w5(this.dispatchLayout,this.outputShape),this.elementsPerThread=k5(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ya(n,[s,o]),Ya(a,[o,i])]}getUserCode(){let e=S5(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x[getIndexFromCoords4D(coord, uniforms.xShape)];
}
return 0.0;`,r=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,n=this.fitB?"return W[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W[row * uniforms.dimBOuter + col];
}
return 0.0;
`,a="",s="";if(this.activation){let o=os(this.activation,!1);this.hasPreluActivationWeights?a=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${o}
}`:a=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let i=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
${a}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${r}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${n}
}
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);
${i}
${s}
result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},_he=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=os(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${n}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${n}
}
`,t="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(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)) {
${r}
${t}
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Xi()}
let coords = getOutputCoords();
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}},zhe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${et()}
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
let rc = getCoordsFromIndex(flatIndex);
if(flatIndex < uniforms.size) {
let blockIndex = rc[0];
let pos = rc[1];
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
var value = 0.0;
if(d0 < uniforms.aShape[${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);
}
}
setOutputAtIndex(flatIndex, value);
}
}
}
`}};function Ohe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=T5({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function Dhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=m*f,S=[v,b],T=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new zhe(S,x),z=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],O=n.runWebGPUProgram(I,[_],_.dtype,z),j=qe({inputs:{x:O},backend:n,attrs:{shape:[1,S[0],S[1]]}});R.push(O),R.push(j);let X=[1,S[0],S[1]],D=new z9(X,[1,v,r.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,J=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],ie=[j,M];a&&ie.push(a),s&&ie.push(s),o==="leakyrelu"&&(z.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let Z=n.runWebGPUProgram(D,ie,j.dtype,J),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function U9({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return Ohe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return Dhe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new _he(r,l,o,u);else{p?d=new $he(r,l,o,u):d=new Phe(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),o==="leakyrelu"&&(f.push({type:"float32",data:[i]}),d.uniforms+=" alpha : f32,"),n.runWebGPUProgram(d,m,e.dtype,f)}function Lhe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return U9({x:a,filter:s,convInfo:p,backend:n})}var Bhe={kernelName:ni,backendName:"webgpu",kernelFunc:Lhe},Whe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=w5(this.dispatchLayout,this.outputShape),this.elementsPerThread=k5(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${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[getIndexFromCoords4D(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[getIndexFromCoords4D(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[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
}
${S5(this.elementsPerThread,this.workGroupSize)}
`}},Vhe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
${et()} {
if(index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let batch = coords[0];
let d1 = coords[${r}];
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 (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutputAtIndex(index, dotProd);
}
}
`}};function Uhe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Vhe(p);else{f=new Whe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[a,s],"float32",c)}var Ghe={kernelName:ai,backendName:"webgpu",kernelFunc:Uhe},jhe=kr({opType:2}),Hhe={kernelName:si,backendName:"webgpu",kernelFunc:jhe},qhe=kr({opType:3}),Khe={kernelName:ii,backendName:"webgpu",kernelFunc:qhe},Xhe=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=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) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${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`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let height_ratio = f32(${r});
let width_ratio = f32(${s});
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 = ${n};
let width_scale = ${i};
let in_y = ${a};
if( in_y < 0.0 || in_y > ${e} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let in_x = ${o};
if( in_x < 0.0 || in_x > ${t} ) {
setOutputAtIndex(index, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${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;
setOutputAtIndex(index, 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);
setOutputAtIndex(index, newValue);
}
}
}
`}},Zhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Xhe(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Yhe={kernelName:Xo,backendName:"webgpu",kernelFunc:Zhe},dv=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=r,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",r=this.exclusive?t:`getX(${pv(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],a="",s="";return this.exclusive?(a=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(a=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
${et()}
if (index < uniforms.size) {
var coords = getCoordsFromIndex(index);
let end = ${hv(e,"coords",this.op)};
var val = ${r};
let pow2 = i32(pow(2.0, uniforms.index));
if (${a}) {
let idx = ${s};
${hv(e,"coords",this.op)} = idx;
val ${this.op}= getX(${pv(e,"coords",this.op)});
}
setOutputAtIndex(index, val);
}
}
`}};function pv(e,t,r){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 ${r} for rank ${e} is not yet supported`)}function hv(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function G9(e,t,r,n,a,s){let i=t.shape.length,o=N.getAxesPermutation([n],i),l=t;o!=null&&(l=Ja({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=zn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new dv(e,l.shape,!1,s),f=h,m=[{type:"float32",data:[p]}];h=r.runWebGPUProgram(c,[h],h.dtype,m),r.disposeData(f.dataId)}if(a){let p=new dv(e,l.shape,a,s),c=h,f=[{type:"float32",data:[0]}];h=r.runWebGPUProgram(p,[h],h.dtype,f),r.disposeData(c.dataId)}if(o!=null){let p=N.getUndoAxesPermutation(o),c=Ja({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeData(h.dataId),r.disposeData(l.dataId),c}return h}function Jhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return G9("*",a,r,s,i,o)}var Qhe={kernelName:Ko,backendName:"webgpu",kernelFunc:Jhe};function ece(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return G9("+",a,r,s,i,o)}var tce={kernelName:oi,backendName:"webgpu",kernelFunc:ece},rce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(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 = ${this.getInputSamplingString()};
setOutputAtIndex(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function nce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new rce(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var ace={kernelName:Zo,backendName:"webgpu",kernelFunc:nce},j9=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=os(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${n}
}`:e=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${n}
}
`,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
${e}
${v5()}
fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
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)) {
${r}
${t}
setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},H9=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=os(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsByOutputCoords(outCoord);
${n}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${n}
}
`,t="dotProd = activation(dotProd, coords);"}let r=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
${e}
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)) {
setOutputAtCoords(batch, row, col, chan, value);
}
}
${Xi()}
let coords = getOutputCoords();
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / uniforms.channelMul;
let q = d2 - d1 * uniforms.channelMul;
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + uniforms.filterHeight *
uniforms.dilation[0];
let inputColEnd = inputColStart + uniforms.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 < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < uniforms.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 < uniforms.filterHeight; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < uniforms.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;
}
}
}
${r}
${t}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function sce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new j9(h):(c=new H9(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var ice={kernelName:li,backendName:"webgpu",kernelFunc:sce},q9=qr({opSnippet:0,cpuKernelImpl:_pe,supportsComplex:!0}),oce={kernelName:Ti,backendName:"webgpu",kernelFunc:q9},lce=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
if (isnan(candidate)) {
bestValue = uniforms.NAN;
} else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
{ bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let r=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
fn DIV_CEIL(a : u32, b : u32) -> u32 {
return ((a - 1u) / b + 1u);
}
${`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`}
fn getOffset(outputIndex : i32) -> i32 {
let outputCoords = getCoordsFromIndex(outputIndex);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${et()}
let outputIndex = index / i32(workGroupSizeX);
let offset = getOffset(outputIndex);
var bestValue = ${t};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
k = k + i32(workGroupSizeX)) {
let candidate = f32(x[offset + k]);
${e}
}
xBestValues[localId.x] = bestValue;
workgroupBarrier();
var reduceSize = min(u32(Length), workGroupSizeX);
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
currentSize = reduceSize / 2u) {
let interval = DIV_CEIL(reduceSize, 2u);
if (localId.x < currentSize) {
let candidate = xBestValues[localId.x + interval];
${e}
xBestValues[localId.x] = bestValue;
}
reduceSize = interval;
workgroupBarrier();
}
if (localId.x == 0u && outputIndex < uniforms.size) {
${r}
}
}
`}};function Hh(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Ja({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=Fpe(m,w.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=Dpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":mh(e.dtype),x=[{type:"int32",data:[m]}],b=new lce(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),f=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function N5(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Hh(a,s,i,"sum",r)}var uce={kernelName:Di,backendName:"webgpu",kernelFunc:N5};function dce(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Ja({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=q9({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=N5({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var pce={kernelName:th,backendName:"webgpu",kernelFunc:dce},hce=kr({opType:4}),cce={kernelName:di,backendName:"webgpu",kernelFunc:hce},fce=qr({opSnippet:4,dtype:"bool",cpuKernelImpl:vpe}),mce={kernelName:Yo,backendName:"webgpu",kernelFunc:fce},K9=kr({opType:5,cpuKernelImpl:wpe,dtype:"float32"}),gce={kernelName:pi,backendName:"webgpu",kernelFunc:K9};function oy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var yce={kernelName:Jo,backendName:"webgpu",kernelFunc:oy},Ace=kr({opType:6,cpuKernelImpl:kpe}),xce={kernelName:Qo,backendName:"webgpu",kernelFunc:Ace},bce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
setOutputAtIndex(index, uniforms.value);
}
}
`}};function Fd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new bce(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var vce={kernelName:Ku,backendName:"webgpu",kernelFunc:Fd},wce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputAtIndex(index, outputValue);
}
}
`}},kce={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new wce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},Ice=kr({opType:7,cpuKernelImpl:Ipe}),Sce={kernelName:hi,backendName:"webgpu",kernelFunc:Ice},Tce=qr({opSnippet:12,dtype:"int32"}),Nce={kernelName:ci,backendName:"webgpu",kernelFunc:Tce},Cce=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},X9=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=Jde(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function Z9(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function cv(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=Z9(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>X9(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var Ece={kernelName:zp,backendName:"webgpu",kernelFunc:Rce},pu;function Rce(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Y().getBool("WEBGPU_USE_IMPORT")&&i)return cv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(pu==null&&(pu=document.createElement("canvas").getContext("2d")),pu.canvas.width=d,pu.canvas.height=h,pu.drawImage(a,0,0,d,h),a=pu.canvas),u||l||i||o)return cv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var Mce=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
${et()}
if (index < uniforms.size)
{
let xValue = getXByOutputIndex(index);
let meanValue = getMeanByOutputIndex(index);
let varianValue = getVarianceByOutputIndex(index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
}
`}},Fce={kernelName:fi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Mce(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function $ce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return U9({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var Pce={kernelName:zs,backendName:"webgpu",kernelFunc:$ce};function _ce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=d;f==null&&(f=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=N.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new j9(m,y,p,A):(b=new H9(m,y,p,A),x.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var zce={kernelName:Os,backendName:"webgpu",kernelFunc:_ce},Oce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Ar(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(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;
}
setOutputAtIndex(index, getA(flattenIndex, coords[1]));
}
}
`}};function Dce(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=Spe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let f=new Oce(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(f,[c,p],c.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var Lce={kernelName:rl,backendName:"webgpu",kernelFunc:Dce},Bce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Wce(this.aShape);return`
${et()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let indexZ = i32(getIndices(resRC.x, resRC.z));
let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
setOutputAtIndex(index, inBounds * getA(${e}));
}
}
`}};function Wce(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function Y9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),S=Tpe(v,x,f);return h.forEach(T=>r.disposeData(T.dataId)),r.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Bce(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var Vce={kernelName:tl,backendName:"webgpu",kernelFunc:Y9},Uce=qr({opSnippet:5,cpuKernelImpl:Cpe,dtype:"bool"}),Gce={kernelName:nl,backendName:"webgpu",kernelFunc:Uce},jce=qr({opSnippet:6,dtype:"bool",cpuKernelImpl:Npe}),Hce={kernelName:mi,backendName:"webgpu",kernelFunc:jce};function qce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Gh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var Kce={kernelName:yi,backendName:"webgpu",kernelFunc:qce},Xce=qr({opSnippet:7,dtype:"bool",cpuKernelImpl:Rpe}),Zce={kernelName:al,backendName:"webgpu",kernelFunc:Xce},Yce=qr({opSnippet:8,dtype:"bool",cpuKernelImpl:Epe}),Jce={kernelName:sl,backendName:"webgpu",kernelFunc:Yce},Qce=kr({opType:9,cpuKernelImpl:Mpe}),e0e={kernelName:Ai,backendName:"webgpu",kernelFunc:Qce},t0e=qr({opSnippet:9,dtype:"bool"}),r0e={kernelName:il,backendName:"webgpu",kernelFunc:t0e},n0e=kr({opType:10}),a0e={kernelName:Qu,backendName:"webgpu",kernelFunc:n0e};function J9(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Hh(a,s,i,"max",r)}var s0e={kernelName:xi,backendName:"webgpu",kernelFunc:J9},i0e=qr({opSnippet:15,cpuKernelImpl:$pe}),o0e={kernelName:bi,backendName:"webgpu",kernelFunc:i0e};function l0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return zn({inputs:{x:a},backend:r});h=new B9(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new L9(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var u0e={kernelName:vi,backendName:"webgpu",kernelFunc:l0e};function d0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Hh(a,i,s,"mean",r)}var p0e={kernelName:wi,backendName:"webgpu",kernelFunc:d0e};function h0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Hh(a,s,i,"min",r)}var c0e={kernelName:ki,backendName:"webgpu",kernelFunc:h0e},f0e=qr({opSnippet:16,cpuKernelImpl:Ppe}),m0e={kernelName:Ii,backendName:"webgpu",kernelFunc:f0e},g0e=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=Ar(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${et()}
if (index < uniforms.size) {
let start = ${i}(${t});
let end = ${i}(${r});
var outC = getCoordsFromIndex(index);
for (var i = 0; i < ${e}; i = i + 1) {
if (${s} < ${n}) {
${s} = ${n} * 2 - ${s} - ${this.offset};
} else if(${s} >= ${a}) {
${s} = (${a} - 1) * 2 - ${s} + ${this.offset};
}
}
let coords = outC - start;
setOutputAtIndex(index, getX(${o}));
}
}
`}},y0e={kernelName:Si,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new g0e(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function A0e(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=zpe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Gh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var x0e={kernelName:ol,backendName:"webgpu",kernelFunc:A0e};function b0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Xn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var v0e={kernelName:ul,backendName:"webgpu",kernelFunc:b0e};function w0e(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Xn.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var k0e={kernelName:dl,backendName:"webgpu",kernelFunc:w0e};function U0(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=jh({inputs:{input:n},backend:r}),s=U0({inputs:{x:a},backend:r}),i=Nm({inputs:{input:n},backend:r}),o=U0({inputs:{x:i},backend:r}),l=Rd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Fd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var I0e={kernelName:Cl,backendName:"webgpu",kernelFunc:U0};function Q9(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=jh({inputs:{input:n},backend:r}),s=Q9({inputs:{x:a},backend:r}),i=Nm({inputs:{input:n},backend:r}),o=U0({inputs:{x:i},backend:r}),l=Rd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Fd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var S0e={kernelName:pl,backendName:"webgpu",kernelFunc:Q9};function T0e(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return oy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=oy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=V9({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var N0e={kernelName:cl,backendName:"webgpu",kernelFunc:T0e},C0e=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Ar(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${et()}
if (index < uniforms.size) {
let start = ${a};
let end = ${s};
let outC = getCoordsFromIndex(index);
if (${i} || ${o}) {
setOutputAtIndex(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputAtIndex(index, getX(${l}));
}
}
}
`}},eS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return zn({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Fd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new C0e(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},E0e={kernelName:Ni,backendName:"webgpu",kernelFunc:eS},R0e=qr({opSnippet:13}),M0e={kernelName:Ci,backendName:"webgpu",kernelFunc:R0e};function F0e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new O9(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var $0e={kernelName:Ei,backendName:"webgpu",kernelFunc:F0e};function P0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Hh(a,s,i,"prod",r)}var _0e={kernelName:Ri,backendName:"webgpu",kernelFunc:P0e},z0e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Lpe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},O0e={kernelName:rd,backendName:"webgpu",kernelFunc:z0e},tS=qr({opSnippet:3}),D0e={kernelName:ui,backendName:"webgpu",kernelFunc:tS},L0e=kr({opType:12}),B0e={kernelName:Mi,backendName:"webgpu",kernelFunc:L0e},W0e=kr({opType:13}),V0e={kernelName:$i,backendName:"webgpu",kernelFunc:W0e},U0e=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC =
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
// 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;
setOutputAtIndex(index, newValue);
}
}
`}};function G0e(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new U0e(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var j0e={kernelName:Fi,backendName:"webgpu",kernelFunc:G0e},H0e=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveOutSize = vec2<f32>(
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${e};
// 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 + uniforms.roundBase)));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutputAtIndex(index, newValue);
}
}
`}};function q0e(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new H0e(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var K0e={kernelName:ad,backendName:"webgpu",kernelFunc:q0e},X0e=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
cosRadians : f32,`,this.shaderKey="rotate",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`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(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));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputAtIndex(index, outputValue);
}
}
`}},Z0e={kernelName:El,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new X0e(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Y0e=kr({opType:15,cpuKernelImpl:Bpe}),J0e={kernelName:Pi,backendName:"webgpu",kernelFunc:Y0e},Q0e=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Ke(e),this.dispatch=ze(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=Ar(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
return index;
}
`):this.updatesRank===2&&(n="coords[0], coords[1]",a="vec2<i32>(flattenedIndex, coords[1])",s=`
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.updatesShape[1];
let d1 = index - d0 * uniforms.updatesShape[1];
return vec2<i32>(d0, d1);
}
`);let i=`getUpdates(${n})`,o=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
var assumed = atomicLoad(&(result[flatIndex]));
var success = 0;
for (; success == 0;) {
let new = bitcast<f32>(assumed) + updateValue;
let newI32 = bitcast<i32>(new);
let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
assumed = resValue[0];
success = resValue[1];
}
`;return`
${s}
${et()}
if (index < uniforms.size) {
let coords = getUpdatesCoordsFromFlatIndex(index);
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${t}));
flattenedIndex = flattenedIndex + indexInside * ${r};
}
let updateValue = ${i};
let flatIndex = getOutputIndexFromCoords(${a});
${o}
}
}`}};function efe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=Fd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new Q0e(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var tfe={kernelName:yl,backendName:"webgpu",kernelFunc:efe},rfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
${et()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputAtIndex(index, getA(${t}));
} else {
setOutputAtIndex(index, getB(${t}));
}
}
}
`}};function nfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new rfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Cr(a.dtype,s.dtype))}var afe={kernelName:Al,backendName:"webgpu",kernelFunc:nfe},sfe=kr({opType:18}),ife={kernelName:zi,backendName:"webgpu",kernelFunc:sfe},ofe=kr({opType:16}),lfe={kernelName:_i,backendName:"webgpu",kernelFunc:ofe},ufe=kr({opType:17}),dfe={kernelName:bl,backendName:"webgpu",kernelFunc:ufe},rS=qr({opSnippet:2,cpuKernelImpl:jpe,supportsComplex:!0}),pfe={kernelName:Wi,backendName:"webgpu",kernelFunc:rS};function hfe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=J9({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=rS({inputs:{a,b:u},backend:r}),h=K9({inputs:{x:d},backend:r}),p=N5({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=tS({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var cfe={kernelName:Li,backendName:"webgpu",kernelFunc:hfe},ffe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=eS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Ja({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},mfe={kernelName:vl,backendName:"webgpu",kernelFunc:ffe},gfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=Ar(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${et()}
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 = getCoordsFromIndex(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)
{
setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function yfe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new gfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Afe={kernelName:dh,backendName:"webgpu",kernelFunc:yfe};function xfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Md({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var bfe={kernelName:wl,backendName:"webgpu",kernelFunc:xfe},vfe=kr({opType:19}),wfe={kernelName:Oi,backendName:"webgpu",kernelFunc:vfe},kfe={kernelName:ud,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Gh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},Ife=qr({opSnippet:11}),Sfe={kernelName:Bi,backendName:"webgpu",kernelFunc:Ife},Tfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Ar(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
setOutputAtIndex(index, getX(${t}));
}
}
`}};function Nfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=Ot.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=Ot.computeOutShape(A,x,b),T=Md({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=qe({inputs:{x:T},backend:r,attrs:{shape:f}}),r.disposeData(T.dataId)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),T=We(a.shape,a.dtype,S),E=Upe(c,T,b,A);v=r.makeTensorInfo(f,a.dtype,E.values)}else{let S=new Tfe(c),T=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(S,[a],a.dtype,T);v=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var Cfe={kernelName:kl,backendName:"webgpu",kernelFunc:Nfe};function Efe(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Gpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var Rfe={kernelName:ph,backendName:"webgpu",kernelFunc:Efe},Mfe=kr({opType:21}),Ffe={kernelName:Vi,backendName:"webgpu",kernelFunc:Mfe},$fe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Pfe(this.rank,"uniforms.");return`
${et()}
if (index < uniforms.size) {
let resRC = getCoordsFromIndex(index);
setOutputAtIndex(index, getA(${e}));
}
}
`}};function Pfe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function _fe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Hpe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new $fe(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var zfe={kernelName:ts,backendName:"webgpu",kernelFunc:_fe},Ofe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[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.
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
var i = 0;
if (isFirstInPair) {
i = elemIdx;
} else {
i = elemIdx - uniforms.inc;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.inc;
} else {
i1 = i32(getIndices(batch, i + uniforms.inc));
}
var x0 = f32(0.0);
var x1 = f32(0.0);
if (i0 < uniforms.inputSize) {
x0 = getX(batch, i0);
} else {
x0 = uniforms.negativeInf;
}
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = uniforms.negativeInf;
}
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) {
// Elements in opposite order of direction
let iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}},Dfe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
${et()}
if (index < uniforms.size) {
let outC = getCoordsFromIndex(index);
let batch = outC[0];
let elemIdx = outC[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.
var i = 0;
if (elemIdx < uniforms.k) {
i = elemIdx;
} else {
i = elemIdx * 2 - elemIdx % uniforms.k;
}
var i0 = 0;
if (uniforms.firstPass == 1) {
i0 = i;
} else {
i0 = i32(getIndices(batch, i));
}
var i1 = 0;
if (uniforms.firstPass == 1) {
i1 = i + uniforms.k;
} else {
i1 = i32(getIndices(batch, i + uniforms.k));
}
let x0 = getX(batch, i0);
var x1 = f32(0.0);
if (i1 < uniforms.inputSize) {
x1 = getX(batch, i1);
} else {
x1 = x0;
}
if (x0 >= x1) {
setOutputAtIndex(index, f32(i0));
} else {
setOutputAtIndex(index, f32(i1));
}
}
}
`}};function hu(e,t){t!==null&&e.disposeData(t.dataId)}function fv(e){let t=1;for(;t<e;)t*=2;return t}function Lfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,S]=qpe(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Fd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=fv(s),p=fv(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,v,S)=>{let T=f(),E=new Ofe(S),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],_=c;c=r.runWebGPUProgram(E,T,"int32",R),hu(r,_)};for(let b=1;b<h;b*=2){let v=b*2;for(let S=b;S>=1;S/=2)m(v,S,[u,p])}for(let b=p;b>h;b/=2){let v=f(),S=new Dfe([u,b/2]),T=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(S,v,"int32",T),hu(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=Md({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),hu(r,g);let y=Y9({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});hu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),hu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),hu(r,x),[y,c]}var Bfe={kernelName:Sl,backendName:"webgpu",kernelFunc:Lfe},Wfe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(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;
}
}
} else if (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);
} else if (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);
}
} else if (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);
} else if (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;
}
${et()}
if (index < uniforms.size) {
let coords = getCoordsFromIndex(index);
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;
}
}
setOutputAtIndex(index, outputValue);
}
}
`}};function Vfe(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Wfe(g),A=i==="nearest"?1:2,x;switch(o){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 r.runWebGPUProgram(y,[a,s],"float32",b)}var Ufe={kernelName:Tl,backendName:"webgpu",kernelFunc:Vfe};function Gfe(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Md({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var jfe={kernelName:Nl,backendName:"webgpu",kernelFunc:Gfe},Hfe=[cpe,Zpe,Jpe,the,ohe,uhe,phe,che,Ahe,whe,Ihe,Che,ype,Fhe,Bhe,Ghe,Hhe,Khe,Yhe,Qhe,tce,ace,ice,pce,cce,mce,gce,yce,xce,vce,kce,Ece,Sce,Nce,Fce,Pce,zce,Lce,Vce,Gce,Hce,gpe,Rhe,Kce,Zce,Jce,e0e,r0e,a0e,s0e,o0e,u0e,p0e,c0e,m0e,y0e,oce,x0e,v0e,k0e,xhe,S0e,N0e,E0e,M0e,$0e,_0e,O0e,bhe,D0e,B0e,V0e,ppe,j0e,K0e,Z0e,J0e,tfe,afe,ife,lfe,dfe,ghe,Cfe,Rfe,cfe,mfe,Afe,bfe,wfe,kfe,Sfe,pfe,uce,Ffe,zfe,Bfe,Ufe,she,jfe,I0e];for(let e of Hfe)qn(e);var qfe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=mv(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=mv(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function mv(e,t){return`${e}_${t}`}var nS=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){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(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>"};
${et()}
let flatIndexBase = index * uniforms.numChannels;
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
let flatIndex = flatIndexBase + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromIndex(flatIndexBase);
let values = ${e};
result[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),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 r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Kfe=class extends nS{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 r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Xfe=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),gv=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;w.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),w.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},aS=class extends _u{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!I5())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 qfe(this.device),this.tensorMap=new Xp(this,ar()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().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 aS.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.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.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,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*ny(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*ny(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}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 nS),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Kfe),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),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 r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=P9(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,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);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let T=this.tensorMap.get(a.dataId);return T.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=gv(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(T=>T.shape),o="int32";i.map(T=>{s.push({type:o,data:T})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let T=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?T/4:T]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((T,E)=>{if(T.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(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),h=d.map(T=>T.dtype).concat(a.dtype),p=d.map(T=>N.getBroadcastDims(T.shape,a.shape)),c=d.map(T=>w.arraysEqual(T.shape,a.shape)).join("_"),f=p.map(T=>T.join("_")).join(";"),m=Z9(e,i,h,f,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(m,()=>X9(this.device,e,y,d,a)),x=this.activeTimers!=null,b=Cce(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let v=this.getComputePass();x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(A),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let S={byteSize:u.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};return this.uniformDisposalQueue.push(S),Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}runFromPixelsProgram(e,t,r,n,a){e.dispatch=gv(this.device,e);let s=this.device.createBindGroup({layout:r.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:n},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,s),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a),this.submitQueue(),o&&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),r=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,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=Xfe){return Y().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&w.sizeFromShape(r.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)}},C5=aS;C5.nextDataId=0;var sS={};Le(sS,{WebGPUBackend:()=>C5,webgpu_util:()=>F9});I5()&&Ml("webgpu",async()=>{Y().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Y().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r=t.limits,n={},a=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension},a?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 s=await t.requestDevice(n);return new C5(s,a)},3);var Ut=(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",e))(Ut||{}),Cm=(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",e))(Cm||{}),iS;function Zfe(e){iS=e.wasm.cwrap(_s,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Yfe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=Cm[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,S=new Uint8Array(new Int32Array(a.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return iS(p,S,a.shape.length,c,T,s.shape.length,l,u,g,f,m,h||0,v),b}var Jfe={kernelName:_s,backendName:"wasm",setupFunc:Zfe,kernelFunc:Yfe};function Ir(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Qfe=Ir(jo);function Kr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var eme=!0,tme=Kr(Qa,eme),oS;function rme(e){oS=e.wasm.cwrap(Ys,null,["array","number","number","number"])}function nme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return oS(s,a.length,Ut[n.dtype],i),n}var ame={kernelName:Ys,backendName:"wasm",setupFunc:rme,kernelFunc:nme};function Em(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var sme={kernelName:gi,backendName:"wasm",kernelFunc:Em},lS;function ime(e){lS=e.wasm.cwrap(Ui,null,["number","array","number","number","number","array","number"])}function Xs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=lme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=ome(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=Em({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return lS(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function ome(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function lme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var ume={kernelName:Ui,backendName:"wasm",kernelFunc:Xs,setupFunc:ime};function Zi(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Xs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var uS;function dme(e){uS=e.wasm.cwrap(Lu,null,["number, number, number"])}function pme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Zi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;uS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var hme={kernelName:Lu,backendName:"wasm",setupFunc:dme,kernelFunc:pme},dS;function cme(e){dS=e.wasm.cwrap(Bu,null,["number, number, number"])}function fme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Zi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;dS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var mme={kernelName:Bu,backendName:"wasm",setupFunc:cme,kernelFunc:fme},pS;function gme(e){pS=e.wasm.cwrap(Js,null,["number","number","number","number","number"])}function yme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Zi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[d[0]];return pS(o,Ut[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var Ame={kernelName:Js,backendName:"wasm",kernelFunc:yme,setupFunc:gme},hS;function xme(e){hS=e.wasm.cwrap(Qs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function bme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),v=n.dataIdMap.get(b.dataId).id;return hS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,v),b}var vme={kernelName:Qs,backendName:"wasm",setupFunc:xme,kernelFunc:bme};function rn(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var wme={kernelName:fl,backendName:"wasm",kernelFunc:rn},cS;function kme(e){cS=e.wasm.cwrap(ei,null,["number","array","number","number","array","number","number","number","number"])}function Ime(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=Rl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=rn({inputs:{x:a},backend:r,attrs:{shape:x}}),S=rn({inputs:{x:s},backend:r,attrs:{shape:b}}),T=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(S.dataId).id,R=i?v.shape[2]:v.shape[1],_=o?S.shape[1]:S.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],v.dtype),z=r.dataIdMap.get(I.dataId).id,O=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(S.shape).buffer);return cS(T,O,v.shape.length,E,j,S.shape.length,i,o,z),r.disposeData(v.dataId),r.disposeData(S.dataId),I.shape=A,I}var Sme={kernelName:ei,backendName:"wasm",setupFunc:kme,kernelFunc:Ime};function Vo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=Ot.parseSliceParams(t,r,n),o=Ot.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=Ot.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(i))),u}if(t.dtype==="string"){let f=D0(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)Tme(l,d[0],p,s,i);else if(c===3)Nme(l,d[0],d[1],p,s,i);else if(c===4)Cme(l,d[0],d[1],d[2],p,s,i);else{let f=D0(l,s,i,t.shape,t.dtype);p.set(f)}return u}function Tme(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function Nme(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function Cme(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Eme={kernelName:xl,backendName:"wasm",kernelFunc:Vo};function Rme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=rn({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Xs({inputs:{x:c},backend:r,attrs:{perm:u}}),m=rn({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Vo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var Mme={kernelName:Ho,backendName:"wasm",kernelFunc:Rme};function qh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var Fme={kernelName:ti,backendName:"wasm",kernelFunc:qh},$me=Ir(ri),fS;function Pme(e){fS=e.wasm.cwrap(es,null,["number","number","number","number"])}function _me(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o=r.dataIdMap.get(a.dataId).id,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(l.dataId).id;return fS(o,s,i,u),l}var zme={kernelName:es,backendName:"wasm",setupFunc:Pme,kernelFunc:_me};function mS(e){let{inputs:t,backend:r}=e,n=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=N.computeOutShape(t.map(c=>c.shape),n),s=t.filter(c=>w.sizeFromShape(c.shape)>0);if(s.length===1)return Em({inputs:{x:s[0]},backend:r});let i=r.makeOutput(a,t[0].dtype);if(w.sizeFromShape(a)===0)return i;let o=s.map(c=>c.shape);if(N.assertParamsConsistent(o,n),s[0].dtype==="string"){let c=s.map(x=>{let b=w.sizeFromShape(x.shape.slice(n));return rn({inputs:{x},backend:r,attrs:{shape:[-1,b]}})}),f=c.map(x=>({vals:r.readSync(x.dataId),shape:x.shape}));a=N.computeOutShape(c.map(x=>x.shape),1);let m=c[0].shape[0]===1,g=Jx(f,a,t[0].dtype,m),y=N.computeOutShape(s.map(x=>x.shape),n);i.shape=y;let A=r.dataIdMap.get(i.dataId);return A.stringBytes=N.fromStringArrayToUint8(g),c.forEach(x=>r.disposeData(x.dataId)),i}let l=w.sizeFromShape(s[0].shape.slice(0,n)),u=0,d=s.map(c=>{let f=w.sizeFromShape(c.shape.slice(n));return u+=f,f}),h=s.map(c=>r.typedArrayFromHeap(c)),p=r.typedArrayFromHeap(i);for(let c=0;c<l;c++){let f=c*u;for(let m=0;m<h.length;m++){let g=d[m],y=c*g,A=h[m].subarray(y,y+g);p.set(A,f),f+=g}}return i}var Ome={kernelName:qo,backendName:"wasm",kernelFunc:mS},gS;function Dme(e){gS=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Lme(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h,dataFormat:p}=r,c=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,S=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,R=f.inChannels,_=f.outChannels,M=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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Vme(e){let{backend:t,inputs:r,attrs:n}=e,{dy:a,filter:s}=r,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:d}=n,h=1,p=N.convertConv2DDataFormat(l),c=N.computeConv2DInfo(d,s.shape,i,h,o,u,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:v,outWidth:S,strideHeight:T,strideWidth:E}=c,R=m-1-c.padInfo.top,_=g-1-c.padInfo.left,M=c.dataFormat==="channelsLast",I=w.computeStrides(c.inShape),z=w.computeStrides(a.shape),[O,j,X]=w.computeStrides(s.shape),D=I[0],Q=M?I[1]:I[2],V=M?I[2]:1,ee=M?1:I[1],J=z[0],ie=M?z[1]:z[2],Z=M?z[2]:1,ae=M?1:z[1],de=t.makeOutput(c.inShape,"float32"),Ae=t.dataIdMap.get(de.dataId).id,be=t.dataIdMap.get(a.dataId).id,Ee=t.dataIdMap.get(s.dataId).id;return yS(be,Ee,f,m,g,A,x,y,v,S,b,T,E,R,_,O,j,X,D,Q,V,ee,J,ie,Z,ae,Ae),de}var Ume={kernelName:ai,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme},Gme=Ir(si),jme=Ir(ii),AS=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(AS||{}),xS;function Hme(e){xS=e.wasm.cwrap(Xo,null,["number","number","number","number","array","number","number","number","number","number"])}function qme(e){let{backend:t,inputs:r,attrs:n}=e,{method:a,extrapolationValue:s,cropSize:i}=n,{image:o,boxes:l,boxInd:u}=r,d=l.shape[0],[h,p]=i,c=[d,h,p,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=qh({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(u.dataId).id,x=t.makeOutput(c,"float32"),b=t.dataIdMap.get(x.dataId).id,v=new Uint8Array(new Int32Array(o.shape).buffer);return xS(g,y,A,d,v,h,p,AS[a],s,b),m!=null&&t.disposeData(m.dataId),x}var Kme={kernelName:Xo,backendName:"wasm",setupFunc:Hme,kernelFunc:qme},bS;function Xme(e){bS=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number"])}function Zme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;w.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumprod does not support ${a.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),d=a;u!==null&&(d=Xs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumprod",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],f=r.dataIdMap.get(d.dataId).id,m=r.dataIdMap.get(p.dataId).id;bS(f,i?1:0,o?1:0,c,m,Ut[a.dtype]);let g=p;if(u!==null){let y=N.getUndoAxesPermutation(u);g=Xs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var Yme={kernelName:Ko,backendName:"wasm",setupFunc:Xme,kernelFunc:Zme},vS;function Jme(e){vS=e.wasm.cwrap(oi,null,["number","number","number","number","number","number"])}function Qme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length;w.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=N.getAxesPermutation([s],l),d=a;u!==null&&(d=Xs({inputs:{x:a},attrs:{perm:u},backend:r}));let h=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[h],l);let p=r.makeOutput(d.shape,d.dtype),c=d.shape[h],f=r.dataIdMap.get(d.dataId).id,m=r.dataIdMap.get(p.dataId).id;vS(f,i?1:0,o?1:0,c,m,Ut[a.dtype]);let g=p;if(u!==null){let y=N.getUndoAxesPermutation(u);g=Xs({inputs:{x:p},attrs:{perm:y},backend:r}),r.disposeData(d.dataId),r.disposeData(p.dataId)}return g}var e1e={kernelName:oi,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme},wS;function t1e(e){wS=e.wasm.cwrap(Zo,null,["number","number","number","array","number","array","array","number","number"])}function r1e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return wS(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,f.length,b),m}var n1e={kernelName:Zo,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e},kS;function a1e(e){kS=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function s1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=N.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,v=c.dilationWidth,S=c.strideHeight,T=c.strideWidth,E=c.inChannels,R=c.outChannels,_=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. 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Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),J=n.dataIdMap.get(ee.dataId).id,ie=o==null?0:n.dataIdMap.get(o.dataId).id;return NS(y,D,Q,V,A,v,S,b,T,E,R,_,X,M,I,z,O,j,x,g,ie,f||0,J),ee}var I1e={kernelName:zs,backendName:"wasm",setupFunc:w1e,kernelFunc:k1e},CS;function S1e(e){CS=e.wasm.cwrap(Os,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function T1e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=Cm[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,S=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,z=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,v=d.inChannels,S=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. 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o2e={kernelName:ki,backendName:"wasm",setupFunc:s2e,kernelFunc:i2e},l2e=!1,u2e=Kr(Ii,l2e),zS=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(zS||{}),OS;function d2e(e){OS=e.wasm.cwrap(Si,null,["number","array","number","number","array","array","number","number"])}function p2e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return OS(i,u,t.shape.length,Ut[t.dtype],p,c,zS[a],l),o}var h2e={kernelName:Si,backendName:"wasm",kernelFunc:p2e,setupFunc:d2e},c2e=!0,f2e=Kr(Ti,c2e),m2e=Ir(ol);function E5(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var DS;function g2e(e){DS=e.wasm.cwrap(ul,"number",["number","number","number","number","number"])}function y2e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=DS(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=E5(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var A2e={kernelName:ul,backendName:"wasm",setupFunc:g2e,kernelFunc:y2e},LS;function x2e(e){LS=e.wasm.cwrap(td,"number",["number","number","number","number","number","bool"])}function b2e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=LS(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=E5(t,p);t.wasm._free(m);let 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$2e={kernelName:cl,backendName:"wasm",kernelFunc:F2e},VS;function P2e(e){VS=e.wasm.cwrap(Ni,null,["number","array","number","number","array","array","number","number"])}function _2e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(w.sizeFromShape(t.shape)===0)return IS({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return VS(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var US={kernelName:Ni,backendName:"wasm",kernelFunc:_2e,setupFunc:P2e},z2e=!1,O2e=Kr(Ci,z2e),GS;function D2e(e){GS=e.wasm.cwrap(Ei,null,["number","number","number"])}function 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yge(e){JS=e.wasm.cwrap(Li,null,["number","number","number","number"])}function Age(e){let{backend:t,inputs:{logits:r},attrs:{dim:n}}=e,a=t.dataIdMap.get(r.dataId).id,s=t.makeOutput(r.shape,r.dtype),i=t.dataIdMap.get(s.dataId).id,o=r.shape[n],l=w.sizeFromShape(r.shape)/o;return w.sizeFromShape(s.shape)===0||JS(a,i,o,l),s}var xge={kernelName:Li,backendName:"wasm",setupFunc:yge,kernelFunc:Age};function bge(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=w.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=US.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(d.length,s.length,!1),p=N.getReshapedPermuted(u.shape,s,o,!1),c=rn({inputs:{x:u},backend:r,attrs:{shape:d}}),f=Xs({inputs:{x:c},backend:r,attrs:{perm:h}}),m=rn({inputs:{x:f},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(f.dataId),m}var vge={kernelName:vl,backendName:"wasm",kernelFunc:bge},QS;function wge(e){QS=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function kge(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(d,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),v=t.dataIdMap.get(b.dataId).id,S=t.makeOutput([4],"int32"),T=t.dataIdMap.get(S.dataId).id,E=QS(h,p,Ut[a.dtype],o,u,l,c,m,y,x,v,T),R=t.readSync(S.dataId),_;switch(R[0]){case 1:{_=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 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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 cT=`
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];
}
`,fT=`
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;
}
`,mT=`
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);
}
`,gT=`
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;
}
`,yT=`
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;
}
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a=t.landmarks.length>=H5.count?H5.symmetryLine:Zh.symmetryLine,s=0,i=Z5,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=Uye(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=zm(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=UT(-s,u),o=X5(t,h,[n,n]),re(h)}else o=X5(t,r,[n,n]);else o=X5(t,r,[n,n]);return[s,i,o]}var qye=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},qT=(e,t)=>{let r=qye(e),n=_d(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var KT=6,Kye=1.4,La,XT=null,Ji=0,Jh=null,Lm=()=>Ji;async function ZT(e){var t;return he.initial&&(La=null),La?e.debug&&se("cached model:",La.modelUrl):La=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Ji=La.inputs[0].shape?La.inputs[0].shape[2]:0,Jh=Se(Ji,"int32"),XT=ca(GT(Ji)),La}function Xye(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,XT),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Jh),t.centersNormalized=pe(t.centers,Jh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Jh),t.endNormalized=L(t.ends,Jh);let r=pd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function YT(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Ji,Ji]),r.div=pe(r.resized,Qe.tf127),r.normalized=ce(r.div,Qe.tf05);let n=La==null?void 0:La.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=It([h[0],h[2]],2),r.concat512=It([h[1],h[3]],2),r.concat=It([r.concat512,r.concat384],1),r.batch=rt(r.concat,0)}else r.batch=rt(n);re(n),r.boxes=Xye(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Nr(r.logits),r.scores=rt(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],KT-1],[1,-1]),c.squeeze=rt(c.slice),c.landmarks=G(c.squeeze,[KT,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=WT(m,[(e.shape[2]||0)/Ji,(e.shape[1]||0)/Ji]),y=Om(g,t.face.scale||Kye),A=Dm(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var Bm={};ks(Bm,{connected:()=>t3,kpt:()=>e3});var e3=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],t3={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var QT=224,Zye,Yye=5,Wm=[8,16,32,32,32];async function eN(){let e=[],t=0;for(;t<Yye;){let r=0,n=t;for(;n<Wm.length&&Wm[n]===Wm[t];)r+=2,n++;let a=Wm[t],s=Math.ceil(QT/a),i=Math.ceil(QT/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}Zye={x:Tt(e.map(r=>r.x)),y:Tt(e.map(r=>r.y))}}function ds(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function tN(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function Vm(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var aN={initial:!0},yn={detector:null,landmarks:null},zd={detector:[224,224],landmarks:[256,256]},r3=Number.MAX_SAFE_INTEGER,Qye={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Gm=null,Qh,Qi=[[0,0],[0,0],[0,0],[0,0]],rN=0,nN=e=>1-1/(1+Math.exp(e));async function sN(e){if(aN.initial&&(yn.detector=null),!yn.detector&&e.body.detector&&e.body.detector.modelPath){yn.detector=await Ge(e.body.detector.modelPath);let t=Object.values(yn.detector.modelSignature.inputs);zd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,zd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&yn.detector&&se("cached model:",yn.detector.modelUrl);return await eN(),yn.detector}async function iN(e){if(aN.initial&&(yn.landmarks=null),yn.landmarks)e.debug&&se("cached model:",yn.landmarks.modelUrl);else{yn.landmarks=await Ge(e.body.modelPath);let t=Object.values(yn.landmarks.modelSignature.inputs);zd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,zd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return yn.landmarks}async function eAe(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Qh&&(r.cropped=Ie.cropAndResize(e,[Qh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[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],s=[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];Qi=[[0,0],a,s,[0,0]],r.pad=Kn(r.cropped||e,Qi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function tAe(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Qi[2][0]+Qi[2][1])/t[0]-Qi[2][0]),Math.trunc(r.position[1]*(t[1]+Qi[1][0]+Qi[1][1])/t[1]-Qi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Qh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Qh[1],r.positionRaw[1]+Qh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function rAe(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function nAe(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=yn.landmarks)==null?void 0:f.execute(e,Qye.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=nN(s[l*m+3]),y=nN(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/zd.landmarks[0],s[l*m+1]/zd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:e3[m],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;rAe(o);let u=tAe(o,r),d=u.map(m=>m.position),h=ds(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(t3)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function n3(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-rN,a=r3<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&Gm!==null)r3++;else{let s={};s.landmarks=await eAe(e,256),Gm=await nAe(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),rN=oe(),r3=0}return Gm?[Gm]:[]}var Od=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var ps,Ul=0,a3=[],lN=0,s3=Number.MAX_SAFE_INTEGER;async function uN(e){if(he.initial&&(ps=null),ps)e.debug&&se("cached model:",ps.modelUrl);else{ps=await Ge(e.object.modelPath);let t=Object.values(ps.modelSignature.inputs);Ul=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ps}async function aAe(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=rt(e);let i=Zt(n.squeeze,6,1);n.stack=ur([i[1],i[0],i[3],i[2]],1),n.boxes=rt(n.stack),n.scores=rt(i[4]),n.classes=rt(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Od[h].label,[c,f]=[s[0][u][0]/Ul,s[0][u][1]/Ul],m=[c,f,s[0][u][2]/Ul-c,s[0][u][3]/Ul-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function i3(e,t){let r=(t.object.skipTime||0)>oe()-lN,n=s3<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&a3.length>0?(s3++,a3):(s3=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ul,Ul]),o=t.object.enabled?ps==null?void 0:ps.execute(i,["tower_0/detections"]):null;lN=oe(),re(i);let l=await aAe(o,s,t);a3=l,a(l)}))}var jm={};ks(jm,{connected:()=>l3,kpt:()=>o3});var o3=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],l3={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mr,pN=0,Zr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},u3=Number.MAX_SAFE_INTEGER;async function hN(e){return he.initial&&(Mr=null),Mr?e.debug&&se("cached model:",Mr.modelUrl):Mr=await Ge(e.body.modelPath),Mr}async function sAe(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=yr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Rn(a,0),l=fd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function d3(e,t){let r=(t.body.skipTime||0)>oe()-pN,n=u3<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Zr.keypoints).length>0?(u3++,[Zr]):(u3=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Mr!=null&&Mr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Mr.inputs[0].shape[2],Mr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return ce(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Mr==null?void 0:Mr.execute(s)),pN=oe(),re(s),i){Zr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await sAe(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Zr.keypoints.push({score:Math.round(100*y)/100,part:o3[f],positionRaw:[m/Mr.inputs[0].shape[2],g/Mr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Mr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}Zr.score=Zr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=Zr.keypoints.map(p=>p.position[0]),l=Zr.keypoints.map(p=>p.position[1]);Zr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Zr.keypoints.map(p=>p.positionRaw[0]),d=Zr.keypoints.map(p=>p.positionRaw[1]);Zr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(l3)){let f=[];for(let m=0;m<c.length-1;m++){let g=Zr.keypoints.find(A=>A.part===c[m]),y=Zr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}Zr.annotations[p]=f}a([Zr])}))}var iAe=["angry","disgust","fear","happy","sad","surprise","neutral"],Bn,Hm=[],fN=0,mN=0,p3=Number.MAX_SAFE_INTEGER;async function gN(e){var t;return he.initial&&(Bn=null),Bn?e.debug&&se("cached model:",Bn.modelUrl):Bn=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),Bn}async function h3(e,t,r,n){var i,o;if(!Bn)return[];let a=p3<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-mN;return t.skipAllowed&&s&&a&&fN===n&&Hm[r]&&Hm[r].length>0?(p3++,Hm[r]):(p3=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=Bn!=null&&Bn.inputs[0].shape?Bn.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=ce(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=Bn==null?void 0:Bn.execute(p.grayscaleMul),mN=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:iAe[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}Hm[r]=u,fN=n,l(u)}))}var An,c3=[],AN=0,xN=0,bN=Number.MAX_SAFE_INTEGER;async function vN(e){return he.initial&&(An=null),An?e.debug&&se("cached model:",An.modelUrl):An=await Ge(e.face.mobilefacenet.modelPath),An}async function f3(e,t,r,n){var i,o;if(!An)return[];let a=bN<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-xN;return t.skipAllowed&&s&&a&&AN===n&&c3[r]?(bN++,c3[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(An==null?void 0:An.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[An.inputs[0].shape[2],An.inputs[0].shape[1]],!1),h.data=An==null?void 0:An.execute(h.crop);let p=await h.data.data();u=Array.from(p)}c3[r]=u,AN=n,xN=oe(),l(u)})}var hs,eo=0,oAe=2.3,m3=ea.leftEyeLower0,g3=ea.rightEyeLower0,Dd={leftBounds:[m3[0],m3[m3.length-1]],rightBounds:[g3[0],g3[g3.length-1]]},Ld={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function TN(e){var t;return he.initial&&(hs=null),hs?e.debug&&se("cached model:",hs.modelUrl):hs=await Ge((t=e.face.iris)==null?void 0:t.modelPath),eo=hs.inputs[0].shape?hs.inputs[0].shape[2]:0,eo===-1&&(eo=64),hs}function qm(e,t,r,n){for(let a=0;a<q5.length;a++){let{key:s,indices:i}=q5[a],o=ea[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var lAe=e=>{let t=e[Dd.leftBounds[0]][2],r=e[Dd.rightBounds[0]][2];return t-r},kN=(e,t,r,n,a,s=!1)=>{let i=Dm(Om(VT([e[r],e[n]]),oAe)),o=_d(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[eo,eo]);if(s&&he.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},IN=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Ld.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/eo:i/eo)*r[0]+t.startPoint[0],o/eo*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Ld.index)}},SN=(e,t,r)=>{let n=e[ea[`${r}EyeUpper0`][Ld.upperCenter]][2],a=e[ea[`${r}EyeLower0`][Ld.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function NN(e,t,r,n){if(!hs)return r.debug&&se("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=kN(e,t,Dd.leftBounds[0],Dd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=kN(e,t,Dd.rightBounds[0],Dd.rightBounds[1],n,!0),d=It([i,u]);re(i),re(u);let h=hs.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Ld.numCoordinates*3),{rawCoords:f,iris:m}=IN(c,a,s,!0),g=p.slice(Ld.numCoordinates*3),{rawCoords:y,iris:A}=IN(g,o,l,!1),x=lAe(e);Math.abs(x)<30?(qm(e,f,"left",null),qm(e,y,"right",null)):x<1?qm(e,f,"left",["EyeUpper0","EyeLower0"]):qm(e,y,"right",["EyeUpper0","EyeLower0"]);let b=SN(e,m,"left"),v=SN(e,A,"right");return e.concat(b).concat(v)}var xn={eyeLLower:[33,7,163,144,145,153,154,155,133],eyeRLower:[263,249,390,373,374,380,381,382,362],lips:[185,96,90,181,84,17,314,405,320,307,409,40,39,73,37,0,267,269,270,409,40,88,178,178,87,14,268,402,318,324,409,80,41,38,87,12,268,303,318,324,185,95,80,81,85,16,315,404,319,325,409,40,39,73,72,0,302,303,270,408,185,88,88,81,82,15,316,403,319,324,409,80,41,38,87,12,268,303,318,324],eyeL:[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],eyeR:[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417]};async function EN(e,t){let r={irisL:t[3].dataSync(),irisR:t[1].dataSync(),eyeL:t[0].dataSync(),eyeR:t[6].dataSync(),lips:t[5].dataSync()},n=xn.eyeRLower.reduce((s,i)=>s+=e[i][2],0)/xn.eyeRLower.length;for(let s=0;s<r.irisR.length/2;s++)e.push([r.irisR[2*s+0],r.irisR[2*s+1],n]);let a=xn.eyeLLower.reduce((s,i)=>s+=e[i][2],0)/xn.eyeLLower.length;for(let s=0;s<r.irisL.length/2;s++)e.push([r.irisL[2*s+0],r.irisL[2*s+1],a]);for(let s=0;s<r.eyeL.length/2;s++)e[xn.eyeL[s]]=[r.eyeL[2*s+0],r.eyeL[2*s+1],e[xn.eyeL[s]][2]];for(let s=0;s<r.eyeR.length/2;s++)e[xn.eyeR[s]]=[r.eyeR[2*s+0],r.eyeR[2*s+1],e[xn.eyeR[s]][2]];for(let s=0;s<r.lips.length/2;s++)e[xn.lips[s]]=[r.lips[2*s+0],r.lips[2*s+1],e[xn.lips[s]][2]];return e}var Ba={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Wa=null,Bd=0;async function RN(e,t){var o,l,u,d,h,p,c,f,m,g;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Ba.timestamp,n=Ba.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||Ba.boxes.length===0?(Ba.boxes=await YT(e,t),Ba.timestamp=oe(),Ba.skipped=0):Ba.skipped++;let a=[],s=[],i=0;for(let y=0;y<Ba.boxes.length;y++){let A=Ba.boxes[y],x=0,b,v={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([x,b,v.tensor]=HT((u=t.face.detector)==null?void 0:u.rotation,A,e,(d=t.face.mesh)!=null&&d.enabled?Bd:Lm()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let S=await Rm(v.tensor);re(v.tensor),v.tensor=S}if(v.boxScore=Math.round(100*A.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!Wa)t.debug&&se("face mesh detection requested, but model is not loaded");else{let S=Wa.execute(v.tensor),T=S.find(I=>I.shape[I.shape.length-1]===1),E=S.find(I=>I.shape[I.shape.length-1]===1404),R=await T.data();v.faceScore=Math.round(100*R[0])/100;let _=G(E,[-1,3]),M=await _.array();if(v.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))A.confidence=v.faceScore;else{(f=t.face.attention)!=null&&f.enabled?M=await EN(M,S):(m=t.face.iris)!=null&&m.enabled&&(M=await NN(M,v.tensor,t,Bd)),v.mesh=jT(M,A,x,b,Bd),v.meshRaw=v.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/Bd]);for(let z of Object.keys(ea))v.annotations[z]=ea[z].map(O=>v.mesh[O]);v.score=v.faceScore;let I={...qT(v.mesh,A),confidence:A.confidence,landmarks:A.landmarks};v.box=Y5(I,e),v.boxRaw=J5(I,e),s.push(I)}re([...S,_])}else{v.box=Y5(A,e),v.boxRaw=J5(A,e),v.score=v.boxScore,v.mesh=A.landmarks.map(S=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*S[0]/Lm(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*S[1]/Lm()]),v.meshRaw=v.mesh.map(S=>[S[0]/(e.shape[2]||0),S[1]/(e.shape[1]||0),(S[2]||0)/Bd]);for(let S of Object.keys(Zh))v.annotations[S]=[v.mesh[Zh[S]]]}v.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?a.push(v):re(v.tensor)}return Ba.boxes=s,a}async function MN(e){var t,r,n;return he.initial&&(Wa=null),Wa?e.debug&&se("cached model:",Wa.modelUrl):(t=e.face.attention)!=null&&t.enabled?Wa=await Ge((r=e.face.attention)==null?void 0:r.modelPath):Wa=await Ge((n=e.face.mesh)==null?void 0:n.modelPath),Bd=Wa.inputs[0].shape?Wa.inputs[0].shape[2]:0,Wa}var FN=Wl,$N=Yh;var bn,Km=[],PN=0,_N=0,A3=Number.MAX_SAFE_INTEGER;async function zN(e){var t;return he.initial&&(bn=null),bn?e.debug&&se("cached model:",bn.modelUrl):bn=await Ge((t=e.face.description)==null?void 0:t.modelPath),bn}function x3(e){let t=e.image||e.tensor||e;if(!(bn!=null&&bn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[bn.inputs[0].shape[2],bn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function b3(e,t,r,n){var i,o,l,u;if(!bn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=A3<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-PN;return t.skipAllowed&&a&&s&&_N===n&&((l=Km[r])==null?void 0:l.age)&&((u=Km[r])==null?void 0:u.age)>0?(A3++,Km[r]):(A3=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let f=x3(e),m=bn==null?void 0:bn.execute(f);PN=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Rn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let S=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(S[b-1]>S[b+1]?10*b-100*S[b-1]:10*b+100*S[b+1])/10;let T=m.find(R=>R.shape[1]===1024),E=T?await T.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}Km[r]=h,_N=n,d(h)}))}function Xm(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=ce(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=rt(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Nr(n.slice),n.scores=rt(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=BN(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var fAe=5,jN=1.65,HN=[0,5,9,13,17,1,2],mAe=0,gAe=2,qN=0,Qm=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>k3([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return Zm(Ym(a),fAe)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=Zm(Ym(r),jN);n.palmLandmarks=[];for(let a=0;a<HN.length;a++)n.palmLandmarks.push(t[HN[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=Xm(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=w3(n,[0,0]),u=o.map(c=>[...k3(c,l),c[2]]),d=VN(a),h=[...ec(r),1],p=[to(h,d[0]),to(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-qN,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?WN(u.palmLandmarks[mAe],u.palmLandmarks[gAe]):0,h=ec(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&he.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=w3(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=LN(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);qN=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),S=await v.array();re(x),re(v);let T=this.transformRawCoords(S,m,d,f),E=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=Zm(Ym(u),jN),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Yr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Yr.nameMapping[e],getPoints:e=>Yr.pointsMapping[e]},no={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>no.nameMapping[e]},Bt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Bt.nameMapping[e]},ro=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:wa,index:cs,middle:fs,ring:Gl,pinky:jl}=Yr,{none:ka,half:AAe,full:Ia}=no,{verticalUp:Wd,verticalDown:x6e,horizontalLeft:I3,horizontalRight:xAe,diagonalUpRight:bAe,diagonalUpLeft:Vd,diagonalDownRight:b6e,diagonalDownLeft:v6e}=Bt,ao=new ro("thumbs up");ao.curl(wa,ka,1);ao.direction(wa,Wd,1);ao.direction(wa,Vd,.25);ao.direction(wa,bAe,.25);for(let e of[Yr.index,Yr.middle,Yr.ring,Yr.pinky])ao.curl(e,Ia,1),ao.direction(e,I3,1),ao.direction(e,xAe,1);var er=new ro("victory");er.curl(wa,AAe,.5);er.curl(wa,ka,.5);er.direction(wa,Wd,1);er.direction(wa,Vd,1);er.curl(cs,ka,1);er.direction(cs,Wd,.75);er.direction(cs,Vd,1);er.curl(fs,ka,1);er.direction(fs,Wd,1);er.direction(fs,Vd,.75);er.curl(Gl,Ia,1);er.direction(Gl,Wd,.2);er.direction(Gl,Vd,1);er.direction(Gl,I3,.2);er.curl(jl,Ia,1);er.direction(jl,Wd,.2);er.direction(jl,Vd,1);er.direction(jl,I3,.2);er.weight(cs,2);er.weight(fs,2);var so=new ro("point");so.curl(wa,Ia,1);so.curl(cs,ka,.5);so.curl(fs,Ia,.5);so.curl(Gl,Ia,.5);so.curl(jl,Ia,.5);so.weight(cs,2);so.weight(fs,2);var io=new ro("middle finger");io.curl(wa,ka,1);io.curl(cs,Ia,.5);io.curl(fs,Ia,.5);io.curl(Gl,Ia,.5);io.curl(jl,Ia,.5);io.weight(cs,2);io.weight(fs,2);var Ud=new ro("open palm");Ud.curl(wa,ka,.75);Ud.curl(cs,ka,.75);Ud.curl(fs,ka,.75);Ud.curl(Gl,ka,.75);Ud.curl(jl,ka,.75);var KN=[ao,er,so,io,Ud];var vAe=.7,Hl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function XN(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function YN(e,t){if(!e||!t)return[0,0];let r=XN(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=XN(e[1],e[2],t[1],t[2]);return[r,n]}function ZN(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function wAe(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Hl.NO_CURL_START_LIMIT?y=no.none:g>Hl.HALF_CURL_START_LIMIT?y=no.half:y=no.full,y}function JN(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:n===Math.abs(t)?t>0?a=Bt.horizontalLeft:a=Bt.horizontalRight:r>0?a=Bt.horizontalLeft:a=Bt.horizontalRight,a}function QN(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Bt.verticalDown:a=Bt.verticalUp:n===Math.abs(t)?t<0?a=Bt.verticalDown:a=Bt.verticalUp:r<0?a=Bt.verticalDown:a=Bt.verticalUp,a}function kAe(e,t,r,n,a,s,i,o){let l,u=QN(e,t,r,n),d=JN(a,s,i,o);return u===Bt.verticalUp?d===Bt.horizontalLeft?l=Bt.diagonalUpLeft:l=Bt.diagonalUpRight:d===Bt.horizontalLeft?l=Bt.diagonalDownLeft:l=Bt.diagonalDownRight,l}function IAe(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=Hl.DISTANCE_VOTE_POWER:m>.66?c+=Hl.DISTANCE_VOTE_POWER:f+=Hl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],S=r[0],T=r[1];x===g?(S=r[0],T=r[1]):x===A&&(b=t[0],v=t[1]);let _=YN([b,v],[S,T]),M=ZN(_,Hl.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let z of n){let O=ZN(z,Hl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],c+=O[1],f+=O[2]}let I;return p===Math.max(p,c,f)?I=QN(l,o,u,h):f===Math.max(c,f)?I=JN(s,a,i,d):I=kAe(l,o,u,h,s,a,i,d),I}function eC(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Yr.all){let i=Yr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=YN(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Yr.all){let i=s===Yr.thumb?1:0,o=Yr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=wAe(l,u,d),p=IAe(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function e1(e){if(!e||e.length===0)return null;let t=eC(e),r={};for(let n of Yr.all)r[Yr.getName(n)]={curl:no.getName(t.curls[n]),direction:Bt.getName(t.directions[n])};return r}function tC(e){let t=[];if(!e||e.length===0)return t;let r=eC(e);for(let n of KN){let a=n.matchAgainst(r.curls,r.directions);a>=vAe&&t.push({name:n.name,confidence:a})}return t}var rC={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]},Gd,jd,nC;async function T3(e,t){let r=await nC.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(rC))s[d]=rC[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=e1(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function N3(e){var r,n;he.initial&&(Gd=null,jd=null),!Gd||!jd?[Gd,jd]=await Promise.all([e.hand.enabled?Ge((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ge((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&se("cached model:",Gd.modelUrl),e.debug&&se("cached model:",jd.modelUrl));let t=new Jm(Gd);return nC=new Qm(t,jd),[Gd,jd]}var pr=[null,null],SAe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],oo=[[0,0],[0,0]],TAe=["hand","fist","pinch","point","face","tip","pinchtip"],sC=4,iC=1.6,NAe=512,CAe=1.4,t1=Number.MAX_SAFE_INTEGER,C3=0,ms=[0,0],Ht={boxes:[],hands:[]},oC={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],base:[0],palm:[0,17,13,9,5,1,0]};async function lC(e){var t;if(he.initial&&(pr[0]=null),pr[0])e.debug&&se("cached model:",pr[0].modelUrl);else{r1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),pr[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(pr[0].modelSignature.inputs);oo[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,oo[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[0]}async function uC(e){var t;if(he.initial&&(pr[1]=null),pr[1])e.debug&&se("cached model:",pr[1].modelUrl);else{pr[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(pr[1].modelSignature.inputs);oo[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,oo[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return pr[1]}async function EAe(e,t){let r=[];if(!e||!pr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,NAe),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await pr[0].executeAsync(n.cast,SAe),n.boxes=rt(n.rawBoxes,[0,2]),n.scores=rt(n.rawScores,[0]);let o=nn(n.scores,1);re(o[sC]),o.splice(sC,1),n.filtered=ur(o,1),re(o),n.max=yr(n.filtered,1),n.argmax=Rn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=Vm(m,CAe),y=[Math.trunc(m[0]*ms[0]),Math.trunc(m[1]*ms[1]),Math.trunc(m[2]*ms[0]),Math.trunc(m[3]*ms[1])],A=d[p],x=TAe[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function E3(e,t,r){let n={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&&pr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[oo[1][0],oo[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=pr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/oo[1][1],h[1]/oo[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[ms[0]*(h[0]+t.boxRaw[0]),ms[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=e1(n.keypoints);for(let h of Object.keys(oC))n.annotations[h]=oC[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function R3(e,t){var a,s;if(!pr[0]||!pr[1]||!((a=pr[0])!=null&&a.inputs[0].shape)||!((s=pr[1])!=null&&s.inputs[0].shape))return[];ms=[e.shape[2]||0,e.shape[1]||0],t1++;let r=(t.hand.skipTime||0)>oe()-C3,n=t1<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Ht.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-C3,l=t1<3*(t.hand.skipFrames||0);t.skipAllowed&&Ht.hands.length===t.hand.maxDetected?Ht.hands=await Promise.all(Ht.boxes.map(d=>E3(e,d,t))):t.skipAllowed&&o&&l&&Ht.hands.length>0?Ht.hands=await Promise.all(Ht.boxes.map(d=>E3(e,d,t))):(Ht.boxes=await EAe(e,t),C3=oe(),Ht.hands=await Promise.all(Ht.boxes.map(d=>E3(e,d,t))),t1=0);let u=[...Ht.boxes];if(Ht.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Ht.hands.length;d++){let h=tN(Ht.hands[d].keypoints,ms);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Ht.hands[d].fingerScore&&Ht.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=Vm(h.box,iC),c=Vm(h.boxRaw,iC);Ht.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Ht.hands.length;d++){let h=ds(Ht.hands[d].keypoints,ms);Ht.hands[d].box=h.box,Ht.hands[d].boxRaw=h.boxRaw}i(Ht.hands)})}var Fr,n1=[],M3=Number.MAX_SAFE_INTEGER,pC=0,hC=0;async function cC(e){var t;return he.initial&&(Fr=null),Fr?e.debug&&se("cached model:",Fr.modelUrl):Fr=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),Fr}async function F3(e,t,r,n){var i,o;if(!Fr)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-hC,s=M3<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&pC===n&&n1[r]?(M3++,n1[r]):(M3=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Fr!=null&&Fr.inputs[0].shape?Fr.inputs[0].shape[2]:0,Fr!=null&&Fr.inputs[0].shape?Fr.inputs[0].shape[1]:0],!1),d=Fr==null?void 0:Fr.execute(u),h=(await d.data())[0];n1[r]=Math.round(100*h)/100,pC=n,hC=oe(),re([u,d]),l(n1[r])}))}var tc={};ks(tc,{connected:()=>s1,horizontal:()=>$3,kpt:()=>a1,relative:()=>_3,vertical:()=>P3});var a1=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],$3=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],P3=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],_3=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],s1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var mC=.005,vn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function z3(e){for(let t of $3){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of P3){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of _3){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function gC(e){for(let t=0;t<e.length;t++)if(e[t]&&vn.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-vn.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-vn.keypoints[t].positionRaw[1])];r[0]<mC&&r[1]<mC?e[t]=vn.keypoints[t]:vn.keypoints[t]=e[t]}else vn.keypoints[t]=e[t];return e}function yC(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;vn.padding=[[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]],r.pad=Kn(e,vn.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function AC(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+vn.padding[2][0]+vn.padding[2][1])/t[0]-vn.padding[2][0],n.position[1]*(t[1]+vn.padding[1][0]+vn.padding[1][1])/t[1]-vn.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=ds(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var wn,i1=0,O3=Number.MAX_SAFE_INTEGER,ql={boxes:[],bodies:[],last:0};async function xC(e){return he.initial&&(wn=null),wn?e.debug&&se("cached model:",wn.modelUrl):(r1(["size"],e),wn=await Ge(e.body.modelPath)),i1=wn.inputs[0].shape?wn.inputs[0].shape[2]:0,i1<64&&(i1=256),wn}async function MAe(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:a1[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=ds(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(s1)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return z3(u),i.push(u),i}async function FAe(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:a1[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ds(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(s1)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};z3(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function D3(e,t){if(!wn||!(wn!=null&&wn.inputs[0].shape))return[];t.skipAllowed||(ql.boxes.length=0),O3++;let r=(t.body.skipTime||0)>oe()-ql.last,n=O3<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?ql.bodies:new Promise(async a=>{let s={};O3=0,s.input=yC(e,i1),s.res=wn==null?void 0:wn.execute(s.input),ql.last=oe();let i=await s.res.array();ql.bodies=s.res.shape[2]===17?await MAe(i,t,e):await FAe(i,t,e);for(let o of ql.bodies)AC(o,[e.shape[2]||1,e.shape[1]||1]),gC(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(ql.bodies)})}var Hd,o1=[],vC=0,L3=Number.MAX_SAFE_INTEGER,u1=0,l1=2.5;async function wC(e){if(!Hd||he.initial){Hd=await Ge(e.object.modelPath);let t=Object.values(Hd.modelSignature.inputs);u1=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&se("cached model:",Hd.modelUrl);return Hd}async function $Ae(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=rt(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===Od.length)),h=rt(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<Od.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/u1)),[v,S]=[A-l1/l*b[0],x-l1/l*b[1]],[T,E]=[A+l1/l*b[2]-v,x+l1/l*b[3]-S],R=[v,S,T,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:Od[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function B3(e,t){let r=(t.object.skipTime||0)>oe()-vC,n=L3<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&o1.length>0?(L3++,o1):(L3=0,!he.kernels.includes("mod")||!he.kernels.includes("sparsetodense")?o1:new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[u1,u1],!1),o=pe(i,Qe.tf255),l=o.transpose([0,3,1,2]);re(o),re(i);let u;t.object.enabled&&(u=Hd.execute(l)),vC=oe(),re(l);let d=await $Ae(u,s,t);o1=d,a(d)}))}var nc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],PAe=nc.length,rc=nc.reduce((e,t,r)=>(e[t]=r,e),{}),_Ae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],j6e=_Ae.map(([e,t])=>[rc[e],rc[t]]),IC=[["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 SC(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{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 TC(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var d1=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=r}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let r=2*t;if(r<this.numberOfElements&&this.less(r,r+1)&&r++,!this.less(t,r))break;this.exchange(t,r),t=r}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,r){return this.getValueAt(t)<this.getValueAt(r)}exchange(t,r){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[r],this.priorityQueue[r]=n}};function W3(e,t,r,n){return{y:n.get(e,t,r),x:n.get(e,t,r+PAe)}}function V3(e,t,r){let{heatmapY:n,heatmapX:a,id:s}=e,{y:i,x:o}=W3(n,a,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function U3(e,t,r){return e<t?t:e>r?r:e}function NC(e,t,r,n){let a=r-e,s=n-t;return a*a+s*s}function G3(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Sa,OAe=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],p1=1,qd=16,DAe=50**2;function CC(e,t,r,n,a,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),l=(y,A,x)=>({y:U3(Math.round(y.y/qd),0,A-1),x:U3(Math.round(y.x/qd),0,x-1)}),[u,d]=n.shape,h=l(t.position,u,d),p=o(h),f=G3(t.position,p);for(let y=0;y<i;y++){let A=l(f,u,d),x=W3(A.y,A.x,r,a);f=G3({x:A.x*qd,y:A.y*qd},{x:x.x,y:x.y})}let m=l(f,u,d),g=n.get(m.y,m.x,r);return{position:f,part:nc[r],score:g}}function LAe(e,t,r,n,a){let 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r=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],r,n,0,0,2*Math.PI),t.stroke(),dt.fillPolygons&&(t.fillStyle=dt.useDepth?"rgba(255, 255, 200, 0.3)":dt.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=dt.useDepth?"rgba(255, 200, 255, 0.3)":dt.color,t.beginPath();let r=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],r,n,0,0,2*Math.PI),t.stroke(),dt.fillPolygons&&(t.fillStyle=dt.useDepth?"rgba(255, 255, 200, 0.3)":dt.color,t.fill())}}function XAe(e,t){var r;if(dt.drawGaze&&((r=e.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Kl(e.rotation.angle.yaw)/90,a=e.box[1]+e.box[3]/2+e.box[2]*Kl(e.rotation.angle.pitch)/90,s=new Path2D(`
M ${e.box[0]+e.box[2]/2} ${e.box[1]}
C
${n} ${e.box[1]},
${n} ${e.box[1]+e.box[3]},
${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
`),i=new Path2D(`
M ${e.box[0]} ${e.box[1]+e.box[3]/2}
C
${e.box[0]} ${a},
${e.box[0]+e.box[2]} ${a},
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`);t.stroke(i),t.stroke(s)}}function ZAe(e,t){var r,n,a,s;if(dt.drawGaze&&((n=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:n.strength)&&((s=(a=e.rotation)==null?void 0:a.gaze)==null?void 0:s.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let i=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[i[0],i[1]],4);let o=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Y3(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[o[0],o[1]],4)}}function YAe(e,t){if(dt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let r=0;r<Wl.length/3;r++){let n=[Wl[r*3+0],Wl[r*3+1],Wl[r*3+2]].map(a=>e.mesh[a]);Z3(t,n,dt)}KAe(e,t)}}function JAe(e,t){if(dt.drawPoints&&e.mesh.length>=468)for(let r=0;r<e.mesh.length;r++)ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2],dt),dt.drawAttention&&(xn.lips.includes(r)&&ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]+127,dt),xn.eyeL.includes(r)&&ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt),xn.eyeR.includes(r)&&ys(t,e.mesh[r][0],e.mesh[r][1],e.mesh[r][2]-127,dt))}function QAe(e,t){dt.drawBoxes&&Ua(t,e.box[0],e.box[1],e.box[2],e.box[3],dt)}async function Kd(e,t,r){if(dt=Gt($r,r),!t||!e)return;let n=Wn(e);if(!!n){n.font=dt.font,n.strokeStyle=dt.color,n.fillStyle=dt.color;for(let a of t)QAe(a,n),qAe(a,n),a.mesh&&a.mesh.length>0&&(JAe(a,n),YAe(a,n),XAe(a,n),ZAe(a,n))}}async function Xd(e,t,r){var s;let n=Gt($r,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Ua(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=gs(t[i].keypoints[o].position[2],n),ys(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=gs(o.position[2],n),a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)zC(a,l,n)}}}async function Zd(e,t,r){let n=Gt($r,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Ua(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=gs(i[2],n),ys(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||-256;a.fillStyle=gs(u,n),a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=gs(l*u,n),a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function Yd(e,t,r){let n=Gt($r,r);if(!t||!e)return;let a=Wn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Ua(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} 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ea.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Qd&&Qd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Qd:i.x-Qd,y:i.y>.5?i.y+Qd:i.y-Qd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)exe(i/t,o/t,a)||(n.set(nb*n.get(0,o,i,0),0,o,i,0),n.set(nb*n.get(0,o,i,1),0,o,i,1),n.set(nb*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var rxe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(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],o=a?[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=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},DC=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,v,S,T,E]=m,R,_,M;return x<1?x>-1?(M=Math.asin(x),_=Math.atan2(-S,g),R=Math.atan2(-v,b)):(M=-Math.PI/2,_=-Math.atan2(T,E),R=0):(M=Math.PI/2,_=Math.atan2(T,E),R=0),isNaN(R)&&(R=0),isNaN(_)&&(_=0),isNaN(M)&&(M=0),{pitch:2*-R,yaw:2*-_,roll:2*-M}},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 o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(m=>[m[0]*t[0]/o,m[1]*t[1]/o,m[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),f=i.length===478?rxe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:f}};var ab=async(e,t)=>{var c,f,m,g,y,A,x,b,v,S,T,E,R,_,M,I,z,O,j,X,D,Q;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await RN(t,e.config);if(e.performance.face=he.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){se("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await OC(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?DC(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(f=e.config.face.emotion)!=null&&f.enabled?h3(p[V].tensor||ft([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(m=e.config.face.emotion)!=null&&m.enabled?await h3(p[V].tensor||ft([]),e.config,V,p.length):[],e.performance.emotion=he.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?j5(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await j5(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.antispoof=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?F3(p[V].tensor||ft([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await F3(p[V].tensor||ft([]),e.config,V,p.length):0,e.performance.liveness=he.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?D5(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(v=e.config.face.gear)!=null&&v.enabled?await D5(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(S=e.config.face.ssrnet)!=null&&S.enabled?B5(p[V].tensor||ft([]),e.config,V,p.length):null,s=(T=e.config.face.ssrnet)!=null&&T.enabled?U5(p[V].tensor||ft([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await B5(p[V].tensor||ft([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await U5(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(_=e.config.face.mobilefacenet)!=null&&_.enabled?f3(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=(M=e.config.face.mobilefacenet)!=null&&M.enabled?await f3(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?b3(p[V].tensor||ft([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(z=e.config.face.description)!=null&&z.enabled?await b3(p[V].tensor||ft([]),e.config,V,p.length):null,e.performance.description=he.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let J=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ie=(Q=e.config.face.detector)!=null&&Q.return?rt(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),J&&J!==0&&(Z.iris=Math.trunc(500/J/11.7)/100),ee&&(Z.rotation=ee),ie&&(Z.tensor=ie),h.push(Z),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),h};var LC=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},BC=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},WC=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},VC=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=tC(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ce={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},sb=0;function UC(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,S,T,E,R,_,M,I,z,O,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ce.canvas=e.canvas),e.error&&(Ce.error=e.error),!Ce.body||e.body.length!==Ce.body.length)Ce.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ce.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ce.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var de,Ae,be,Ee,Me,De,Be,Ze,ot;return{score:Z.score,part:Z.part,position:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[0]||0)+(Z.position[0]||0))/a:Z.position[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[1]||0)+(Z.position[1]||0))/a:Z.position[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[2]||0)+(Z.position[2]||0))/a:Z.position[2]],positionRaw:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/a:Z.positionRaw[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/a:Z.positionRaw[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/a:Z.positionRaw[2]],distance:[Ce.body[D].keypoints[ae]?((a-1)*(((de=Ce.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Z.distance)==null?void 0:Ae[0])||0))/a:(be=Z.distance)==null?void 0:be[0],Ce.body[D].keypoints[ae]?((a-1)*(((Ee=Ce.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+(((Me=Z.distance)==null?void 0:Me[1])||0))/a:(De=Z.distance)==null?void 0:De[1],Ce.body[D].keypoints[ae]?((a-1)*(((Be=Ce.body[D].keypoints[ae].distance)==null?void 0:Be[2])||0)+(((Ze=Z.distance)==null?void 0:Ze[2])||0))/a:(ot=Z.distance)==null?void 0:ot[2]]}}),J={},ie={connected:{}};(o=(i=t.body)==null?void 0:i.modelPath)!=null&&o.includes("efficientpose")?ie=jm:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?ie=Bm:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(ie=tc);for(let[Z,ae]of Object.entries(ie.connected)){let de=[];for(let Ae=0;Ae<ae.length-1;Ae++){let be=ee.find(Me=>Me.part===ae[Ae]),Ee=ee.find(Me=>Me.part===ae[Ae+1]);be&&Ee&&de.push([be.position,Ee.position])}J[Z]=de}Ce.body[D]={...e.body[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.hand||e.hand.length!==Ce.hand.length)Ce.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let Q=e.hand[D].box.map((ie,Z)=>((a-1)*Ce.hand[D].box[Z]+ie)/a),V=e.hand[D].boxRaw.map((ie,Z)=>((a-1)*Ce.hand[D].boxRaw[Z]+ie)/a);Ce.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ce.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((ie,Z)=>ie.map((ae,de)=>((a-1)*(Ce.hand[D].keypoints[Z][de]||1)+(ae||0))/a)):[],J={};if(Object.keys(Ce.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ce.hand[D].annotations=e.hand[D].annotations,J=Ce.hand[D].annotations;else if(e.hand[D].annotations)for(let ie of Object.keys(e.hand[D].annotations))J[ie]=e.hand[D].annotations[ie]&&e.hand[D].annotations[ie][0]?e.hand[D].annotations[ie].map((Z,ae)=>Z.map((de,Ae)=>((a-1)*Ce.hand[D].annotations[ie][ae][Ae]+de)/a)):null;Ce.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.face||e.face.length!==Ce.face.length)Ce.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Q=e.face[D].box.map((ee,J)=>((a-1)*Ce.face[D].box[J]+ee)/a),V=e.face[D].boxRaw.map((ee,J)=>((a-1)*Ce.face[D].boxRaw[J]+ee)/a);if(e.face[D].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,ee.angle={roll:((a-1)*(((f=(c=Ce.face[D].rotation)==null?void 0:c.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[D].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ce.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((S=(v=Ce.face[D].rotation)==null?void 0:v.angle)==null?void 0:S.pitch)||0)+(((E=(T=e.face[D].rotation)==null?void 0:T.angle)==null?void 0:E.pitch)||0))/a},ee.gaze={bearing:((a-1)*(((_=(R=Ce.face[D].rotation)==null?void 0:R.gaze)==null?void 0:_.bearing)||0)+(((I=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:I.bearing)||0))/a,strength:((a-1)*(((O=(z=Ce.face[D].rotation)==null?void 0:z.gaze)==null?void 0:O.strength)||0)+(((X=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:X.strength)||0))/a},Ce.face[D]={...e.face[D],rotation:ee,box:Q,boxRaw:V}}Ce.face[D]={...e.face[D],box:Q,boxRaw:V}}if(!Ce.object||e.object.length!==Ce.object.length)Ce.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Q=e.object[D].box.map((ee,J)=>((a-1)*Ce.object[D].box[J]+ee)/a),V=e.object[D].boxRaw.map((ee,J)=>((a-1)*Ce.object[D].boxRaw[J]+ee)/a);Ce.object[D]={...e.object[D],box:Q,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ce.persons||D.length!==Ce.persons.length)Ce.persons=JSON.parse(JSON.stringify(D));else for(let Q=0;Q<D.length;Q++)Ce.persons[Q].box=D[Q].box.map((V,ee)=>((a-1)*Ce.persons[Q].box[ee]+V)/a)}e.gesture&&(Ce.gesture=e.gesture);let s=oe();return sb=he.perfadd?sb+Math.round(s-r):Math.round(s-r),e.performance&&(Ce.performance={...e.performance,interpolate:sb}),Ce}var lb={};ks(lb,{distance:()=>sc,match:()=>ob,similarity:()=>ib});function sc(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var GC=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function ib(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=sc(e,t,r);return GC(n,r.order||2,r.min||0,r.max||1)}function ob(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){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 n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=sc(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let 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d3(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await D3(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?Gt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?T3(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?R3(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(z=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&z.includes("handdetect")?d=this.config.hand.enabled?await T3(o.tensor,c):[]:(j=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await R3(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?B3(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?i3(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await B3(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await i3(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...BC(l),...LC(u),...VC(d),...WC(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((J=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return jC(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};ep=new WeakMap,ic=new WeakMap,oc=new WeakMap,y1=new WeakMap;return jE(fxe);})();
/**
* @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 2022 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 2022 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 2022 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 2022 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.
* =============================================================================
*/
/**
* Human main module
* @default Human Library
* @summary <https://github.com/vladmandic/human>
* @author <https://github.com/vladmandic>
* @copyright <https://github.com/vladmandic>
* @license 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 See the LICENSE file. */
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